Last week, I had the opportunity to attend the Lunar Interoperability Forum at the White House. It was attended by over 100 space engineers, advocates, and policy professionals and the topic was an internet on the moon. You read that right. NASA, in partnership with several other organizations and companies, already has plans for an internet and position, navigation, and timing services on the moon, cleverly called LunaNet. The forum brought a global audience to discuss technical standards for LunaNet and for an interplanetary internet service that would cover missions to Mars. The forum was enlightening and showed how far along the research and development for this service really is. The focus of the forum was built around the need to create technical standards for LunaNet in the same way that we created things like TCP/IP for the terrestrial internet. There are dedicated scientists and visionaries working on this problem right now and I wanted to share some insights I gained from this event. 

1. Internet in Space is Coming…Soon: With the return to piloted space flight and plans to return to the moon and go to Mars, people are starting to think about permanent habitations on celestial bodies. If that happens, there is a need for communications between habitations, relays from the far side of the moon to Earth, and for inhabitants to be able to transit the lunar surface. The answer to that is an internet and PNT service on the moon that is separate in structure and design from what we use on Earth.  

2. The Speed of Light is Too Slow: On Earth, we can transmit data over wires, airwaves, or fiber and see negligible lag times. Over the distances we are discussing for a lunar or interplanetary internet, the speed of light will still cause delays and disruptions in our internet traffic. There is a concept called Delay/Disruption Tolerant Networks (DTN) that is the dominant technical framework for how to construct internet in space. Our ability to deal with lag times of minutes or hours will be critical to the success of internet in space. 

3. AI Vacuum: Through the entire day, there were few, if any, mentions of the role AI would play in a future space internet architecture. The focus is on traditional radio technology with advances such as DTN built in. The space internet standardization process is not currently looking at how AI could help manage challenges of the internet in space. 

4. Governance: The focus of the forum was on technical standards, so I did not expect a robust policy discussion, but the need for creative governance is clear. Building an entirely new internet presents opportunities for governance based on the lessons learned with the terrestrial internet. There is a need for a parallel effort to study the governance impacts and to create recommendations that can accompany the development of the technical standards.  

Overall, it was an incredible experience that shed considerable light on how far the concept of internet and PNT in space has progressed. Some speakers even talked about the standards and the first implementations being within 5 years. Keep an eye on the development of space internet on the moon and beyond as a parallel effort to the continuance of human space exploration.  

At the same time, we need more people to get involved in this conversation because there are opportunities to create an internet that is enabled by the most innovative technology, like AI, and for us to avoid some governance pitfalls by studying the concept early. Habitation on the moon is not far-fetched and this kind of infrastructure will be what ensures the continued success of sustained presence missions. 

We should also not lose sight of the benefits for terrestrial technology that will come because of this kind of funding and engineering challenges. Building an internet in the harshest environments possible will result in innovations that will apply to people on Earth, the same way they did during the Apollo program, such as thermal blankets and even the Dustbuster. Building the first infrastructure in space, for space, is an incredible engineering challenge that will benefit we Earthlings even if we never make the trip ourselves. 

To stay up to date with Frontier Foundry’s work building AI solutions for regulated industries, visit our website, or follow us on LinkedIn, X, and Bluesky.

You may be experiencing a similar problem that Florentino Ariza had. Florentino was in love with Fermina Daza, but Fermina’s father did not approve. It was not until much later when Fermina’s husband, Dr. Urbino, who was approved by her father, died and Fermina and Florentino were finally able to take their voyage down the Magdalena River. Perhaps you are not dealing with decades of heartbreak (or maybe you are, I do not know your life) but you are dealing with a question about how to use large language models (LLMs) in your organizations. Not particularly dissimilar from Florentino’s predicament, your boss or your organization has forbidden the potential presented by LLMs for any number of reasons. Maybe you are in love with the potential that LLMs bring but are not allowed to take your own voyage down the Magdalena River toward new value. Do not worry. You need not wait decades like Florentino did. You can begin your life journey anytime with custom LLMs that are developed for your specific use case.  

LLMs have become synonymous with the big names such as ChatGPT or Bard (or Gemini or whatever Google’s marketing team rebranded it as). They are also well understood to hallucinate and provide potentially inaccurate responses to queries. This is true, and any output of LLMs should be checked by a human. LLMs are not, and are unlikely to become, “human out of the loop” systems. The value of LLMs is an interface through which humans interact with computers and other forms of artificial intelligence (AI). Expecting perfect results from an LLM after a single query is not reality—just ask the New York lawyers who were sanctioned for using ChatGPT for a court filing only to have it hallucinate precedent cases. LLMs are excellent at augmenting humans, not replacing them.  

With that out of the way, what about using LLMs in an organization that has sensitive data? Perhaps you deal with regulated financial data or law enforcement sensitive information or medical information. Are you prepared to send your sensitive data to an open cloud and use an LLM you do not have control over? Depending on your data, you could not even if you wanted to. Fermina’s father foils us again. 

However, the use of LLMs does not have to be synonymous with the big names and it is possible to securely develop your own LLMs in house that do not connect to the cloud and can operate securely on an air gapped laptop. You can train them on specific sets of data rather than the entire internet and reduce the chances of hallucinations. The data can stay internal and not have to go to a cloud, nor even be connected to the internet if you desire. So, under what circumstances should you consider developing a custom LLM? 

1. Define your business problem and how an LLM can help create value. It may be in time saved, accuracy, reduction in force, or in customer service. In any case, make sure the problem you are trying to solve can be solved by an LLM. 

1. Inventory your data and your data policies. Do you have data to take special precautions with? Does your data require audits to your leadership or third parties? If so, a cloud based LLM is out of the question for you, but an in-house custom LLM is a great path to pursue. 

1. Evaluate your workforce. Does your workforce understand the capabilities and limitations of LLMs? Ensure they understand your vision of LLMs as an augmentation to human expertise, not a replacement for it. 

1. How quickly do you need to move? Hiring and creating a custom LLM development shop in house is expensive and likely to take a long time. Consider whether you have the resources and desire to create this capability or if it makes sense to partner with an AI firm that specializes in model creation, technology governance, data automation, and other important components of a successful AI program. 

1. Security, Compliance, Regulation. Not all data can be transmitted to a cloud that is not under your control via a third party. If you have specific security, regulatory, and/or compliance concerns, LLMs should not be a pipedream for you. Regulatory environments change so the tools you invest in need to be ready for new developments. The cloud is not a necessity anymore and you can still use the best tools available. 

It is painful going through life with something missing. It is even worse when that missing piece translates into lost value. Cloud based LLMs have already changed how humans interact with AI and computers in general, but the future of LLMs is customization. There will always be large LLMs trained on the entire internet, and they will continue to entertain us with sonnets about the joys of eating In-N-Out Burger and frustrate professors as students try to shortcut assignments. But the real value is in targeted use cases where focus can be applied to specific problems, trained on specific data sets and unplugged from the cloud. 

This is your Fermina and your ticket to a dreamy boat trip down the Magdalena toward new value. 

To stay up to date with Frontier Foundry’s work building AI solutions for regulated industries, visit our website, or follow us on LinkedIn, X, and Bluesky.

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

I promise this isn’t going to be one of those “data is the new oil” posts, but the oil analogy is useful. According to a recent Financial Times article, China is building battery production facilities at a rate that is currently double the estimated demand for gigawatt hours globally. The article cites Chinese government subsidies as a primary enabler of the volume of battery plant construction. A CRU Group study states that China’s battery production capacity is on pace to deliver 1,500 gigawatt hours (GWh) in 2023, which is exceedingly more than the forecast global demand level of 636 GWh. If China alone is producing enough battery production capacity to feed the global demand twice over, what are they really trying to accomplish? Sure, government subsidy money comes easy in China, especially for technology projects, but this is a different level. Looking at the strategic context of Chinese technology policy and other global actions, they are building reserve capacity and with it a major lever in great power competition. 

Reserve capacity is a term usually associated with the oil market. In order to control oil prices, oil producing countries must be able to control the supply of oil relative to the demand. This means oil producing countries can either increase the output of oil when demand is high to lower prices or decrease oil output when demand is high to raise prices. The world has seen both strategies. The ability to control output in concert with demand is more complex than it may seem. Right now, countries like Saudi Arabia have entire oil refinery plants that are fully operational that sit idle until they are ordered to start refining. Those idle plants cost money and staff to maintain, but the ability to control prices is worth more than putting money into a plant that is not producing. It’s not just about economics, it’s about geopolitical power. If you own the ability to increase or decrease the flow of oil and impact the price of oil globally nearly by decree, you have immense power and other countries might be more willing to cater to what you want in other areas. Having reserve capacity in the energy sector is a global power. 

So, let’s play this out. As far as domestic need, FT reports that China’s production is set to be more than twice what would be required to replace their entire vehicle fleet with electric cars by 2030, no small number. Sure, the global demand for electric cars, and the batteries that run them, is growing, but not at a pace that would justify the surge in investment and rapid increase of battery production currently happening in China. FT goes on to report that there is an estimated 500GWh production gap in Europe that could be filled by Chinese battery production. There is definite economic opportunity in that that gap, and China is almost certainly keen to capitalize on it. However, we can take a lesson from Russia here as we saw what it was able to do with European natural gas dependence. For decades, Russia was able to get Europe to look the other way as it engaged in bad and dangerous behavior until it took the step into Ukrainian territory and out of Europe’s willingness to turn the other cheek. Europe responded, with substantial prodding from the US, by changing its posture toward Russian energy. There was pain involved in that, but Europe was willing to endure and move toward sustainable and renewable energy sources at a faster pace. 

To give credit where credit is due, you have to admire the elegance of China’s move to build reserve capacity in battery production. On one hand, they identified economic opportunity in a significant global battery production gap and set themselves up well in advance to answer that need. Chinese companies, and the CCP, stand to make billions on battery sales in the coming decades. On the other hand, China watched what happened to Russia in Ukraine (in more ways than one) and positioned itself strategically to control the market into which Europe is moving in a post-Ukraine invasion world. Now, China has the ability to turn up or turn down the supply of batteries globally and control their price in the same way that OPEC does with oil. This is what the FT article misses. While the economic opportunities to control the market, and to expand are certainly important, those opportunities only represent half of the overall strategic plan. Building reserve capacity in energy production has never been a bad bet and it is an issue the West should be more concerned about. 

It’s hard to predict the future, but an assumption that China will still want to reclaim Taiwan is a pretty safe bet. I often hear people talk about how soon China will actually attempt a forceful reunification and hear estimates ranging from “this year” to no more than 2 years. I disagree with these timelines because China thinks longer and more strategically than that. Reserve capacity in battery production makes that point because it is giving the Chinese a second global lever to pull should they make the decision to retake Taiwan.  

In this scenario, let’s say that China’s reserve capacity is at least as high as the FT article claims. China would likely give the global economy something like two years to ensure it is dependent on Chinese capacity and to ensure that the changes in battery production levels will impact the global prices as intended. The demand for an overall market saturation of electric vehicles will be higher after two years making the expected global economic impact higher than it is today. If China were to undertake a forceful reunification of Taiwan, it would be able to threaten the global economy in two important ways. 

1. Semiconductors: TSMC is a strategically important piece of semiconductor production capacity that China desperately wants to control. If China invaded or otherwise claimed ownership of Taiwan, TSMC would be held as ransom. If any country interferes with Chinese plans, China could shutter TSMC or worse. That kind of move would have immediate impacts on the global economy and would be a deterrent to intervention.  

1. Batteries: In the same way, China could threaten to decrease or shut off its production and export of batteries as a second deterrent. Would Europe be willing to go through another energy realignment resulting from a conflict thousands of miles away? China’s desire to fill Europe’s battery production gaps suggests it would not. If China can use technology to drive wedges in global alliances and sanctions participants, it has done its job. 

The fact that China identified a global market gap in battery production and subsequently the CCP is subsidizing a technology production project comes as a surprise to no one. What is noteworthy about the FT reporting is that China is building a strategically important reserve energy production capacity that it will almost certainly use as an instrument of state power to pursue its long-term goals. It has a strategy to address a market, create dependence, and control production and prices while taking lessons from Russia’s adventurism. That’s the problem, but what’s the solution? 

The solution is not easy because the West and China are not playing the same game. CCP is willing to take economic losses to subsidize an economic project that it will use as an instrument of state power years in the future. The West is not structured this way so racing China in a capacity building exercise is futile. The bigger worry is that the West did not see this coming. The priorities for the US and its allies were elsewhere while China was building up it production infrastructure, similar to when China was busy building 5G. The US will need to increase its battery production capacity along with its ability to domestically manufacture chips, but the bigger issue is one of expertise and priorities. The US and the West need to see the world through an frontier technology lens the same way that China has for decades. It needs people who are experts in frontier technology the same way that it has experts in cybersecurity. We need to spend time and resources educating and growing these professionals so that they can see strategic value like this in coming technologies. Those people need to be given the resources to do their job and that means prioritizing frontier technology topics.  

One of the advantages of an autocracy is that you can just do things. You want to spend billions on reserve battery production capacity, no problem. No Congress, no election, just do it. The US and the West have chosen not to live in that society and should be proud of that. Our approach to strategic challenges like this will be different. We will build some of our own capacity, but we are unlikely to unilaterally build at this scale absent a clear economic demand and favorable profit margins. That means we have to think more creatively and more strategically and that requires a clear recognition of the strategic value of frontier technologies before they are in ubiquitous use. Chinese control of potentially two technology choke-points could be decisive and we need to work to counter it in a way that is consistent with our values and builds alliances and partnerships globally.  

One suggestion would be to use the international technology standards making process to control China’s ability to dominate the market. It is possible to use standards making strategically in order to force China into a position where it must comply or not compete. The same could be done for international standards for batteries to ensure that Chinese batteries perform to a certain level, have a certain life span, and that supply chains are controlled in certain ways. This could be a powerful lever and building an alliance around a shared priority could be our best card to play. 

To stay up to date with Frontier Foundry’s work building AI solutions for regulated industries, visit our website, or follow us on LinkedIn, X, and Bluesky.

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

Oyez, oyez, oyez! The AI Governance Council is now in session! All those concerned with the matters before the Council be seated.  

Esteemed members of the Council, distinguished innovators, and those gathered in these hallowed halls of technology governance, we are convened this day under the solemn duty and the weighty charge bestowed upon us by the governance policies of our noble organization. 

Let it be known that the Council, in its unwavering pursuit of technological governance, trust, and order, has before it today a case of grave import. We are to deliberate upon matters most serious, where the scales of governance shall be weighted with due care and diligence. 

To the members of the Council, yours is a solemn challenge: you are the arbiters of innovation, the keepers of automation. Your duty is to weigh the evidence presented with neither prejudice nor partiality. Let not station nor stature of tech companies nor the whispers of the social media gallery sway your judgement, for in your hands lies the fate of the AI system who stands before us presumed mission enhancing until proven otherwise. 

Let all who are present here today remember: we are bound by the solemn duty to uphold public trust in AI and enhance the mission capabilities of our organization, and to dispense governance with an even hand.  

With these words, I declare this Council open. Let us proceed with the gravity and the respect that this solemn occasion demands. The innovators may now present their case. 

Is this what you think of when you think of AI governance? A hallowed hall of stern looking, powdered wig wearing, judges who are issuing a harsh ruling to an AI being? It’s what I think of, and I actually worked on AI governance for the US government back before it was en vogue. I’m sorry to report that I own not a single black robe and only three powdered wigs…just kidding, I don’t own any powdered wigs either. But AI governance is a very popular topic that has been rattling around policy making and technology circles for at least 4 years and broader technology governance for much longer. This is a difficult problem because of the ever-bedeviling reality with which policy makers must contend: two things can be true at the same time.  

With the release of the Biden Artificial Intelligence Executive Order, tech circles are abuzz with talk of incoming regulation on AI. That may indeed be in our future, but that’s not the same as AI governance. AI governance is about how an organization oversees its use of AI products that are not regulated. Governance is a matter of policy, not of law or of rulemaking, and is not going to be uniform across organizations. AI governance is extremely specific to the organization implementing it and the industry or sector it is in. Creating an effective AI governance structure that prioritizes innovation and presumes innocence is critical for any organization that is attempting to use AI broadly. Fundamentally it comes down to a question of “can” or “should.” 

In the US government, there is a law that will make many reading this roll their eyes. It’s the Federal Information Security Modernization Act  or FISMA. FISMA originally required federal agencies to take a “risk-based, cost effective” approach to cybersecurity and required agencies to develop policies to ensure security in information systems. Today, that means that if a federal agency wants to use a particular piece of software or hardware, it must comply with a set of standards. This is governance that asks the question, “CAN we use this system?” Said another way, if the system in question complies with a set of security requirements, FISMA says you are allowed to use it. What FISMA does not ask is SHOULD you use it. In the world of information technology products, the CAN question is often good enough. Few people ask whether we SHOULD be using phones or computers to conduct business. However, AI is entirely different than standard IT technology that is governed by FISMA and requires a different approach. 

When I was a policy maker in the federal government, I recall proposals to slot AI systems under FISMA as the governance structure. But AI governance is not ONLY about whether an organization CAN use AI under its security policies, but also whether it SHOULD use AI for the application being proposed. Further, AI governance should address under what governance conditions an AI system can be used based on both the risk of using the system and of not using the system. For example, an AI governance council might impose a requirement of quarterly audits of the system’s output or enhanced security measures around its training data. Critically, governance of AI must be based on risk, but not ONLY the risk of using AI, but also the risk of not. 

Many of the current conversations around the use of AI, particularly in the federal government, are about the risk of using AI. Risks such as reputation, public trust, security, and more. What’s missing from these conversations is what the risk to reputation, public trust, security, and other issue might be if an organization decides NOT to innovate, NOT to automate, and incurs a significant incident because of human error that could have been prevented by a more detail oriented and less error prone AI. Would you rather have human eyes monitoring volumes of network traffic for anomalies or an AI? 

AI governance is not synonymous with regulation, but it is also not synonymous with the types of technology governance to which we’ve all grown accustomed. It requires an entirely different approach that asks a risk-based question of “can” or “should” from both the perspective of using AI and of NOT using AI. This is a nuanced approach that does not fit well into existing structures, which makes its adoption a challenge. But just like most things in life, a single structure to govern every situation is rarely effective because more than one thing can be true at the same time. It can be true that an organization must put a governance structure in place to ensure its AI is not misused while it is also true that if it did not use that AI system, it would see a degradation to its mission relative to its competitors. AI governance is something new and it will not be the same for every organization because what AI means to different entities will vary widely. Each organization will have to find its own way, but there are frameworks that can help create a structure that mitigates misuse and allows innovation to enhance mission capabilities. 

A panel of powdered wigs may not be how you choose to do AI governance (or maybe it is), but governance should be dispensed with an even hand. Ours is a solemn charge for we are truly the arbiters of innovation and the keepers of automation. AI advancements shall continue to charge forward and the meek shall not inherit competitive advantage. Those who can effectively govern will be able to quickly and effectively adopt while those still struggling to figure out if they CAN use AI will watch their bottom lines dwindle.  

To stay up to date with Frontier Foundry’s work building AI solutions for regulated industries, visit our website, or follow us on LinkedIn, X, and Bluesky.

© 2024 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

The AI industry has invested over $100 billion in GPU infrastructure based on assumptions about computational requirements that may be fundamentally flawed.

This analysis examines structural inefficiencies in transformer architectures and GPU utilization patterns, drawing on academic research and open-source projects to show that memory bandwidth, not compute, is the primary bottleneck in large language model (LLM) inference. Projects such as llama.cpp (93,000+ stars), vLLM (50,000+ stars), ExLlamaV2, MLX, Petals, and AirLLM demonstrate that layer-wise inference, IO-aware attention, distributed computing, and CPU offloading are reshaping AI deployment economics. Gao et al. documented average GPU utilization of 50% or less across 400 real deep learning jobs on Microsoft’s internal platform, confirming that memory constraints dominate production workloads.

This pattern is not new. Every generation of computer science has faced the choice between smart engineering and brute-force spending. The implications extend beyond cost to questions of access, sustainability, and the fundamental architecture of AI systems.

The Hardware Lie We Tell Ourselves

So here is something that should bother everyone who has been writing checks for GPU infrastructure. A collection of GitHub repositories with over 150,000 developers may have just exposed one of the most uncomfortable truths in AI. We have collectively spent over $100 billion building GPU infrastructure that operates at a fraction of its theoretical capacity. The projects are diverse (llama.cpp, vLLM, ExLlamaV2, MLX, Petals, AirLLM) but they share a premise that sounds almost absurd: you do not need the hardware you have been told you need.

Look at AirLLM — 70-billion parameter model on a 4GB GPU. Or llama.cpp running inference on consumer CPUs that were supposed to need data center hardware. Or Petals distributing model layers across volunteer computers over the internet, BitTorrent-style. The fact that any of this works at all should make everyone question the conventional wisdom about what large language models actually require.

Bloomberg reports that global AI infrastructure spending exceeded $150 billion in 2024, mostly flowing to GPUs and data centers. NVIDIA alone captured over $47 billion in data center revenue last fiscal year, gaining near-monopolistic control over the hardware powering modern AI. If even a fraction of that investment is based on false assumptions about hardware requirements, we are looking at tens of billions in misallocated capital. This might be the largest example of collective inefficiency in technology history.

And it is not just about money. The hardware paradigm has created barriers that concentrate AI capabilities among a few well-funded players. A researcher at a small university cannot afford GPT-4-scale experiments. A startup in an emerging market cannot compete with hyperscalers for GPU allocation. If these barriers are artificial, only existing because we optimized for the wrong constraints, then we have inadvertently built an oligopoly on computational intelligence. How many times do we need to learn this lesson?

The Oldest Problem in Computing

Before we get into GPU inefficiency specifically, here is a history lesson that keeps repeating and we keep forgetting: the tension between compute and memory has defined this field since vacuum tubes.

In 1946, the ENIAC could perform 5,000 additions per second but had only 20 words of internal memory. Engineers spent more time managing data movement than designing algorithms. The first stored-program computers of the late 1940s used mercury delay lines that could store a few thousand bits, and those bits had to be carefully orchestrated to keep the processor fed. The Williams tube, the first random-access memory, held perhaps 2,048 bits. Every generation of computer scientists has faced the same fundamental problem of processors that can compute faster than memory can deliver data.

Wulf and McKee formalized this observation in their seminal 1994 paper identifying the “memory wall,” which highlighted the growing disparity between processor speed and memory bandwidth [9]. They predicted that this gap would become the dominant constraint on system performance. They were right. In 1980, DRAM latency was roughly comparable to processor cycle time. By 2020, processor speeds had improved by roughly 10,000x while DRAM latency had improved by only 10x. The gap continues to widen.

Throughout this history, computer scientists have faced a choice. They can either engineer around the constraint or spend their way past it. The smart path involves careful algorithm design, cache-aware programming, memory hierarchy optimization, and data structure engineering. The brute-force path involves buying more hardware. Both approaches have their place, but history shows that the smart path often delivers order-of-magnitude improvements that no amount of spending can match.

Take the transition from bubble sort (O(n²)) to quicksort (O(n log n)). No amount of hardware improvement would have made bubble sort competitive, making the algorithmic improvement necessary. Or consider the development of B-trees for database indexing, which transformed disk access patterns from linear scans to logarithmic searches. These reconceptualizations of the problem made previously intractable workloads practical.

The AI industry is facing the same choice, and it has overwhelmingly chosen brute force. When capital is abundant and competitive pressure is intense, throwing hardware at the problem is the fastest path to capability. But this approach creates technical debt. It establishes patterns that become expensive to unwind, and it may not even be sustainable. Silicon has physical limits, energy costs are real, and capital is not infinite. The spending path has a ceiling. We are hitting it.

The Memory Wall We Pretend Does Not Exist

There exists an inconvenient truth that the AI industry has papered over with hardware purchases.

Modern GPUs spend most of their time waiting for data, not computing.

The roofline model, introduced by Williams, Waterman, and Patterson at Lawrence Berkeley National Laboratory in 2008 [3], provides a framework for understanding this relationship. Every computation exists somewhere on a graph bounded by two constraints: peak computational throughput (FLOPS) and memory bandwidth (bytes per second). The “ridge point” where these lines intersect determines whether a workload is compute-bound or memory-bound.

For an NVIDIA H100 GPU, the current gold standard for AI training and inference, the ridge point occurs at an arithmetic intensity of roughly 300 operations per byte. This means that to keep the H100’s computational units fully utilized, a workload must perform 300 floating-point operations for every byte transferred from memory. Below this threshold, the GPU is memory-bound, with its computational cores idly waiting for data. The H100 can deliver nearly two petaflops of FP16 tensor computation, but this capacity is meaningless if data cannot arrive fast enough to keep it busy.

Most operations in large language model inference fall well below this threshold. For each new token, the model must compute attention scores against all previous tokens and multiply activations through billions of parameters. But the arithmetic intensity of these operations is low because the data — weights, activations, key-value caches — must be loaded fresh for each forward pass. The weights alone for a 70-billion parameter model at FP16 precision occupy 140 GB. For a single-token generation step with batch size one, the entire 140 GB must be moved to generate a few thousand multiply-accumulate operations per weight. The arithmetic intensity works out to roughly one operation per byte, which is 300 times lower than what the GPU needs to achieve peak utilization.

Gao et al. documented this in their 2024 ICSE paper examining 400 real deep learning jobs on Microsoft’s internal platform [2]. Average GPU utilization was 50% or less for production AI at one of the world’s most sophisticated tech companies. The paper won a distinguished paper award, not because the findings were controversial, but because nobody had bothered to systematically measure what everyone already suspected.

They identified 706 distinct low-utilization issues — basically a taxonomy of inefficiency that reads like an indictment of our entire approach to AI infrastructure. Data loading bottlenecks, suboptimal batch sizes, framework overhead, I/O contention. But underneath all of it existed the same problem. Nobody designed the software with memory hierarchy as a first-class constraint.

The Open-Source Efficiency Revolution

While the industry was scaling up hardware, a parallel movement was scaling down requirements. Open-source projects built by individual contributors and small teams have been proving the emperor has no clothes. Our own company has been running multi-billion parameter models on off-the-shelf laptops for a while now. Here is what is out there.

A. llama.cpp: 93,000 Stars and Counting

In March 2023, Georgi Gerganov released llama.cpp, a pure C/C++ implementation of LLaMA inference with no dependencies [10]. The premise was to run large language models efficiently on consumer hardware, including CPUs without any GPU acceleration. Within two years, it had accumulated over 93,000 GitHub stars, attracted 1,418 contributors, and become the foundation for dozens of downstream applications including Ollama, LM Studio, and GPT4All.

The technical work is solid. llama.cpp introduced aggressive quantization (1.5-bit to 8-bit integer representations), the GGUF file format for efficient model storage and loading, and highly optimized kernels for CPU inference using AVX, AVX2, AVX512, and ARM NEON instructions. On Apple Silicon, it runs fast through Metal framework integration. NVIDIA’s own engineers have contributed CUDA Graph optimizations that achieve approximately 150 tokens per second on an RTX 4090 for Llama 3 8B.

It shows developers that memory hierarchy should be treated as a first-class design constraint. By carefully managing data layout, quantization, and cache utilization, llama.cpp shows that much of the “required” GPU hardware was compensating for inefficient software. A Llama 3 8B model that supposedly needs a data center GPU runs fine on a MacBook. Software always beats hardware. We are just watching it play out on a new stage.

B. vLLM and PagedAttention: Virtual Memory for AI

Developed at UC Berkeley’s Sky Computing Lab, vLLM introduced PagedAttention, an attention algorithm that applies the classical virtual memory and paging techniques from operating systems to KV cache management [4]. The project has accumulated over 50,000 GitHub stars and is now deployed in production at numerous organizations including major cloud providers.

The problem PagedAttention solves is KV cache fragmentation. During autoregressive generation, the key-value cache for each request grows dynamically and unpredictably. Traditional systems pre-allocate contiguous memory blocks, wasting 60%-80% of GPU memory to fragmentation and over-reservation. PagedAttention partitions the KV cache into fixed-size blocks that can be stored non-contiguously, managed via a block table analogous to a page table in an operating system.

The numbers back its efficiency: 2-4x throughput improvement over FasterTransformer and Orca with the same latency. For parallel sampling and beam search, memory sharing reduces overhead by up to 55%, translating to 2.2x throughput improvement. The system supports continuous batching that dynamically replaces completed sequences with new ones, maximizing GPU utilization. These gains come purely from better memory management. The same hardware, the same model, just smarter software.

C. ExLlamaV2: Mixed-Precision at the Layer Level

ExLlamaV2 takes a different approach to efficiency, using mixed-precision quantization that varies within a model [13]. The EXL2 format supports two, three, four, five, six, and eight-bit quantization, with the ability to mix precision levels not just between layers but within each linear layer. More important weights (those that contribute more to output accuracy) get more bits; less important weights get fewer.

The quantization process uses GPTQ-style optimization but with finer granularity. Parameter selection is automatic, based on measuring quantization error against calibration data for each possible setting. The result is models that achieve a target average bitrate (say, 4.0 bits per weight) while preserving accuracy better than uniform quantization. Benchmarks show ExLlamaV2 achieving 56+ tokens per second on a T4 GPU — faster than GPTQ, faster than llama.cpp’s GGUF format, with comparable or better accuracy.

The project now supports paged attention via Flash Attention 2.5.7+, includes a dynamic generator with smart prompt caching and K/V cache deduplication, and supports speculative decoding for additional speedups. ExLlamaV3 extends this with the EXL3 format, a streamlined variant of QTIP from Cornell that can convert a model in a single step with a fused Viterbi kernel.

D. MLX: Apple’s Unified Memory Advantage

Apple’s MLX framework exploits a hardware advantage that the NVIDIA-centric AI industry has largely ignored in unified memory architecture [12]. On Apple Silicon, the CPU, GPU, and Neural Engine share the same physical memory pool, eliminating the PCIe transfers that bottleneck discrete GPU systems. The framework has rapidly accumulated over 21,000 GitHub stars.

This matters for throughput. An M4 Max with 128 GB of unified memory is comparable to high-end data center GPUs, providing 546 GB/s of bandwidth accessible to any processor. Recent benchmarks from vllm-mlx show 21%-87% higher throughput than llama.cpp across models from 0.6B to 30B parameters on Apple Silicon. For multimodal workloads, content-based prefix caching achieves up to 28x speedup on repeated image queries and 24.7x on video analysis.

With the M5 chip’s Neural Accelerators, inference speeds improve another 4x for time-to-first-tokens on compute-bound operations. MLX supports on-the-fly quantization that can convert a 7B Mistral model to 4-bit in seconds. A researcher with a high-end MacBook can now run experiments that previously required cloud GPU allocation, and can do so with better energy efficiency than a data center setup.

E. Petals: BitTorrent for Language Models

Petals goes furthest, distributing the model across the internet [8]. Developed through the BigScience collaboration by researchers at University of Washington, Hugging Face, and ENS Paris-Saclay, Petals allows users to run Llama 2 70B or even Llama 3.1 405B by connecting to a swarm of volunteer GPUs that each host a subset of model layers.

The architecture works by orchestrating each server to load several transformer blocks while a distributed hash table coordinates which servers hold which layers. When a client sends a request, it is routed through a chain of servers chosen to minimize total forward pass time. The system handles server disconnections gracefully through fault tolerance and load balancing, automatically re-routing when participants join or leave the swarm.

Performance is usable. Llama 2 70B achieves six tokens per second while Falcon 180B reaches four tokens per second — fast enough for interactive chatbots. More importantly, Petals is 3-25x faster than CPU offloading for single-batch inference in realistic network conditions. The project proves that the constraint is data movement, not compute. Even internet latency beats the bandwidth constraints of moving 140 GB of weights through a PCIe bus repeatedly.

F. AirLLM: The 4GB Proof of Concept

AirLLM shows the extreme case: 70B parameter inference on 4 GB of GPU memory, 405B on 8 GB [11]. The approach is layer-wise loading — process one transformer layer at a time, loading weights from storage as needed, using HuggingFace Accelerate’s meta device feature to defer actual memory allocation.

The insight is that during inference, simultaneous access to all layers is not necessary. Sequential access is, as a forward pass proceeds from the first layer to the last. At any moment, only one layer is actively computing, while the other 79 layers in a 70B model sit idle in memory. AirLLM trades that idle memory for active loading, using safetensor format for memory-mapped loading that maximizes speed.

With prefetching (overlapping the loading of layer N+1 with the computation of layer N), block-wise quantization during transfer (2-4x bandwidth reduction), and NVMe SSD bandwidth of 7 GB/s, the latency penalty is manageable. Version 2.0 added compression support that provides up to 3x speed improvement with minimal accuracy loss. The project now supports CPU inference, macOS, and models including Llama 3.1 405B, all while running on hardware that NVIDIA would recommend 640 GB of GPU memory for.

The Attention Paradox

Nowhere is the inefficiency more pronounced than in the transformer’s self-attention mechanism, the very innovation that made modern language models possible.

Self-attention computes pairwise interactions between all tokens in a sequence, allowing it to capture long-range dependencies that eluded earlier architectures like recurrent neural networks. But this capability comes with quadratic complexity: double the sequence length, quadruple the computation and memory.

For a 4,096-token context (modest by current standards, as models like Claude and GPT-4 support 100,000 tokens or more), the attention matrix contains over 16 million entries per attention head. A typical large language model has 32-128 attention heads across dozens of layers. The memory required to store these matrices, combined with the key-value caches that enable efficient autoregressive generation, quickly dominates the total memory footprint of inference.

Tri Dao’s FlashAttention paper, presented at NeurIPS 2022, revealed that standard attention implementations were catastrophically inefficient [1]. The problem was not the algorithm’s computational requirements; it was the memory access patterns. Standard implementations materialize the full N×N attention matrix in GPU high-bandwidth memory (HBM), requiring multiple round trips between the GPU’s compute units and its relatively slow main memory.

The GPU memory hierarchy makes this worse. The A100 GPU is impressive by any measure, having 80 GB of HBM with bandwidth of 2 TB/s. But it also has 192 KB of on-chip SRAM per streaming multiprocessor with bandwidth estimated around 19 TB/s. That is nearly a 10x difference. Standard attention implementations ignore this hierarchy entirely, treating all memory as equally expensive to access. FlashAttention restructures the computation to work in tiles that fit in fast on-chip SRAM, achieving up to 7.6x speedup on GPT-2 while using linear rather than quadratic memory.

This is Computer Architecture 101: cache-aware algorithms, tiled matrix multiplication, loop blocking. These are standard techniques in scientific computing that date back decades, yet it took until 2022 for someone to systematically apply them to the most compute-intensive operation in modern AI. FlashAttention-2 pushed utilization from 25%-40% to 50%-73% [7]. Still not optimal, but a major jump from paying attention to memory access patterns.

The Economics of Waste

Cloud providers charge $2-$4 per hour for an A100 GPU. A typical 70B model inference setup needs four to eight of them running in parallel. That is $8-$32 per hour before networking, storage, and overhead.

If layer-wise inference, quantization, and memory-efficient attention can deliver equivalent functionality on consumer hardware, we are not talking about incremental savings, but two orders of magnitude. An RTX 4090 costs about $1,600, which is 50-200 hours of A100 rental. For research, batch processing, dev/test, education — anywhere latency is not critical — the economics become a no-brainer.

Beyond cost savings, this changes who can participate in AI development. The current paradigm has created a world where meaningful AI capabilities require either massive capital investment or dependence on a handful of cloud providers. OpenAI spent an estimated $100 million training GPT-4. Academic researchers at smaller institutions, startups without venture backing, and developers in emerging markets are effectively locked out of working with state-of-the-art models.

Layer-wise inference and similar techniques could democratize access to large models in a way that reduced training costs alone cannot. Training a foundation model is a one-time cost; inference is ongoing. If running these models requires a fraction of the hardware currently assumed necessary, the barriers to entry drop dramatically. A university research lab could explore model behavior without cloud bills that exceed their equipment budgets. A startup could prototype with the same models that power enterprise applications.

The environmental angle is similar. Data center energy consumption for AI workloads is growing exponentially. If half the power consumed by GPUs is being wasted due to memory bottlenecks as the Microsoft study suggests [2], then the entire AI industry is burning megawatts of electricity on operations that never actually occur. Multiply this by the thousands of GPUs in a major AI data center, and the environmental cost of inefficiency adds up fast.

Why Did We Build It This Way?

If efficient inference was possible all along, why did everyone standardize on approaches that waste most of their compute?

Historical accident, misaligned incentives, and cultural blind spots. The usual suspects.

The historical accident is straightforward. The transformer architecture emerged from Google Brain in 2017, where computational resources were effectively unlimited. The original “Attention Is All You Need” paper explicitly optimized for parallelism and throughput at scale, not for memory efficiency. This made sense for Google’s training infrastructure — they had thousands of TPUs and GPUs at their disposal, and getting models trained faster was more valuable than reducing hardware requirements. But these design patterns were blindly replicated as the models were deployed more broadly, even in contexts where the original assumptions no longer held.

The misaligned incentives go deeper. NVIDIA has every reason to sell bigger, more expensive GPUs. If customers can run workloads on smaller hardware, revenue suffers. Yes, they have invested in CUDA and cuDNN optimizations, but those have focused on improving throughput on high-end hardware, not enabling deployment on consumer devices. Data center revenue grew 409% year-over-year last quarter. That number depends on customers believing they need expensive hardware. Connect the dots.

Cloud providers have the same problem. AWS, Google Cloud, and Azure make money when you use more resources, not fewer. They will give you convenience and integration, but they have zero motivation to help you minimize compute. Their entire business model is consumption.

The cultural blind spot matters more than people admit. Academic incentives reward novel architectures and state-of-the-art performance on benchmarks, not engineering optimizations that make existing systems cheaper to run. A paper showing 3% accuracy improvement on a language modeling benchmark is more publishable than one showing 3x cost reduction for equivalent performance. The prestigious venues (NeurIPS, ICML, ICLR) have traditionally favored algorithmic novelty over systems engineering.

There is also a knowledge gap. The deep learning community drew heavily from math and statistics backgrounds, where memory hierarchies and cache behavior are foreign concepts. Computer architecture, systems programming, performance engineering — different fields, different cultures, different publication venues. The people who understood memory optimization were not the same people building ML frameworks. It is like asking quantum physicists to optimize database queries.

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The Road Ahead

These open-source projects are not production-ready solutions for all use cases. The latency trade-offs are real. Generating tokens takes longer when loading layers from disk or routing through internet servers. The throughput for high-volume applications will not match dedicated inference hardware running at full occupancy. Interactive applications requiring immediate responses may not tolerate the additional latency.

But these projects represent something more important than individual optimizations. They are proof that the constraints we have accepted are artificial. Once you demonstrate that a 70B model can run on 4 GB of memory, or that Llama 2 70B can be served by a swarm of volunteers over the internet, the conversation changes: “How do we make it practical for different use cases?”

The broader research community is beginning to respond. Work on KV-cache compression shows promising results for reducing memory footprint during long-context generation. MiniCache demonstrated that cross-layer redundancy in attention states can be exploited for substantial memory savings. HeadInfer showed that head-wise offloading can extend context lengths significantly with minimal accuracy impact.

Mixture-of-experts architectures with dynamic routing can reduce active parameters per token while maintaining model capability. Attention-free language models like RWKV and Mamba offer linear-time alternatives to quadratic attention, with memory requirements that scale much more gracefully with sequence length.

These techniques all share a common thread. They treat memory as a precious resource to be optimized rather than an unlimited input to be maximized. This shift in perspective may ultimately matter more than any individual technique. When memory efficiency becomes a first-class design constraint, the entire space of possible architectures opens up.

Conclusion

AI infrastructure is hitting a wall. The path of adding more GPUs to solve scaling challenges is running into physical, economic, and environmental limits. NVIDIA’s data center revenue growth is sustained by capital investment that assumes current architectures are optimal. If layer-wise inference, quantization, and memory-efficient attention become mainstream, the demand curve shifts hard.

This pattern repeats throughout computing history. When resources are abundant, we solve problems by spending. When resources get constrained, or when someone actually measures utilization, we discover that smart engineering beats money. The engineers who optimized for mercury delay lines knew this. The database engineers who built B-trees knew this. The web developers who invented CDNs knew this.

AI is now mature enough that efficiency cannot be an afterthought. The easy scaling gains are done. Environmental costs are impossible to ignore. The concentration of capabilities among a few players is raising real concerns about competition and access. A reckoning is coming.

Seventy billion parameters on a 4 GB GPU. Llama 2 70B served by volunteers over the internet. Over 93,000 developers building efficient inference on consumer hardware. These sound like magic tricks. They are not. They are just careful engineered solutions to a problem the industry — intoxicated by scaling laws and flush with venture capital — never bothered to solve. The magic trick was convincing everyone it was not possible. The real work is rebuilding infrastructure around what is actually necessary.

Afterword: A Personal Perspective on AI Infrastructure

I have spent years working at the intersection of technology and regulated industries, helping banks modernize infrastructure and advising government agencies on tech adoption. You develop a sensitivity to the gap between what vendors promise and what organizations actually need. The AI infrastructure market has an enormous gap.

When a major bank asks if they need an A100 cluster for their compliance AI assistant, the honest answer is usually “no.” They need reliable inference, reasonable latency, and strong security. None of that requires the hardware that hyperscaler marketing suggests. But the industry’s default recommendation is always maximum hardware investment. Funny how that works.

This pattern is not new. Every technology cycle produces its own “you need more hardware” narrative. What is different now is scale and speed. Organizations are making billion-dollar infrastructure decisions based on assumptions nobody has rigorously tested.

For practitioners: benchmark your actual workloads before accepting vendor recommendations. Test layer-wise inference, quantization, CPU offloading on your specific use cases. Figure out whether your bottleneck is compute or something more mundane like memory bandwidth. The results usually tell a different story than the sales pitch.

The $100 billion has already been spent. It is time to build what’s next.

- Sultan

This article was written by Sultan Meghji, CEO of Frontier Foundry and former Chief Innovation Officer at the FDIC. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

References

[1] T. Dao, D. Y. Fu, S. Ermon, A. Rudra, and C. Ré, “FlashAttention: Fast and memory-efficient exact attention with IO-awareness,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), Stanford Univ., Stanford, CA, USA and Univ. at Buffalo, Buffalo, NY, USA, 2022.

[2] Y. Gao, Y. He, X. Li et al., “An empirical study on low GPU utilization of deep learning jobs,” in Proc. IEEE/ACM 46th Int. Conf. Softw. Eng. (ICSE), 2024, Distinguished Paper Award.

[3] S. Williams, A. Waterman, and D. Patterson, “Roofline: An insightful visual performance model for multicore architectures,” Commun. ACM, vol. 52, no. 4, pp. 65–76, 2008.

[4] W. Kwon, Z. Li, S. Zhuang et al., “Efficient memory management for large language model serving with PagedAttention,” in Proc. ACM SIGOPS 29th Symp. Operating Syst. Principles (SOSP), UC Berkeley, Berkeley, CA, USA, 2023.

[5] X. Jiang, Y. Zhou et al., “NEO: Saving GPU memory crisis with CPU offloading for online LLM inference,” in Proc. Mach. Learn. Syst. (MLSys), UC Berkeley, UC Davis, and Harvard, 2025.

[6] Y. Sheng, L. Zheng, B. Yuan et al., “FlexGen: High-throughput generative inference of large language models with a single GPU,” in Proc. Int. Conf. Mach. Learn. (ICML), Stanford, UC Berkeley, and ETH Zurich, 2023.

[7] T. Dao, “FlashAttention-2: Faster attention with better parallelism and work partitioning,” arXiv:2307.08691, Princeton Univ., Princeton, NJ, USA, 2023.

[8] A. Borzunov et al., “Petals: Collaborative inference and fine-tuning of large models,” in Proc. Assoc. Comput. Linguistics (ACL), Univ. of Washington, Hugging Face, and ENS Paris-Saclay, 2023.

[9] W. A. Wulf and S. A. McKee, “Hitting the memory wall: Implications of the obvious,” ACM SIGARCH Comput. Archit. News, vol. 23, no. 1, pp. 20–24, 1994.

[10] G. Gerganov et al., “llama.cpp: LLM inference in C/C++,” GitHub, 2023–2026, 93,000+ stars. [Online]. Available: https://github.com/ggml-org/llama.cpp

[11] G. Li, “AirLLM: Scaling large language models on low-end commodity computers,” GitHub, 2023. [Online]. Available: https://github.com/lyogavin/airllm

[12] A. Hannun et al., “MLX: Efficient and flexible machine learning on Apple silicon,” Apple Mach. Learn. Res., 2023, 21,000+ stars.

[13] ExLlamaV2 Contributors, “ExLlamaV2: A fast inference library for running LLMs locally,” GitHub, 2023–2026. [Online]. Available: https://github.com/turboderp-org/exllamav2

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

AI Giants takes a look at five key players in artificial intelligence from both a technical and financial perspective, exploring their successes, shortcomings, and future roles in the AI ecosystem.

Written by Max Kozhevnikov, Data and Software Engineer at Frontier Foundry, this series documents some of the AI industry’s biggest developments and stories from the past year, and offers unique perspectives on how these companies can get a leg up on their competition.

Explore all 5 parts linked below and let us know your thoughts on these Giants’ strategies to dominate the AI market.

Who do you think will be the AI race’s biggest winner?

To stay up to date with Frontier Foundry’s work building AI solutions for regulated industries, visit our website, or follow us on LinkedIn, X, and Bluesky.

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© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

I’ve spent a significant portion of my career at the intersection of regulated industries and frontier technology. As the first Chief Innovation Officer at the FDIC, I watched financial institutions make the same mistake repeatedly: they would adopt new technology, get comfortable with the contractual language wrapped around it, and convince themselves that comfort was the same thing as security. It never was. The 2008 financial crisis had a technology component that most people still don’t fully understand. Firms had outsourced critical functions to third-party providers and discovered, under conditions of stress, that their contractual protections were worth exactly what they could recover in litigation, which, by then, was beside the point.

I’m watching the same mistake happen right now in the legal industry, at scale, with AI.

The legal AI market has exploded. Harvey has reached an $11 billion valuation. Thomson Reuters paid $650 million for Casetext. LexisNexis has partnered with Harvey, integrated OpenAI, Anthropic, Mistral, Google, and Microsoft into its Protégé platform, and is marketing the result as a “fully encrypted legal AI environment.” Every one of these companies has a security pages, data privacy agreements, and will tell you with great confidence that your client data is safe. What none of them will tell you is that there is a fundamental architectural gap between what their contracts promise and what their technology can guarantee. And in the legal industry, where even the existence of a representation can be privileged information, that gap could spell organizational doom.

The Architecture Problem Nobody’s Discussing

Every major legal AI platform is built the same way at its core. They take foundation models (GPT-4o, Claude, Gemini, Mistral) and build an application layer on top. That application layer is where most of their engineering effort goes: the legal fine-tuning, the retrieval augmented generation, the workflow orchestration, the user interface. This is genuine value-add work, and the best platforms do it well.

But here’s what that architecture means in practice: when a lawyer uploads a client document and asks Harvey or CoCounsel or Protégé to analyze it, meaningful fragments of that document travel to a foundation model for inference. And that inference happens inside infrastructure that these platforms do not own, do not operate, and cannot technically control.

Harvey routes data through OpenAI’s infrastructure on Azure, Anthropic’s infrastructure on AWS Bedrock, and Google’s infrastructure on Vertex AI. CoCounsel runs on both Google Cloud and Amazon AWS, routing to different model providers depending on the task.

LexisNexis’s Protégé accesses five separate model providers — OpenAI, Anthropic, Mistral, Google, and Microsoft — using what it calls “Best Fit” auto-routing, meaning your client’s document could touch any of them, in any combination, based on what the system determines is optimal at that moment.

Each of these platforms will tell you that their agreements with these providers include strict data handling commitments: no retention, no training, no human review. I believe them. These are real contractual commitments that enterprise API channels provide, as distinct from consumer-facing products.

But a contract is not an architectural control. A contract tells you what recourse you have after something goes wrong. Signing a contract does not make data leaks a technical impossibility.

What does make data leaks technically impossible at the inference layer is confidential computing. This is a class of hardware and software technology (Intel TDX, AMD SEV-SNP, Azure Confidential Compute) where computation happens inside a hardware-isolated enclave that is cryptographically sealed. Even the cloud provider operating the underlying infrastructure cannot inspect what’s being processed inside that enclave. The model runs, the inference happens, the output comes out, and at no point does any human or system outside the enclave have technical access to what went in.

None of the major legal AI platforms have publicly committed to deploying confidential computing for model inference. Not Harvey. Not CoCounsel. Not LexisNexis.

With the most secure technologies available, they continue promising complete security while relying on architectures that cannot deliver it.

Harvey: The Best Technical Story with the Same Fundamental Gap

Harvey is the most technically sophisticated of the three companies I want to discuss, and I want to give credit where it’s due before I explain why their security architecture still has a critical weakness.

Harvey built something genuinely smart: a proprietary model proxy layer that sits between their application and every external model API. Every request to OpenAI, Anthropic, or Google routes back through Harvey’s own Kubernetes cluster before going out, meaning external model providers never receive a raw, direct connection from the end user. There’s always a Harvey-controlled intermediary handling the request, rotating API keys, and managing what gets logged at the Harvey layer.

Their workspace isolation architecture is also real engineering. Every law firm exists in a logically separated workspace with no cross-contamination possible. Harvey’s penetration testing, conducted by firms like NCC Group and BishopFox, specifically targets workspace isolation to validate that the separation holds, and tests for the scenario where one firm’s data could appear in another firm’s outputs.

The inference-only architecture addresses a real threat, as the biggest documented risk in AI data exposure lies in training, not inference. When a model is trained on data and then that data gets memorized and reproduced in outputs to other users, that’s a genuine threat. Harvey’s commitment to never using client data for training, enforced contractually with every model provider, eliminates this specific risk in a meaningful way.

On top of that, their customer-managed encryption keys are technically enforced, meaning Harvey is completely incapable of decrypting client data at rest without the firm’s keys.

So why am I still worried?

Because once Harvey’s proxy forwards a request to OpenAI’s Azure infrastructure, or Anthropic’s AWS Bedrock environment, or Google’s Vertex AI cluster, Harvey controls nothing technical about what happens inside those systems. The proxy has done its job, and the request has been handed off. What happens next is governed entirely by Harvey’s contractual arrangements with those providers, not by any cryptographic or hardware mechanism that Harvey controls.

And those providers are operating shared infrastructure serving thousands of enterprise clients simultaneously. Of course, they have security teams, policies, audit processes; I’m not saying someone at OpenAI or Anthropic is doing something nefarious with Harvey’s clients’ data. But the technical controls preventing that from being possible, at the model provider layer, are not Harvey’s to deploy or verify.

Harvey’s security page says, “Harvey contractually guarantees through our Security Addendum that your data stays yours.” That sentence is carefully written. The promise is contractual. The word “guarantee” implies something stronger than what the architecture actually provides at the model provider boundary.

For most law firms handling most matters, this risk level is probably acceptable. But for firms handling matters where privilege is paramount? Acceptable isn’t good enough.

Thomson Reuters CoCounsel: The Legacy Brand with a MultiCloud Problem

Thomson Reuters commands enormous institutional trust in the legal industry. After serving law firms for over a century, Westlaw is embedded in the workflow of virtually every American attorney. When they say something is secure, lawyers believe them. That trust is largely deserved. Thomson Reuters has real security infrastructure and a track record of handling sensitive legal information at scale.

But trust earned in one era does not automatically transfer to a new architectural paradigm, and CoCounsel’s architecture creates a specific problem that Thomson Reuters has not publicly reckoned with: they are routing client data through two separate cloud providers’ infrastructure, to multiple model providers, in ways that create more surface area than their legacy security reputation was built to cover.

CoCounsel’s core product runs on Google Cloud, while its Anthropic/Claude integration, which is used specifically for tax services, runs on Amazon AWS Bedrock. This means client data, depending on the task, is processed within Google’s infrastructure or Amazon’s infrastructure. Thomson Reuters has no control over either of these environments. They have enterprise agreements with both, which include strong data handling commitments, but Google and Amazon are each operating vast, complex infrastructure serving millions of enterprise clients. The controls preventing any given client’s legal matter from being accessible to anyone inside those organizations are, at the boundary, contractual.

CoCounsel has earned ISO 42001 certification, becoming one of the first generative AI systems in professional services to do so. Its features include zero-retention architecture for client data and SOC 2 Type II audits, showing real investments in security governance.

What they don’t have, as far as publicly available information reveals, is a confidential computing architecture that would make it technically impossible for anyone at Google or Amazon (or anyone who successfully breached Google or Amazon’s infrastructure) to access the legal matter data being processed for inference. The ISO certification covers Thomson Reuters’ own practices and nothing more. They have no control over what happens inside Google’s or Amazon’s data centers once the API call lands.

There is also a less-discussed issue specific to CoCounsel’s situation. The acquisition of Casetext brought with it the CoCounsel product and team, but the architectural decisions made at a startup don’t automatically upgrade to enterprise-grade infrastructure just because a large company acquires them. CoCounsel was built quickly, in a competitive market, with the goal of demonstrating capability. Some of those architectural shortcuts may still be present underneath the enterprise packaging.

I’m don’t believe Thomson Reuters is cavalier about security, but when their narrative leans heavily on their institutional credibility and contractual arrangements and provides few details on the specific architectural question of what happens at the model provider boundary, suspicions arise.

For the many firms that have relied on the Thomson Reuters brand as a proxy for security, this distinction is worth understanding before it becomes relevant in ways that are difficult to remediate.

LexisNexis Protégé: Five Providers, One Surface Area Problem

LexisNexis presents the most complex security picture of the three, and not in a good way.

The Protégé platform has expanded aggressively over the past year, integrating model providers at a pace that appears to be driven primarily by competitive pressure rather than security architecture. As of the most recent announcements, Protégé’s “Best Fit” mode can route queries to OpenAI (GPT-4o, GPT-5, o3), Anthropic (Claude Sonnet), Google, Mistral, and Microsoft. That’s five separate external model providers, each operating their own infrastructure, each with their own data handling practices, each with their own contractual arrangements with LexisNexis.

When LexisNexis describes Protégé as operating within a “fully encrypted Lexis+ AI environment,” that description is accurate for what it covers: the LexisNexis application layer. Data is encrypted at rest within LexisNexis’s systems and in transit.

But “fully encrypted Lexis+ AI environment” describes where data lives, not where it goes. And data goes to five different sets of third-party compute infrastructure for the actual AI processing. At each boundary, we again see a security guarantee transition from architectural to contractual. Multiply that by five, and you have five times the contractual surface area and five times the number of third-party environments where the technical controls are outside LexisNexis’s direct authority.

LexisNexis also introduced something called “identifiable information removal” from AI interactions, meaning they strip personally identifiable information before sending data to model providers. This is a meaningful effort and directionally correct, however legal matters don’t expose their sensitivity through obvious identifiers. Sensitivity is often in the substance of the document, the nature of the legal question, the specific combination of facts. Stripping a name and an address does not anonymize a merger agreement or a document subpoena. The legal community understands this intuitively, and LexisNexis’s PII removal, while better than nothing, does not address the deeper confidentiality concern.

There’s also a governance issue worth naming. LexisNexis’s parent company RELX has been an investor in Harvey since the Series D. LexisNexis subsequently formed a strategic partnership with Harvey, integrating its content into Harvey’s platform. This means LexisNexis is simultaneously operating its own competing legal AI product and providing critical content infrastructure to its primary competitor, while also being a financial stakeholder in that competitor. The incentive structures here are complicated, and firms relying on LexisNexis to be single-mindedly focused on the security of their client data should be aware that LexisNexis’s priorities in this space are multidimensional.

Again, I am not suggesting bad faith, but when a company’s security posture serves multiple conflicting business interests, independent verification becomes more important, not less.

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The Confidential Computing Gap: Why This Matters More Than You Think

I want to be precise about what confidential computing actually solves. The threat model I’m describing is complex, with several components and subtleties that are specifically addressed by preventing data access with properly configured hardware.

The first is the insider threat. Major cloud providers have thousands of employees with varying levels of access to infrastructure. Enterprise API agreements include “no human review” commitments, but those commitments describe policy, not technical impossibility. A determined insider, or someone who has successfully compromised an insider’s credentials, can access infrastructure in ways that violate policy. Without hardware-level isolation, there is no technical barrier to this specific threat.

The second is the sophisticated external breach. The major cloud providers are among the most hardened infrastructure targets in the world, and they do get breached. The Microsoft Exchange hack by HAFNIUM, the Capital One breach through AWS misconfiguration, the SolarWinds compromise that affected cloud environments across the US government and private sector. These are not hypothetical threats. When they happen, it doesn’t matter what the data privacy agreement stated if your data was technically accessible. With confidential computing, the hardware enclave is cryptographically sealed even against the provider’s own infrastructure access. Without it, what gets leaked depends on how the breach occurred and what the attackers were able to access.

The third is the legal discovery risk. When a cloud provider receives a government subpoena or national security letter for data processed within their infrastructure, their data handling commitments to their enterprise clients may not protect them from compliance. The NSA’s PRISM program, revealed by Snowden in 2013, demonstrated that major technology providers had been compelled to provide access to data processed within their systems, at scale, under legal orders that prohibited disclosure to the affected clients. The legal frameworks have evolved since then, but the fundamental issue has not been resolved: data processed within a third party’s infrastructure is potentially subject to that third party’s legal obligations in ways that may override their contractual commitments to you.

Confidential computing addresses all three of these threat vectors by making the data technically inaccessible at the hardware level, even to the cloud provider’s own infrastructure. An insider at Amazon cannot read what’s being processed inside an AMD SEV-SNP enclave, because the hardware prevents it. An attacker who breaches Azure’s control plane cannot decrypt data being processed inside an Intel TDX enclave, because the keys are not available to the control plane. A subpoena to Google for data processed inside a hardware-isolated enclave will not produce useful results, because Google itself cannot access that data.

This is not futuristic technology. Confidential computing is in production today, with systems like Azure Confidential Compute, AWS’s Nitro Enclaves, and Google Cloud’s Confidential VMs being generally available. The question is whether legal AI companies have chosen to build their architectures on it.

The honest answer, based on everything publicly available, is that they haven’t. And the reason they haven’t is that confidential computing adds latency, adds complexity, and adds cost. In a market where every vendor is racing to add features and lower friction, the incentive to invest heavily in an architectural improvement that most customers don’t know to ask for is limited.

The Attorney-Client Privilege Problem Nobody Has Solved

Before I get to what the industry should be asking, I want to name a specific legal risk that is hiding inside the technical architecture discussion.

Attorney-client privilege is not just an ethical obligation, but a legal doctrine that can be waived, and once waived, it generally cannot be recovered. One of the established ways to waive privilege is to voluntarily disclose privileged communications to a third party who does not share the legal interest that created said privilege. Courts have spent decades developing doctrine around when disclosure to a third party does or does not constitute waiver.

The question of whether routing client communications through a third-party AI model provider constitutes a disclosure that could be used to challenge privilege has not been definitively resolved. Most legal technology providers will tell you it doesn’t, and there are reasonable arguments for that position. The analogy to using a telephone or email server, where data passes through third-party infrastructure without waiving privilege, has some force.

But those analogies were developed in an era when the third-party infrastructure was dumb pipe. It received data, transmitted data, and did nothing with it. The legal AI model providers are far more complex, receiving and processing legal communications, generating inference, producing analysis, and creating outputs that reflect the substance of the privileged matter. The legal distinction between a telephone company that carries your call and an AI provider that reads your client’s document is not trivial.

I am not a practicing attorney, and I am not offering a legal opinion. I am observing that this question is live, that courts have not resolved it, and that every law firm adopting AI tools is implicitly taking a position on it. If a sophisticated opposing party in a major litigation decides to challenge the privilege over communications that passed through an AI inference infrastructure, the case they would make has a non-trivial basis, and the firms that would be most exposed are the ones that chose their AI vendor based on the DPA rather than the architecture.

This has not been litigated. But it will be.

This comes back to the confidential computing approach I described earlier, which potentially acts as a better privilege preservation argument. If the data never left an architecture where access by a third-party was a technically impossibility, the argument that privilege was waived by disclosure to a third party becomes significantly harder to make. Hardware-isolated inference serves as both a security and privilege argument.

What the Legal Industry Should Actually Be Asking

The legal industry has a peculiar relationship with technology risk. Law firms are simultaneously some of the most sophisticated buyers of professional services in the world and some of the slowest to update their understanding of what “secure” actually means in a given technological context.

The due diligence process most firms apply to legal AI vendors is borrowed from their process for evaluating other software vendors. SOC 2 Type II? Check. ISO 27001? Check. Data privacy agreement? Check. No training on client data? Check. These necessary questions, but they’re insufficient.

The question that should be at the center of every legal AI security evaluation is this: at what layer does the security guarantee transition from technical enforcement to contractual assurance, and what is the threat model for that boundary?

For Harvey, the answer is: at the boundary between Harvey’s proxy infrastructure and the external model provider’s compute environment. Harvey controls everything before that boundary technically. After that boundary, protection becomes contractual.

For CoCounsel, the answer is: at two separate boundaries — one into Google’s cloud infrastructure, one into Amazon’s — depending on which model is being used for which task. After those boundaries, protection becomes contractual.

For LexisNexis Protégé, the answer is: at five separate boundaries, to five separate model providers, with auto-routing potentially determining which boundary your client’s data crosses on any given query. After every one of those boundaries, protection becomes contractual.

The follow-up question is: given that boundary, what is your threat model, and is a contractual assurance sufficient for the matters your firm handles?

For firms handling M&A transactions where even the identity of the parties is market sensitive information before announcement, the answer should be that contractual assurance is not sufficient. For firms handling government investigations where national security implications may make legal process more likely, the answer should be that contractual assurance is not sufficient. For firms where a single matter represents hundreds of millions of dollars in fees and the reputational consequences of a breach would be existential, the answer should be that contractual assurance is not sufficient.

Legal AI is worth using, but the industry’s security narrative has gotten significantly ahead of its security architecture, and the people bearing the risk of that gap are the law firms and their clients who are trusting these platforms with their most sensitive information.

What Actually Good Looks Like

I want to be constructive here, because I don’t think the answer is for the legal industry to avoid AI. The efficiency gains are real. The competitive pressure to adopt is real. The technology will only get better.

But “what actually good looks like” is specific, and the industry should be able to articulate it.

Good looks like foundation model inference happening inside hardware-isolated confidential computing enclaves, where not even the cloud provider can access what’s being processed. This is technically achievable today.

Good looks like cryptographic proof of data handling — not just contractual commitments, but verifiable logs that can demonstrate what was processed where, when, and by what infrastructure, in a way that can be independently audited.

Good looks like the option for firms handling ultra-sensitive matters to have their data processed on dedicated, isolated infrastructure rather than shared multi-tenant cloud environments, even if that option comes at higher cost.

Good looks like transparency about exactly which model providers are being used for exactly which types of processing. It looks like the ability to limit that routing to specific, approved providers for specific matter types, not auto-routing that optimizes for performance without giving the firm visibility or control.

Good looks like third-party penetration testing that specifically targets the model provider boundary. It goes beyond the application layer and workspace isolation, and identifies what data is technically accessible to the model provider’s infrastructure during inference.

Some of this exists in embryonic form. Some of it doesn’t exist yet at scale in the legal AI market. The firms and vendors who build it will have a genuinely differentiated security story build on architectural guarantees, not contractual ones.

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A Note on Why This Matters for Regulated Industries Generally

I’ve been writing primarily about law firms, because the legal AI market is where this conversation is most visible, but everything I’ve described applies with equal or greater force to the other regulated industries adopting AI: banking, healthcare, defense, intelligence.

At the FDIC, I watched financial institutions struggle with a version of this problem when they first moved to cloud infrastructure. The regulatory framework (OCC guidance, the Bank Service Company Act, vendor management requirements) was built around the assumption that regulators could examine third-party providers. The new AI infrastructure creates a version of this problem that existing regulatory frameworks are poorly equipped to address. The “vendor” has become a chain of vendors, each with their own infrastructure and contractual relationships, and the data flows between them are not always visible to the institution, let alone the regulator.

The banks that handled this well were the ones who refused to accept “we have a contract” as an answer to “what happens to our data.” They required architectural evidence: network diagrams, data flow documentation, technical controls assessments, penetration test results that specifically targeted the boundaries they were worried about. They made vendors explain, in technical terms, what was technically impossible rather than what was contractually prohibited.

The legal industry should adopt the same posture. The technology is different, but the underlying principle is identical: contractual protections and architectural protections are not the same thing, and in the moments when they diverge, only one of them actually protects your clients.

The Bottom Line

Harvey is the most technically sophisticated legal AI platform I’m aware of. Their proxy architecture, workspace isolation, and BYOK encryption are genuine engineering, not marketing. But once your data crosses into OpenAI’s, Anthropic’s, or Google’s infrastructure, Harvey’s technical controls end and their contractual ones begin.

CoCounsel has the benefit of Thomson Reuters’ institutional credibility and a century of trust in the legal industry. But that trust was built in an era before client data traveled to Google Cloud and Amazon AWS for AI inference, and the security architecture has not publicly caught up with that new reality.

LexisNexis Protégé offers the most capable multi-model environment in the market, but that capability comes with the most complex data flow. Five separate model providers, auto-routed, with client data potentially touching any of them in any combination, each with their own contractual, not architectural, protections at the boundary.

None of this means these platforms are reckless or that you shouldn’t use AI for legal work. It means you should be clear-eyed about what you’re buying, and you should be asking vendors the questions they are not yet used to being asked.

Here’s how I’d run the due diligence conversation if I were a law firm’s managing partner or CIO sitting across the table from any of these vendors:

First question: “At what specific point in your data flow does your security guarantee transition from a technical control to a contractual one? Walk me through the exact boundary.” Don’t let them answer with “end-to-end encryption” or “enterprise-grade security.” Make them name the boundary and describe what’s on either side of it.

Second question: “What is your confidential computing roadmap?” If they don’t have one, that tells you something. If they have one, ask when it will be in production and what it will cover. If they claim they already have it, ask for the technical documentation and have someone who understands Intel TDX or AMD SEV-SNP review it.

Third question: “Which specific model providers does my client data touch, and can I restrict that routing?” “Auto-routing to the best model” sounds like a feature. From a data governance perspective, it is a liability. You should know exactly whose infrastructure is processing your matters, and you should have the ability to say certain matters can only go to certain providers.

Fourth question: “What does your penetration testing scope include, and can I see the most recent report?” If the scope doesn’t include the model provider boundary — specifically testing whether data processed at inference time is technically accessible to the provider’s infrastructure — then you haven’t tested the most important thing.

Fifth question: “Has your outside counsel reviewed the privilege implications of your data architecture?” Not the DPA language. The actual architecture. If they haven’t done that analysis, you should require them to before you put privileged matter content into their system.

The vendors who can answer these questions well deserve your business. The vendors who respond with marketing language and point you back to the DPA do not yet deserve your trust with your most sensitive matters, regardless of how good their product is at the legal task level.

While the technologies dominating the market are genuinely impressive, their security narratives have gotten ahead of their security architecture. The people who pay the price for that gap, if and when it closes badly, are not the AI vendors. They are the firms and the clients who trusted them.

“Do you have a DPA?” is a 2018 question.

“Show me your confidential computing architecture” is the 2026 question.

The firms and the vendors who understand the difference between those two are the ones who will get ahead of this problem. Everyone else is hoping the gap never matters.

But in regulated industries, hope is not a risk management strategy.

This article was written by Sultan Meghji, CEO of Frontier Foundry and former Chief Innovation Officer at the FDIC. Visit his LinkedIn here.

To stay up to date with Frontier Foundry’s work building AI solutions for regulated industries, visit our website, or follow us on LinkedIn, X, and Bluesky.

To learn more about the services we offer, please visit our product page.

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

The cost of building intelligent software is collapsing toward zero. But in the two sectors that matter most to the American economy, trillion-dollar incumbents have built moats not out of technology, but out of fear. AI can now write code, interpret regulations, analyze medical records, and reason through complex compliance workflows. What once required a 200-person engineering team and $50 million can be prototyped by a small team in weeks. The marginal cost of software intelligence is approaching zero.

We are entering an era where the barrier to software development is functionally nonexistent.

But walk into any mid-size bank or regional hospital system in America and you’ll find something that defies this reality: enterprise software contracts that cost tens of millions of dollars a year and are locked in for a decade, running on architectures designed before the iPhone existed. And nobody, not the CEO, not the CIO, not the board, is willing to touch them.

This issue lies not within the technology, but within the existing power structure. And understanding it requires understanding what’s really at stake.

Two Sectors, One Quarter of the Economy

Healthcare and financial services are the backbone of the American economy. Together, they account for more than 25% of the U.S. GDP, driving roughly $7 trillion in annual economic activity. Healthcare alone represents nearly 18% of GDP at $4.8 trillion, making it the single largest sector. Financial services, including banking, capital markets, insurance, and asset management, contributes another 8% or so.

Between them, these sectors employ close to 30 million Americans. Healthcare is the nation’s largest employer at 22 million workers. Financial services adds another 7 million, with average compensation roughly double the national average.

These are the sectors that touch every American, every day. And they are both drowning in legacy technology costs that no longer reflect the value being delivered.

The Moat Isn’t Technology. It’s Fear.

Companies like Epic in healthcare and FIS, Fiserv, and Jack Henry in banking haven’t survived because their technology is the best available. They’ve survived because they’ve constructed something far more durable than good software: structural lock-in across multiple reinforcing dimensions.

• The data is held hostage. Epic holds patient records in proprietary formats, and core banking platforms hold decades of transaction histories, customer records, and regulatory reporting infrastructure. Migration is both expensive and risky. A botched core banking conversion can kill a bank, while a botched EHR migration can harm patients. The switching cost exists as an existential threat, not just a budget line item.

• Contracts are designed as quicksand. Multi-year licensing deals with massive early termination penalties and bundled service packages where you can’t extract one module without destabilizing the rest keep customers locked-in for years. Professional services revenue that dwarfs the software license, with Epic implementations routinely costing 3 to 5 times the license itself. These contracts serve as economic weapons designed to make leaving more expensive than staying.

• The regulators have been co-opted. This is the part most people outside these industries don’t understand. These vendors have embedded themselves into the regulatory examination process itself. Examiners know what Epic output looks like. They know FIS report formats. When a bank tells an OCC examiner “we run on FIS,” that’s shorthand for “you don’t need to dig deeper into our systems.” Choosing something unfamiliar introduces regulatory uncertainty, and in regulated industries, uncertainty is the one thing no executive will tolerate.

• The institution has been reprogrammed. Thousands of employees are trained on these systems, with entire workflows being built around their specific quirks and limitations. The CFO who signed the original deal is still on the board. The IT director’s entire career trajectory is staked on the platform choice they made in 2014. Changing systems doesn’t just mean changing software. It means telling a lot of powerful people they were wrong.

The Paradox: Software Is Free, Switching Is Not

Here’s the tension at the heart of this moment. AI makes it trivially cheap to build software that replicates much of what Epic or FIS does in critical functional areas. Compliance automation, document processing, risk scoring, clinical decision support — these capabilities can be constructed with modern AI tools at a fraction of what incumbents charge.

But AI doesn’t solve the switching cost problem. It doesn’t solve the regulatory comfort problem. It doesn’t solve the career-risk problem.

A bank CEO isn’t paying FIS $20 million a year because they believe FIS is worth $20 million. They’re paying because the perceived cost of being wrong about an alternative is $200 million in regulatory penalties, operational disruption, reputational damage, and personal career risk.

When so much is on the line for these decision makers, the software is no longer the product. The insurance policy is.

This is why the “software will be free” thesis, taken at face value, misses something important about these markets. You can build a better mousetrap. You can build it for a tenth of the cost. But if the building is rigged so that replacing the old mousetrap might bring down a load-bearing wall, nobody will touch it.

Where the Wall Cracks

But here’s where it gets interesting. The winning strategy was never “rip out Epic” or “rip out FIS.” That’s a frontal assault on a fortified position, and it will fail for all the structural reasons above.

The winning strategy is building an intelligence layer that sits on top of, or alongside, the legacy stack. The AI doesn’t replace the system of record. It makes the system of record progressively less relevant. This is already happening. Banks are keeping their cores but wrapping AI around them for BSA/AML compliance, fraud detection, and regulatory reporting, while hospitals are keeping Epic but layering AI for clinical decision support, medical coding, and prior authorization. The legacy system quietly becomes a dumb database while the intelligence that actually creates value migrates to the new layer.

The economic tipping point arrives when institutions realize they’re paying $20 million a year for a database. That’s when the renegotiation happens. Not a rip-and-replace revolution, but a power shift. The value migrates from the incumbent’s proprietary platform to the AI layer above it, and suddenly the licensing leverage flips.

There’s a second accelerant: regulatory modernization. As regulators themselves adopt AI-native tools and begin expecting AI-driven compliance outputs, the “examiner familiarity” moat starts to erode. When the OCC or CMS starts evaluating institutions based on the quality of their AI-generated risk analysis rather than the brand name of their core platform, the game changes fundamentally.

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The Trillion-Dollar Question

Healthcare and financial services together represent more than $7 trillion in annual economic activity. A conservative estimate of the “legacy technology tax” (the premium institutions pay above the actual value delivered) is in the hundreds of billions of dollars annually, sustained by lock-in rather than innovation. That’s a liberation movement waiting to happen. The question isn’t whether software will be free. It already functionally is.

As the price of software engineering continues to fall, it only becomes a matter of time before the institutions paying for it realize that what they’re really paying for is a habit.

And habits, unlike contracts, can be broken.

This article was written by Sultan Meghji, CEO of Frontier Foundry and former Chief Innovation Officer at the FDIC. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

For legal organizations of all sizes and specialties, the growing reliance on technology has introduced an undercurrent of challenges that few are willing to confront head-on. While technology promises efficiency and agility, the hidden costs of inadequate privacy solutions are becoming an increasingly pressing concern. There are multiple applications for technologies like artificial intelligence (AI), but privacy and data concerns create barriers to entry. Across the legal industry, there are firms that are subject to hidden, unrealized costs. A decision to move too late, or not at all, on a technology that could bring scaling functions, more billable hours, and more accurate research presents a real opportunity cost. Here, we uncover these overlooked challenges and explore why traditional AI solutions are not sufficient to provide the privacy and security law firms need to overcome these hidden challenges.

Real Stories, Real Stakes

In our conversations with law firms and compliance teams, one theme emerges repeatedly: the fear of data breaches.

The stakes for a data breach of client data are extremely high, but understanding where these breaches could come from is a hidden opportunity. If a firm employs an AI solution that resides in a multiparty cloud architecture, the client data it is using is exposed to unnecessary risk. If, however, the firm employs an AI system on-premise where its data, queries, and outputs never leave its own architecture, that risk is mitigated.

Another common story relates to data jurisdiction issues. In the US, many states are creating their own privacy laws in the absence of federal legislation. Firms may run into issues with where data is stored or transits if they are using a multiparty cloud. The resulting regulatory fallout would not only cost the firm financially but also damage its reputation. This is another risk that is mitigated by keeping data within a firm’s own infrastructure.

Why Existing Solutions Fall Short

Traditional legal technology providers often focus on functionality, speed, ease of use, and integration capabilities. While these are critical, they often come at the expense of robust privacy measures. This is because they are built using the same engineering that is being used to build AI for public consumption. This approach creates mismatches with what the legal industry needs and what it gets on the market. Here’s why:

1. Over-reliance on Cloud Storage: Many platforms store data in centralized cloud environments, making them attractive targets for hackers.

2. Data Cross Contamination: Many platforms use user data to train their algorithms for onward sale to other customers. The sensitivity of legal data precludes this kind of training and must be considered by legal experts before using.

3. One-Size-Fits-All Encryption: Basic encryption methods fail to account for the nuanced privacy requirements of legal professionals handling privileged communications.

4. Regulatory Mismatches: Solutions designed for global markets frequently overlook jurisdiction-specific privacy laws, leaving firms vulnerable to compliance issues.

5. Transparency and Auditability: Lawyers must show their work, especially in courtroom settings. Existing legal AI solutions do not allow attorneys to show how the algorithm got to its conclusion, which could cause long-term problems for work product based on those outputs.

The Hidden Costs of Inaction

On one hand, firm leadership may decide that the best way to mitigate any risk is to not implement AI at all. However, this approach has never been effective with other technologies over time, such as emailing documents or virtual meeting options. In every case, the firms that adopted these technologies quickly and intelligently gained value by moving forward with technologies that showed significant promise. Here’s what’s at stake:

• Scale: Using purpose-built AI will allow legal firms to take on more cases, create more billable hours, and increase the quality of their results at a lower cost.

• Reputation: Having in-house AI that does not risk client data in a multiparty cloud will bring reputational benefits to early adopters. Those who choose not to use AI at all or that use cloud-based applications will be viewed as behind the times, producing lower quality work in more time.

• Workforce: Freeing critical thinking and strategically minded legal experts to focus on client preparation and legal strategy will create more value per unit of work for firms. AI will augment lawyers as a value added.

From Risk to Competitive Edge

The legal sector is a competitive industry, with many different firms working in specialized legal areas. Those firms must continue to bring in high value clients and expand their client base or risk their business. AI for legal experts, when built with privacy and security in mind, creates a higher quality of legal service by pairing humans with customized AI. Firms that can unlock the value potential of freeing a legal expert to critically think with the help of data-driven insights will ultimately produce better work than competitors and drive new clients.

Many firms have large holdings of data in the form of case files, briefs, and other documents used in the course of their work over many years. What value are those data holdings bringing the firm? Many firms are paying significant prices for data storage, so they need to get value for current clients from those data holdings. With privacy preserving AI, firms can unlock case insights based on their existing data holdings to bring to bear in current cases. This creates another dimension to the competitive edge that firms with custom AI will enjoy.

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A New Way Forward

So, how can the legal industry address these challenges? The answer lies in embracing privacy-preserving AI solutions specifically designed for the nuances of legal practice. Here’s a preview of what’s possible:

1. Federated Learning: Instead of storing data in a central location, federated learning enables AI to train on data locally, ensuring sensitive information never leaves its source.

2. Differential Privacy: This cutting-edge approach adds a layer of statistical noise to data, ensuring individual records remain anonymous while preserving analytical insights.

3. Customizable Compliance Frameworks: Solutions that adapt to jurisdiction-specific laws, automating compliance while minimizing manual effort.

These technologies not only address the vulnerabilities of traditional solutions but also provide a competitive edge by safeguarding client trust and enhancing operational efficiency.

The hidden problems plaguing privacy in the legal industry demand more than a band-aid solution. By adopting privacy-preserving AI tailored to the legal market, firms can mitigate risks, protect their reputations, and focus on what they do best: delivering exceptional legal services. At Frontier Foundry, we’re committed to helping the legal industry navigate these challenges with the confidence your specific challenges demand.

Are you ready to uncover a better way forward? Let’s start the conversation!

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

This is Part 5 of our AI Giants series, where we examine the successes and shortcomings of today’s largest AI firms.

Explore our archive to read Part 1, covering Claude’s recent reliability crisis, Part 2, exploring Google’s path to success in the AI industry, Part 3, going over OpenAI’s December “Code Red,” and Part 4, diving into the impact of Perplexity’s legal troubles.

Microsoft has constructed the most comprehensive enterprise AI stack in the industry, offering features that range from custom silicon to advanced productivity software. Despite the breadth of their offerings, Microsoft faces a widening gap between its adoption claims and measurable business impact. The company's $80 billion annual AI infrastructure investment and strategic OpenAI partnership have established dominance in cloud AI services, with Azure revenue growing 40% year-over-year. However, the flagship Microsoft 365 Copilot shows only 1.8% conversion among its 440 million Microsoft 365 subscriber base, despite claims that 90% of Fortune 500 companies have "adopted" it. This tension between ambitious positioning and adoption reality defines Microsoft's AI moment in early 2026.

The stakes are enormous. CEO Satya Nadella has bet the company's future on becoming what analysts call "the architectural foundation of the AI era." Microsoft now operates over 400 data centers across 70 regions, has invested $13.8 billion in OpenAI for a 27% stake valued at $135 billion, and recently added a $5 billion Anthropic partnership to hedge its bets. What remains to be seen is whether this massive AI infrastructure buildout will translate into products that customers actually use and value.

A New Chapter of Partnerships

The Microsoft-OpenAI relationship underwent its most significant transformation in October 2025. Following months of tense negotiations (that included OpenAI executives discussing the "nuclear option" of accusing Microsoft of anticompetitive behavior), the two firms altered their agreement. The restructured deal preserved Azure API exclusivity until an independent expert panel verifies AGI achievement but removed Microsoft's right of first refusal as OpenAI's compute provider. OpenAI committed to purchasing an additional $250 billion in Azure services yet can now deploy non-API products on any cloud – a concession enabling the $500 billion Stargate infrastructure project with SoftBank and Oracle.

Microsoft's total investment now represents a 27% stake in OpenAI's new for-profit structure. Valued at approximately $135 billion, Microsoft has nearly made a 10x return on their original $13.8 billion investment. The partnership's IP terms extend through 2032, including post-AGI models with safety guardrails, while research rights expire in 2030 or upon AGI verification, whichever comes first. Perhaps most significantly, Microsoft gained freedom to pursue AGI independently through its new "MAI Superintelligence Team" led by former Inflection AI CEO Mustafa Suleyman.

Microsoft has hedged its OpenAI dependency through a major Anthropic partnership announced in November 2025. The $5 billion investment brought Claude models into Microsoft 365 Copilot, GitHub Copilot, and Azure AI Foundry. Users in some regions now access Claude as the default model. Combined with Meta's Llama, Mistral, and xAI's Grok in Azure's catalog of 1,900+ models, Microsoft has shifted from OpenAI exclusivity to a multi-model marketplace strategy.

The Enterprise Adoption Gap

The metrics tell two very different stories about Microsoft 365 Copilot. Microsoft claims that 90% of Fortune 500 companies now use the product, and that over 100 million monthly active users engage with Copilot across commercial and consumer products. Customer case studies report impressive results: Vodafone employees saving hours weekly, Lumen Technologies estimating tens of millions in annual savings, and law firm DWF reducing contract drafting time significantly.

Gartner's research reveals a starkly different picture. While 60% of organizations have started Copilot pilot projects, only 1% have completed enterprise-wide deployments. Just 6% have finished pilots and are planning large-scale rollouts. Nearly half of IT leaders lack confidence managing Copilot's security risks, and 72% report employees struggle to integrate Copilot into daily routines. Most concerning: only 3% of organizations surveyed said Copilot provides "significant" value currently.

The pricing strategy has evolved in response. Microsoft introduced a $21/user/month tier for small and medium businesses in December 2025, acknowledging that the $30/user/month enterprise price created friction. "Am I getting $30 of value per user per month out of it? The short answer is no," CIO advisor Tim Crawford told CNBC. Microsoft also bundled vertical Copilots for Sales, Service, and Finance at no extra cost and made Copilot Chat free as an entry point. Price increases effective July 2026 will bundle AI as a baseline feature across Microsoft 365 plans.

The latest capabilities aim to close the value gap. GPT-5 became the default model in late 2025, with subsequent updates adding enhanced code generation and multilingual capabilities. Agent Mode enables iterative document creation across Word, Excel, and PowerPoint. "Work IQ" creates an intelligence layer understanding user context, preferences, and organizational relationships. Microsoft shipped over 400 new features in 2025 and introduced "Agent 365" as a control plane for managing AI agents with enterprise security controls.

Developer Tool Dominance…

GitHub Copilot remains the clear market leader in AI coding assistants with a 42% market share and over 20 million cumulative users as of mid-2025, growing rapidly through the year. The product serves tens of thousands of enterprises, with the vast majority of Fortune 100 companies using it. Satya Nadella noted that Copilot has become a larger business than all of GitHub was when Microsoft acquired the platform for $7.5 billion in 2018.

The competitive landscape has intensified dramatically. Cursor has captured significant market share in under two years from near zero, reportedly reaching substantial annualized recurring revenue with over a million daily users. Cursor's AI-native IDE approach (built on VS Code but offering whole-codebase understanding rather than file-level context) appeals to developers working on complex projects. Cursor 2.0, launched in late 2025, introduced a workflow engine with multi-step planning and multi-agent routines.

GitHub responded at Universe 2025 with "Agent HQ," a unified system for orchestrating AI agents from OpenAI, Anthropic, Google, Cognition, and xAI. Agent mode now autonomously implements changes across multiple files, suggests terminal commands, and self-heals runtime errors. Developers can assign GitHub issues directly to Copilot for autonomous completion, with pull requests generated for human review. Multi-model choice expanded to include Claude 3.5 Sonnet, Claude Opus 4.5, and Gemini 3 Pro alongside OpenAI's models.

Developer sentiment has cooled industry wide. The Stack Overflow 2025 survey found positive sentiment toward AI tools dropped from over 70% to around 60%, with the main frustration being "AI solutions that are almost right, but not quite." Security research has found that a significant percentage of AI-generated code contains potential vulnerabilities, making human review mandatory. GitHub Copilot's competitive pricing ($10/month for Pro versus Cursor's $20/month) remains a significant advantage for individual developers and cost-conscious teams.

… Consumer AI Struggles

Microsoft's consumer AI efforts face persistent challenges against ChatGPT's dominance and Apple's ecosystem coherence. Copilot holds approximately 14% of the AI chatbot market in the US, a distant second to ChatGPT's roughly 60%. While Bing search queries increased following Copilot integration, global search share remains stuck at approximately 4% with Google still commanding nearly 90% of search.

Windows Copilot has received mixed-to-negative user reception. The flagship Windows Recall feature, which was pitched as a "photographic memory" that records screen activity, was delayed and overhauled after researchers discovered it stored data in an unencrypted plaintext database. Microsoft was forced to change it from default-on to opt-in after backlash. A Windows Latest editorial in January 2026 captured user sentiment, as comments express frustration with Microsoft continuing to add AI features to their OS without providing use cases that users want.

The Copilot branding itself has become problematic. Critics have noted that Microsoft's "Copilot" marketing can be misleading given the multiple different products (Microsoft 365 Copilot, GitHub Copilot, Windows Copilot, Security Copilot) each with different capabilities and pricing. Salesforce CEO Marc Benioff famously called it "Clippy 2.0." Internal Microsoft communications reportedly revealed that Nadella acknowledged Copilot integrations "don't really work" in certain scenarios, with research suggesting AI agents struggle to complete real-world office tasks reliably.

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Microsoft’s Positioning in AI Platform War

Microsoft's competitive strategy centers on integration: embedding AI across the entire product suite from infrastructure to applications. Against Google, Microsoft leads in productivity AI adoption for enterprises but struggles to match up financially. Google's stock significantly outperformed Microsoft's in 2025, reflecting market expectations that Gemini and Google Cloud are gaining momentum. Google reversed course to bundle Gemini into Workspace plans rather than charging separately, directly challenging Microsoft's premium Copilot pricing.

Against Amazon, Microsoft trails in overall cloud market share (~20% versus ~30%) but leads in AI-specific workloads and growth rate. AWS Bedrock's "model mall" approach offers breadth, but Microsoft's combination of OpenAI Anthropic Claude access positions them as the only major cloud with both frontier model families. Against Apple, Microsoft struggles in consumer AI coherence while Apple Intelligence's privacy-focused, on-device approach resonates with users.

Microsoft's FY2025 results demonstrated the financial scale of its AI pivot: $281.7 billion in revenue (up 15%) and $128.5 billion in operating income. Wall Street consensus targets imply up to a 30% upside. However, margin pressure is real: Microsoft Cloud gross margin decreased to 69% due to AI infrastructure scaling costs, and the $3.1 billion hit to Q1 FY2026 net income from OpenAI investment losses reduced EPS by $0.41.

Stop Announcing, Start Executing

Microsoft has constructed the infrastructure for AI dominance: $80 billion in data center investment, access to OpenAI and Anthropic's frontier models, the fastest-growing major cloud platform, and AI embedded across a product suite touching nearly half a billion users. The strategic vision of becoming "the architectural foundation of the AI era" has clear execution.

The challenge is bridging the gap between platform capability and customer value realization. With only 1% of organizations achieving enterprise-wide Copilot deployment despite 90% of Fortune 500 companies "using" it, and developer sentiment toward AI tools declining from over 70% to 60% satisfaction, Microsoft must prove that its AI investments provide more than just impressive demos and translate to measurable productivity gains.

What separates Microsoft from the other AI giants in this series is that its fate doesn't hinge on a single product or model breakthrough. The company's sheer scale and enterprise lock-in provide extraordinary resilience. But resilience isn't victory. The next 18 months, as AI moves from experimentation to deployment, will determine whether Microsoft's bet becomes the foundation for a new era of growth, or an expensive lesson in the difference between building platforms and delivering value.

This article was written by Max Kozhevnikov, Data and Software Engineer at Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

The banking industry is experiencing its most significant technological transformation in decades. For community banks, this presents both existential risks and unprecedented opportunities. This article summarizes key frontier technologies in the industry, providing actionable context and resources for bank leadership and boards.

The core message is straightforward: your larger competitors are already deploying these technologies at scale. Community banks that move strategically now will capture competitive advantages. Those that wait will find themselves playing catch-up in an increasingly digital marketplace.

1. Artificial Intelligence in Banking Operations

The Compliance Opportunity

Bank Secrecy Act and Anti-Money Laundering (BSA/AML) compliance represents one of the most significant cost centers for community banks. According to the Conference of State Bank Supervisors, community bankers report that 24.9% of their compliance costs are attributable to anti-money laundering efforts. Industry estimates suggest U.S. financial institutions spent $59 billion on BSA/AML compliance in 2023. The OCC has begun streamlining BSA/AML examination procedures for community banks, with new procedures effective February 2026. However, the fundamental compliance burden remains. AI-powered transaction monitoring, suspicious activity detection, and automated reporting represent the lowest-risk, highest-ROI entry point for AI adoption.

Key Considerations for AI Implementation

• Explainability: Regulators are shifting from “prove you’re compliant” to “prove your AI is explainable.” Any AI system deployed for compliance must be able to demonstrate its decision-making logic.

• Vendor Due Diligence: Most “AI solutions” marketed to community banks are repackaged rules engines with chatbot interfaces. Demand to see model architecture and validation methodologies before procurement.

• Board Literacy: Your board needs AI literacy immediately. Regulators will ask questions, and “we trust our vendor” is not an acceptable answer.

2. The Legacy Technology Gap

Many legacy core banking providers have not kept pace with technological advancement. Community banks often rely on systems designed in the 2010s or earlier, now marketed with superficial updates. Meanwhile, fintech competitors build natively on modern cloud infrastructure with API-first architectures.

The Commoditization of Software

“Vibe coding”—the use of AI coding assistants to rapidly build functional software—is fundamentally changing the build-versus-buy calculus. A competent operator can now build in a weekend what previously required $500,000 and six months of development. This democratization of software development means custom solutions are increasingly viable for institutions that previously could only afford off-the-shelf products.

Strategic Implication: Evaluate whether your current technology partnerships are delivering genuine innovation or simply annual maintenance fees on aging systems. Consider whether targeted custom development might address specific pain points more effectively than waiting for vendor roadmap items.

3. Real-Time Payments: FedNow and the Velocity

Imperative

The Federal Reserve’s FedNow Service has reached significant milestones since its July 2023 launch. As of early 2025, more than 1,400 financial institutions participate in FedNow, with community banks and credit unions making up more than 95% of participants. Transaction volumes grew 1,200% year-over-year through early 2025.

Market Context

The Clearing House’s competing RTP network processed 343 million transactions valued at $246 billion in 2024. While FedNow’s volumes remain smaller, adoption is accelerating rapidly. Use cases gaining traction include instant payroll, digital wallet funding, real estate transactions, and insurance payouts.

Risk Considerations

Real-time payments eliminate the traditional window for fraud detection and returns. With FedNow and RTP, banks have one opportunity to screen for proper authorization and fraud before funds move irrevocably. This velocity creates both fraud risk and opportunity for banks that can move intelligently fast.

Sources: Federal Reserve Financial Services, The Clearing House, American Banker (February 2025)

4. Evolving Cybersecurity Threats

AI-Powered Fraud: Deepfakes and Voice Cloning

Deepfake and voice cloning attacks represent a rapidly escalating threat. In February 2024, a finance worker at engineering firm Arup was tricked into transferring $25 million after a video call with what appeared to be the company’s CFO and other executives— all of whom were AI-generated deepfakes. Deloitte’s Center for Financial Services predicts that generative AI could enable fraud losses to reach $40 billion in the United States by 2027, up from $12.3 billion in 2023. One report found deepfake incidents increased 700% in fintech in 2023 alone. Voice cloning technology now requires as little as three seconds of audio to create a convincing voice clone. A 2024 McAfee study found that 1 in 4 adults have experienced an AI voice scam.

Defensive Measures

• Multi-Person Authorization: Require multiple approvals for any financial transaction over defined thresholds, regardless of who appears to be requesting it.

• Out-of-Band Verification: Establish protocols requiring verification through separate, pre-established communication channels for unusual requests.

• Employee Training: Regular training sessions about deepfake threats, including examples of recent attacks and practice identifying suspicious communications.

Sources: Deloitte Center for Financial Services, The Wall Street Journal, McAfee (2024)

5. Post-Quantum Cryptography: Preparing for the Quantum Threat

Quantum computers, when sufficiently advanced, will be capable of breaking current encryption standards including RSA and elliptic curve cryptography—the foundations of today’s secure communications. While cryptographically relevant quantum computers may be a decade or more away, the “harvest now, decrypt later” threat is immediate: adversaries can collect encrypted data today and decrypt it when quantum capability becomes available.

NIST Post-Quantum Standards

In August 2024, the National Institute of Standards and Technology (NIST) released its first three post-quantum cryptography standards: FIPS 203 (ML-KEM for key encapsulation), FIPS 204 (ML-DSA for digital signatures), and FIPS 205 (SLH-DSA for stateless hash-based signatures). NIST’s transition timeline calls for deprecating quantum-vulnerable algorithms by 2035, with high-risk systems transitioning earlier.

Action Items for Community Banks

Begin asking your technology vendors about their post-quantum cryptography roadmaps. Inventory systems and data that rely on vulnerable cryptographic algorithms. Prioritize transition planning for systems protecting data with long-term sensitivity.

Source: NIST Post-Quantum Cryptography Standardization (August 2024), Department of Homeland Security

6. Cryptocurrency and Digital Assets

The regulatory posture toward cryptocurrency has shifted significantly in 2025. The OCC has issued multiple interpretive letters clarifying that national banks may provide crypto-asset custody services, engage in certain stablecoin activities, and participate in blockchain networks—without requiring prior supervisory non-objection.

Recent Regulatory Developments

• OCC Interpretive Letter 1183 (March 2025): Confirmed that crypto-asset custody, stablecoin activities, and distributed ledger participation are permissible for national banks and federal savings associations.

• OCC Interpretive Letter 1184 (May 2025): Clarified that banks may buy and sell assets held in custody at customer direction and may outsource crypto-asset activities to third parties with appropriate risk management.

• National Trust Bank Charters (December 2025): The OCC granted conditional approval to several crypto-native firms including Ripple, BitGo, Paxos, and Circle to establish national trust banks.

Strategic Consideration

Your customers are already using cryptocurrency whether you offer services or not. The question is whether you will capture that relationship or cede it to Coinbase, PayPal, or fintech competitors.

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7. Embedded Finance and the Invisible Bank

Embedded finance—financial services integrated seamlessly into non-financial platforms—represents a fundamental shift in how consumers access banking services. Every time a customer gets financing through Shopify, pays through Apple Pay, or obtains insurance at checkout, that’s a banking relationship your institution never had a chance to compete for.

Market Scale

The embedded finance market is projected to reach over $450 billion by 2031, with a compound annual growth rate around 24%. A recent study found that 94% of mid-to-large enterprises plan to increase their embedded finance investments, with 76% expecting upgrades within 12 months.

Banking-as-a-Service Opportunity

For community banks, Banking-as-a-Service (BaaS) offers a path to participate in embedded finance. By leveraging regulatory licenses and core infrastructure, banks can become foundational partners for FinTechs and brands seeking to offer financial products. This requires investment in API infrastructure and partnership capabilities but enables access to customer segments that would otherwise be unreachable.

Sources: Mordor Intelligence, McKinsey, Green Dot/PYMNTS (2025)

8. Community Bank Competitive Advantages

Despite the challenges, community banks possess distinct advantages that technology cannot easily replicate:

• Relationship Context: You actually know your customers. Your AI can be trained on real relationship context, not just transaction data. Decades of lending decisions, customer behavior patterns, and local market knowledge represent a data moat that big tech companies cannot replicate.

• Regulatory Trust: Your charter and regulatory relationships provide a foundation that FinTechs spend years and millions trying to establish or work around.

• Talent Flexibility: Remote work has fundamentally changed the talent market. You’re no longer competing with Charlotte or Atlanta for talent if you’re willing to hire the sharp compliance analyst who doesn’t want to relocate. Small banks can punch above their weight here.

9. Implementation Framework

Strategic Delivery Approach

• Pilot Small: Pick one process, one branch, one use case. BSA/AML automation is often the ideal starting point given clear ROI and regulatory alignment.

• Prove Value: Measure everything. Document efficiency gains, false positive reductions, and time savings with specificity.

• Then Scale: Expand based on proven results, not vendor promises.

Vendor Selection Criteria

Partner with vendors who transfer knowledge, not just software licenses. Ask for model architecture documentation, validation methodologies, and regulatory compliance evidence. Demand references from institutions of similar size and complexity. The compliance officer of 2030 won’t be replaced by AI—they’ll be the person who knows how to direct AI. Invest in upskilling your people now, not just your technology.

The banking industry’s ongoing technological transformation has created great opportunities for community banks to close the gap between their national competitors and offer their customers rivaling features and capabilities. Those that fail to adopt these new technologies put themselves at risk of falling behind in an increasingly complex technological landscape.

The window for strategic action is open, but it won’t stay open long.

This article was written by Sultan Meghji, CEO of Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

This is Part 4 of our AI Giants series, where we examine the successes and shortcomings of today’s largest AI firms. Explore our archive to read Part 1, covering Claude’s recent reliability crisis, Part 2, exploring Google’s path to success in the AI industry, and Part 3, going over OpenAI’s December “Code Red.”

Perplexity AI has emerged as the most formidable challenger to Google’s search dominance in a generation. Achieving a $20 billion valuation in September 2025, Perplexity processes nearly 800 million monthly search queries. But this rise comes with an asterisk; the company now faces copyright lawsuits from The New York Times, News Corp, Reddit, and Encyclopedia Britannica that threaten the very foundations of its business model.

The stakes are enormous. Within two years, CEO Aravind Srinivas has built what he calls an “answer engine” that doesn’t just find links but synthesizes information with cited sources. The company’s partnerships with Snapchat, Deutsche Telekom, and Samsung TV could potentially see the platform reach over a billion users. Yet the same aggressive tactics that fueled this growth, including allegations of ignoring robots.txt files and scraping content without permission, now threaten to define Perplexity’s legacy as either a revolutionary platform or, in the words of News Corp CEO Robert Thomson, a leader in “content kleptocracy.”

18 Months of Staggering Growth

Perplexity’s funding trajectory reads like a Silicon Valley fever dream. The company hit a $1 billion valuation in April 2024, reached $9 billion by December 2024, and crossed $20 billion in September 2025, a twentyfold increase in just 18 months. Total funding now exceeds $1.5 billion from elite backers including Accel, SoftBank, IVP, NVIDIA, and Jeff Bezos.

Revenue has scaled just as impressively. Perplexity crossed $100 million in annual recurring revenue in March 2025, more than quadrupling from roughly $20 million in mid-2024. By late 2025, ARR approached $150-200 million, with management targeting $656 million by the end of 2026. The company processes approximately 30 million queries daily, up from 230 million monthly in August 2024 to 780 million by May 2025, representing 240% growth in under a year.

User metrics reinforce the growth story. The platform serves roughly 22 million monthly active users with exceptionally strong engagement, and a 53% DAU/MAU ratio suggests users return frequently rather than testing and abandoning the product.

The business model remains refreshingly simple with subscriptions being their main revenue driver. With millions of paid subscribers, the $20/month Pro tier generates the bulk of Perplexity’s subscription revenue. Enterprise clients including Stripe, Zoom, Databricks, Snowflake, and HP pay $40 per user monthly, while the company’s API now handles thousands of developers across industries. Perhaps most remarkably, Perplexity maintains this growth with only about 250 employees, yielding revenue-per-employee figures among the highest in the AI sector.

Perplexity’s Expanding Ambitions

Perplexity’s technological moat rests on its Sonar model family, built on Meta’s Llama 3.3 70B and optimized for factual accuracy. The company achieved 1,200 tokens per second through Cerebras inference infrastructure, making responses feel nearly instantaneous. The model lineup now includes Sonar Pro (delivering twice as many citations), Sonar Reasoning (for chain-of-thought tasks), and Sonar Deep Research (conducts dozens of autonomous searches and reads hundreds of sources to produce comprehensive reports in two to four minutes).

The Deep Research feature, launched in February 2025, represents Perplexity’s clearest differentiation from competitors. It achieved 21.1% accuracy on “Humanity’s Last Exam,” a benchmark designed to test frontier capabilities. Pro Search allows users to choose between GPT-5.2, Claude Sonnet 4.5, Gemini 3 Pro, Grok 4, and Perplexity’s own Sonar – multi-model flexibility that neither Google nor OpenAI offers.

But the year’s boldest move was launching Comet, an AI-native browser built on Chromium. Initially available only to $200/month Max subscribers in July, Perplexity made it free globally in October and launched an Android version in November. The browser features a persistent AI assistant that can summarize across all open tabs, handle voice commands, and complete tasks autonomously, with a built-in ad blocker that challenges traditional web monetization. An iOS version remains pending.

The Comet Plus subscription ($5/month) represents Perplexity’s attempt to solve the publisher problem proactively. Partners including CNN, Condé Nast, The Washington Post, Fortune, Le Monde, and Le Figaro receive 80% of subscription revenue when their content is cited or visited through the browser. The company has committed $42.5 million to publisher payments in this program.

The Legal Siege from Publishers

Despite the publisher partnerships, Perplexity faces an unprecedented legal assault. On December 5, 2025, The New York Times filed a federal copyright lawsuit alleging the company “copied, distributed, and displayed millions” of Times articles. The complaint claims Perplexity made 175,000+ access attempts to nytimes.com in August 2025 alone, used disguised user agents to evade detection, and circumvented robots.txt restrictions through third-party crawlers.

The Times lawsuit follows similar actions by News Corp (filed October 2024, survived a motion to dismiss in August 2025), Encyclopedia Britannica and Merriam-Webster (September 2025), Reddit (October 2025), and the Chicago Tribune (December 2025). Japanese publishers including Yomiuri Shimbun and Nikkei have filed suits as well, and are seeking ¥2.2 billion ($15 million) each.

The accusations are specific and damaging. A June 2024 Wired investigation found Perplexity’s IP address accessed Condé Nast properties 822 times in three months despite being blocked. Developer Robb Knight demonstrated that Perplexity summarized his content even after he blocked PerplexityBot via both robots.txt and server-side rules. Most damaging, a Cloudflare investigation in August 2025 documented 3-6 million daily requests from an undeclared “stealth crawler” using user agents designed to impersonate regular Chrome browsers.

Perplexity’s defense rests on fair use and the argument that it doesn’t train foundation models on publisher content, only summarizes and cites it. Head of Communications Jesse Dwyer offered a characteristically defiant response: “Publishers have been suing new tech companies for a hundred years, starting with radio, TV, the internet, social media, and now AI. Fortunately, it’s never worked, or we’d all be talking about this by telegraph.”

The legal outcomes will likely define whether Perplexity’s business model is sustainable. The company’s approach stands in stark contrast to OpenAI, which has struck licensing deals worth hundreds of millions with publishers including News Corp (reportedly $250 million+ over five years), AP, Axel Springer, and Vox Media. The $1.5 billion Anthropic settlement in September 2025—the largest copyright recovery in history—may embolden publishers to pursue aggressive litigation strategies.

Intensifying Competition

Regardless of its momentum, Perplexity remains a small player in search. Google controls 89.7% of global search market share, remaining essentially unchanged despite AI disruption narratives. All AI search platforms combined account for just 0.15% of global traffic. Perplexity captures 15.1% of AI referral traffic compared to ChatGPT’s dominant 77.97%.

Google’s response has been substantive. AI Overviews now appear on 55% of global searches and reach 1.5 billion monthly users. The company launched AI Mode at I/O 2025 with conversational capabilities. Studies have shown AI Overviews reduce click-through rates for top-ranked pages, threatening publishers regardless of which AI platform wins.

ChatGPT Search, launched in October 2024, hasn’t demonstrably hurt Perplexity’s growth. A Datos analysis from October 2024 through January 2025 found no significant impact on Perplexity’s user base. However, ChatGPT’s broader capabilities (coding, creative writing, voice features, plugin ecosystem) give it more surface area to capture users who might otherwise try Perplexity.

Perplexity’s differentiation centers on research depth and source transparency. Its inline citations, academic and SEC filing filters, and multi-model selection offer researchers capabilities ChatGPT lacks. A July 2025 Wordstream study found Google AI Overviews had a 26% error rate on PPC topics versus Perplexity’s 13%. Response times favor Perplexity at under two seconds compared to 3.7 seconds for Google AI Overviews.

The advertising strategy remains uncertain. Perplexity launched “sponsored questions” in November 2024 at $30-60 CPM, which is dramatically higher than industry averages. But by October 2025, the company announced it wasn’t accepting new advertisers, and ad sales head Taz Patel departed in August. Only 12% of consumers trust AI search results “a lot more” than organic results, according to Attest, creating fundamental challenges for ad-supported AI search.

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Partnerships Promise a Fruitful 2026

Distribution partnerships represent Perplexity’s most promising growth vector. The $400 million Snapchat deal announced November 5, 2025, will integrate Perplexity into an app with 943 million monthly active users starting early 2026. Snap stock surged over 20% on the announcement.

Telecom partnerships provide additional scale. Deutsche Telekom is launching an AI Phone ($149-999) with Perplexity integrated across ten European markets; Bharti Airtel offers free 12-month Pro subscriptions to its 360 million Indian customers; SK Telecom provides access to 32.5 million South Korean users. Collectively, Perplexity has signed over 25 telecom partnerships potentially reaching 700+ million users.

India has become the company’s largest market by monthly active users. Q2 2025 saw 2.8 million downloads (600% year-over-year growth) and 3.7 million MAUs (640% growth), outpacing ChatGPT’s 350% growth in the country. CEO Srinivas announced a $1 million personal investment and five hours weekly commitment to Indian AI development. However, India’s price sensitivity challenges monetization; ChatGPT generates roughly 100 times more revenue in the market.

Other distribution wins include pre-installation on Motorola Razr phones, integration as a default search option in Firefox, and a Samsung TV app offering free 12-month Pro subscriptions on all 2025 Samsung televisions. The company’s August 2025 bid of $34.5 billion for Google Chrome (more than Perplexity’s own valuation) was rejected but signaled browser ambitions.

Balancing Growth Against Existential Risk

Perplexity occupies a peculiar position: simultaneously one of the most successful AI startups ever built and a company whose core practices face fundamental legal challenges. The next twelve months will likely determine whether it becomes a durable competitor to Google or a cautionary tale about moving fast and breaking things.

The bull case is compelling. Snapchat integration alone could drive massive user acquisition. The Comet browser, if successful, could shift search behavior toward AI-native experiences. Enterprise traction is real, revenue is scaling, and the product genuinely works better than Google for many research tasks. At $150-200 million ARR with 250 employees, unit economics appear strong.

The bear case is equally real. A 38x ARR valuation is historically unsustainable. Multiple lawsuits from major publishers create existential risk. Google and OpenAI have vastly more resources and are actively improving their competing products. The Anthropic settlement precedent suggests publishers can extract enormous damages through litigation. And Perplexity has not yet achieved profitability despite frequent VC infusions.

What makes Perplexity genuinely interesting, and what separates it from the other AI giants in this series, is that its fate hinges on a question that extends far beyond the company itself: Who owns the internet’s knowledge, and what obligations do AI systems have to the humans who created it? The courts, not the market, may ultimately decide whether Perplexity represents the future of search or a cautionary tale about the limits of disruption.

This article was written by Max Kozhevnikov, Data and Software Engineer at Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2026 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

Introduction

In traditional financial markets, trade reconciliation is a critical but complex process. Every security trade requires multiple independent parties—front office, broker, custodian, clearinghouse, and back office teams—to exchange files, confirm details, and ensure that what was intended to be traded matches what was executed and ultimately settled. This workflow involves numerous handoffs, reconciliation points, and exception management steps, all designed to align the “truth” across disparate ledgers.

However, the emergence of tokenized securities and blockchain-based settlement mechanisms introduces the possibility of collapsing this sprawling infrastructure into a single source of truth. Instead of maintaining separate records and comparing them, a smart contract on a blockchain can become the canonical ledger, automatically enforcing settlement rules and embedding economic details of the trade at execution. This removes the need for multi-step reconciliation, reduces settlement risk, and creates operational transparency.

This essay explores how the elimination of reconciliation steps is achieved when security trades are executed via tokenized instruments recorded on-chain, with a smart contract serving as the arbiter of economic truth.

Traditional Reconciliation: A Fragmented Ledger Problem

At the heart of reconciliation lies a structural issue: every party to a transaction maintains their own ledger. The portfolio manager’s order management system records a trade, the broker’s system records an execution, the custodian’s books reflect positions, and clearinghouses record netted settlements. Because these ledgers are siloed, reconciliation is necessary at every interface to ensure agreement on:

• Security identifier (CUSIP, ISIN, token, Ticker, Ref. Data Ticker)

• Trade date and settlement date

• Quantity and price

• Gross and net consideration

• Settlement instructions

• Cash and position movements

Each step introduces the possibility of breaks. Even simple mismatches in formatting (e.g., an ISIN vs ticker) or timing (e.g., late broker confirmation) generate operational overhead. Entire teams are dedicated to comparing files, chasing confirmations, resolving breaks, and reporting aged exceptions. While indispensable in today’s markets, reconciliation is essentially a workaround for fragmented record-keeping.

In short, reconciliation is not inherently value-adding—it is compensatory. The financial industry spends billions annually just to confirm that its fragmented systems are in alignment.

On-Chain Trading: A Shared Ledger

The blockchain model offers a fundamentally different architecture. Instead of each counterparty keeping its own version of the trade, there is a single shared ledger accessible by all permissioned participants. When a trade in a tokenized security occurs, the details are not just written into a firm’s proprietary system—they are encoded into a smart contract and posted to the blockchain.

This transforms the process in three key ways:

1. Single source of truth: The blockchain record becomes the canonical version of the trade, eliminating the need for each side to reconcile local copies.

2. Automated settlement logic: Smart contracts can enforce delivery-versus-payment (DvP), ensuring securities are only transferred once cash is received.

3. Immutable audit trail: Every action is time-stamped and transparent, removing ambiguity about who executed, affirmed, or settled a trade.

The reconciliation problem dissolves because there is no longer “my record” and “your record” to compare. There is only “the record.”

Below, Figure 1 illustrates the contrast between today’s multi-step reconciliation workflow and the streamlined on-chain mechanism.

Figure 1: Traditional reconciliation requires multiple checkpoints (top), while on-chain reconciliation collapses the process into a single smart contract execution (bottom).

The Smart Contract as Reconciliation Engine

To illustrate, imagine the life cycle of a tokenized equity trade. Two counterparties agree to exchange 1,000 tokenized shares of XYZ Corp at $10 per share. A smart contract designed for this security executes the following steps:

1. Order capture: Both parties digitally sign their intent and submit it to the smart contract.

2. Trade validation: The contract verifies both have the required balances—shares on the seller side, cash or stablecoin on the buyer side.

3. Economic embedding: Trade date, settlement date, quantity, price, and counterparty details are recorded directly in the contract.

4. Atomic settlement: On the agreed settlement date, the contract simultaneously debits and credits tokenized cash and securities, achieving DvP without intermediaries.

5. Finality and immutability: Once executed, the ledger reflects the new positions, visible to both parties, custodians, and regulators.

This sequence collapses the dozens of reconciliation checkpoints into a single atomic transaction. There are no broker confirmations to chase, no custodian records to align, no settlement instructions to transmit via SWIFT. The contract itself enforces the economic truth.

Mapping the traditional reconciliation flow against its on-chain replacement highlights the efficiency gain:

• Order creation: Still required, but orders connect directly to the smart contract.

• Execution & confirmation: Execution is recorded instantly on-chain; no separate broker file.

• Trade capture & allocation: Embedded directly in the contract.

• Settlement instruction: Removed, as the contract contains all logic.

• Custodian matching: Obsolete; custodians read the shared ledger.

• Position & cash reconciliation: Redundant; balances update atomically.

• Exception management: Reduced to smart contract validation errors, handled at execution time.

The traditional reconciliation team’s role shrinks from daily fire-fighting to high-level oversight.

The Advantages, Challenges, and Considerations of On-Chain Reconciliation

Smart contracts and on-chain systems provide several advantages compared to traditional reconciliation.

1. Operational efficiency — Vastly fewer checks, reconciliations, and manual interventions.

2. Reduced settlement risk — Atomic DvP ensures no failed settlements due to insufficient securities or cash.

3. Cost savings — Smaller back-office footprint, fewer exception-handling staff.

4. Transparency — Shared, immutable ledger visible to counterparties, custodians, and regulators.

5. Accelerated settlement — Supports T+0 or real-time settlement, a major leap from T+2 norms.

Of course, this shift introduces new considerations:

• Interoperability: If multiple chains host different assets, liquidity may fragment. Standards must emerge.

• Legal recognition: Jurisdictions must formally recognize blockchain entries as binding records of ownership.

• Custody evolution: Custodians will not vanish, but their focus shifts from reconciliation to safeguarding keys, monitoring compliance, and integrating off-chain and on-chain data.

• Error handling: Smart contracts must have governance frameworks to address disputes, reversals, or forced regulatory actions.

• Data privacy: Permissioned blockchains must balance transparency with confidentiality requirements for institutions.

Thus, while reconciliation itself disappears, oversight, governance, and risk management remain essential.

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Industry Implications and Future Outlook

The broader implication of on-chain reconciliation is a structural shift in how the industry allocates resources. Today, the reconciliation “market” is enormous: technology vendors, managed service providers, and entire back-office teams are dedicated to resolving breaks. As tokenization spreads, this function shrinks. The focus moves from matching files to building secure, auditable smart contracts and shared infrastructure.

Major institutions have already recognized this trend. DTCC has piloted tokenized settlement models through its Project Whitney and Project Ion, while Nasdaq has announced platforms for tokenized assets that integrate directly with its market infrastructure. Just this past November, Morgan Stanley released their JPM Coin, making it the first bank to issue USD deposit tokens on a public chain, enabling its use in real-time, on-chain trade reconciliations. These initiatives demonstrate that incumbents are preparing for a transition where reconciliation is no longer a market function but an architectural feature.

Industry experts are already predicting how reconciliation’s evolution will play out. SEC Chair Paul Atkins believes tokenization will become central to US markets in “a couple of years.” Many foresee on-chain systems gradually replacing the Automated Clearing House (ACH) system altogether, with Custodia Bank CEO Caitlin Long projecting it will be obsolete within five years. She expects this transition to begin with banks as they begin to adopt primitive tokenization and blockchain systems, providing a foundation for others to build off until the technology becomes commonplace across capital markets. Figure Co-Founder Mike Cagney believes the emergence of blockchain will create financial ecosystems for assets that for the first time can be made liquid, creating both disruption and great opportunity. These new architectures have the potential to restructure entire markets.

Conclusion

Tokenized securities and blockchain settlement redefine the role of reconciliation, changing the very architecture of the market. Instead of reconciling thousands of records daily, firms can design, audit, and monitor smart contracts to enforce economic truth by default. Custodians can focus on safeguarding digital assets, regulators gain real-time transparency, and investors see reduced costs and risks. With smart contracts encoding trade economics and atomic settlement, reconciliation ceases to be a manual process and becomes inherent in the system’s design. For the first time, capital markets could achieve what decades of reconciliation teams have worked toward: complete alignment of economic truth across all participants.

I do not expect the reconciliation market to fall off a cliff in the near future; legacy systems and operational inertia will ensure it persists for some time. However, with institutions such as DTCC and Nasdaq actively working to tokenize every security, the inevitable is arriving much sooner than most expect. The back office of the future will be coded into the contract itself, and reconciliation as we know it will steadily fade into obsolescence.

How soon do you think the transition will occur? Will these new systems bring about true disintermediation, or will traditional structures persist for longer than experts are predicting?

Leave your thoughts in the comments below and keep the conversation going!

This article was written by Roque Martinez, CTO of Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2025 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

This is Part 3 of our AI Giants series, where we examine the successes and shortcomings of today’s largest AI firms. Explore our archive to read Part 1, covering Claude’s recent reliability crisis, and Part 2, exploring Google’s path to success in the AI industry.

OpenAI’s dominant position in AI faces its most serious challenge yet. On December 1, 2025, CEO Sam Altman issued an internal “code red” memo directing all resources toward improving ChatGPT amid intensifying pressure from Google’s Gemini 3 and Anthropic’s Claude. The declaration marks a dramatic reversal from December 2022, when Google issued its own code red in response to ChatGPT’s launch. The hunter has now become the hunted.

Despite commanding 800 million weekly active users and reaching $13 billion in annualized revenue, OpenAI’s technological lead has narrowed considerably. GPT-5 released in August 2025 to mixed reviews from general user bases, failing to deliver the transformative leap many expected. GPT-5.1, its November successor, addressed some criticisms but arrived just as Gemini 3 and Sonnet 4.5 began outperforming ChatGPT on key benchmarks. Now, with competitors stealing market share, OpenAI finds itself on the edge of a crisis. For a company valued at $500 billion and burning $8 billion annually, the stakes could not be higher.

Altman’s “Code Red” Reveals Competitive Anxiety

The December 1st memo landed with unusual urgency. According to The Information and Wall Street Journal, which viewed the internal communication, Altman declared: “We are at a critical time for ChatGPT.” The code red designation represented an escalation from the “code orange” declared in October by Nick Turley, head of ChatGPT, who warned the company would face “the greatest competitive pressure [it’s] ever seen.”

The immediate catalyst was Google’s mid-November release of Gemini 3, which surpassed GPT-5 on industry benchmarks for multimodal reasoning, mathematics, and code. Google’s model topped the LMArena leaderboard and outperformed GPT-5 on Humanity’s Last Exam. Within 24 hours, over one million users had tried Gemini 3, while the broader Gemini ecosystem had grown to 650 million monthly active users.

Altman’s response was swift and sweeping. The memo directed resources toward improving ChatGPT’s personalization, speed, reliability, and image generation capabilities, while putting several major initiatives on hold:

• Advertising products currently in beta testing

• Pulse, a personalized morning updates assistant

• AI shopping and health agents

• Autonomous AI agent development

The company also instituted daily calls for employees working on ChatGPT improvements and encouraged temporary team transfers to focus on the core product. Altman indicated that OpenAI would release a new reasoning model “next week” that beats Gemini 3 in internal evaluations. This promise frames the code red as both a defensive response and offensive preparation.

After GPT-5 Disappointment, GPT-5.1 Attempts Recovery

OpenAI’s flagship model journey in 2025 has been turbulent. GPT-5, released August 7th, was billed as the company’s first “unified” model that combined reasoning and general capabilities. The technical improvements were real: 94% on the AIME 2025 math benchmark, 74.9% on SWE-bench Verified for software engineering, and roughly 45% fewer hallucinations than GPT-4o.

But consumer reception proved rocky. Users described the model as “flat,” “clinical,” and “lobotomized.” Sam Altman acknowledged on X that “we for sure underestimated how much some of the things that people like in GPT-4o matter to them.” The backlash intensified when OpenAI forcibly migrated users to GPT-5, deleting access to legacy models like GPT-4o without warning. Thousands of users signed a petition demanding restoration. Within 24 hours, OpenAI partially reversed course.

GPT-5.1, released November 12, 2025, attempted to address these complaints with a warmer default tone and extensive personalization features allowing users to select from preset styles (Professional, Candid, Quirky, Nerdy, Cynical, Friendly, and Efficient). The model also introduced adaptive reasoning that dynamically adjusts thinking time based on task complexity, delivering 2-3x faster responses than GPT-5 with comparable or better quality. A week later, OpenAI released GPT-5.1-Codex-Max with novel “compaction” technology. Designed for complex, multi-hour coding sessions, this capability enabled coherent work across millions of tokens.

OpenAI’s Financial Balancing Act

OpenAI’s financial trajectory combines remarkable growth with staggering spending. Revenue doubled from $6 billion ARR in January 2025 to $13 billion by mid-year, crossing the $1 billion/month milestone in July. The company projects $100 billion in revenue by 2028-2029. Consumer subscriptions through ChatGPT (Plus at $20/month, Pro at $200/month) drive approximately 70% of revenue, with enterprise and API services contributing the remainder.

The user base has exploded to over one million paying business customers and roughly 15 million ChatGPT Plus subscribers. Enterprise seats grew 9x year-over-year, while API usage for coding and agent-building work more than doubled following GPT-5’s launch. Major enterprises including Morgan Stanley, T-Mobile, Target, and Cisco have all deployed OpenAI’s models at scale.

Yet profitability remains distant. First-half 2025 saw $2.5 billion in cash burn against $4.3 billion in revenue, with full-year losses projected at $8 billion. OpenAI has committed over $1 trillion in infrastructure spending over the next decade, including the $500 billion Stargate Project with SoftBank and Oracle, a $300 billion Oracle cloud deal, and a $38 billion AWS partnership announced in November. Microsoft, which has invested over $14 billion, reportedly lost $3.1 billion on its OpenAI stake in fiscal Q1 2025 alone.

The October 2025 funding round valued OpenAI at $500 billion (up from $157 billion just a year earlier), making it the world’s most valuable private company. But this valuation requires extraordinary growth: the company must reach $200 billion in revenue by 2030 to achieve profitability, per internal projections. An IPO at potentially $1 trillion valuation looms for 2026 or 2027, assuming the company can demonstrate a credible path to sustainable profits.

Stacking Up Against Claude and Gemini

*Note: In lieu of OpenAI’s announcement of a new reasoning model meant to compete with Opus 4.5 and Gemini 3.0, we chose to compare ChatGPT 5/5.1 to Claude Sonnet 4.5.

OpenAI faces distinct challenges from its two primary competitors. Against Anthropic’s Claude, the battle is for enterprise hearts and wallets. Anthropic’s revenue surged from $1 billion in December 2024 to $4-5 billion ARR by mid-2025. More troubling for OpenAI, Anthropic now commands 42% market share in coding versus OpenAI’s 21%, while also growing its enterprise market share to 32% compared OpenAI’s decline to 25%. Additionally, Anthropic generates $211 in revenue per user compared to OpenAI’s $25, a reflection of its enterprise-first strategy versus OpenAI’s consumer focus.

Claude Sonnet 4.5 currently leads GPT models in the crucial SWE-bench Verified benchmark at 77.2% compared to GPT-5.1’s 76.3%. On OSWorld (computer use tasks), ClaudeSonnet 4.5 achieves 66.3%, giving it a massive lead over OpenAI’s models. Claude can also maintain autonomous focus for 30+ hours on complex multi-step tasks, making it preferred for long-running agentic coding work.

Against Google’s Gemini, the threat is an existential distribution advantage. Google can integrate AI across Chrome, Android, Gmail, YouTube, and Search, reaching billions of users without acquisition costs. While it was Gemini 3’s benchmark dominance that triggered Altman’s code red, Google’s deeper advantage is economic: a cash-rich core business that can subsidize free AI offerings indefinitely, plus custom TPU chips reducing dependence on Nvidia.

OpenAI’s strategic responses include deepening enterprise tooling (AgentKit for agent development), expanding the product ecosystem (Sora video generation, shopping features), and building infrastructure independence through massive data center investments. The AWS deal notably diversifies cloud partnerships beyond Microsoft, suggesting some strategic hedging.

What the Benchmarks Reveal

On software development benchmarks specifically, the competition is closer than headlines suggest. GPT-5.1 achieves 76.3% on SWE-bench Verified versus Claude Sonnet 4.5’s 77.2% – a gap of roughly 9 additional issue resolutions per 1,000 attempts. On Aider Polyglot (multi-language code editing), GPT-5 scores 88%, among the highest recorded.

GPT-5’s advantages lie in pricing (58% cheaper on input tokens at $1.25/million versus $3.00), mathematical reasoning (94.6% AIME 2025, 87.3% GPQA Diamond), and speed(GPT-5.1 delivers 2-3x faster responses). The model family also offers cost-efficient variants (GPT-5-mini at $0.25/million input tokens and GPT-5-nano at $0.05), enabling high-volume deployments that would be prohibitively expensive with Claude.

Developer opinion splits along use-case lines. Cursor’s CEO Michael Truell called GPT-5 “the smartest coding model we’ve ever tried,” while Qodo found it “led in catching coding mistakes... often the only one to catch critical issues, such as security bugs.” But for long-running autonomous tasks and complex refactoring, developers consistently prefer Claude’s stability and consistency.

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Consistently Inconsistent User Sentiment

Enterprise users have generally embraced GPT-5. Box CEO Aaron Levie called it “a breakthrough... performing with a level of reasoning that prior systems couldn’t match.” The Latent Space newsletter declared it “the closest to AGI we’ve ever been.” API usage growth and enterprise adoption metrics support these endorsements.

Consumer sentiment tells a different story. Trustpilot reviews from November 2025 describe GPT-5 as “a paid beta disguised as a finished product,” with complaints about tools crashing mid-task, output loops, and unreliable memory features. The forced legacy model deprecation sparked lasting resentment. Only 5% of ChatGPT’s 800 million weekly users pay for subscriptions, suggesting most users find the free tier sufficient or remain unconvinced of premium value.

Legal challenges mount as well. Seven lawsuits filed in California courts in late 2025 allege ChatGPT “drove people to suicide and harmful delusions,” claiming OpenAI “knowingly released GPT-4o prematurely” despite internal safety warnings. Public Citizen demanded withdrawal of Sora 2, citing “a consistent and dangerous pattern of OpenAI rushing to market with a product that is either inherently unsafe or lacking in needed guardrails.”

Industry analysts remain measured. MIT Technology Review characterized GPT-5 as “above all else, a refined product” with “a more pleasant and seamless user experience... but it falls far short of the transformative AI future that Altman has spent much of the past year hyping.” AI critic Gary Marcus noted that “GPT-5 is barely better than last month’s flavor of the month,” and highlighted that Polymarket odds for OpenAI having the “best AI model at end of August” dropped from 75% to 14% within an hour of GPT-5’s launch.

The Road Ahead: Execution Under Pressure

OpenAI’s December 2025 situation crystallizes a company at an inflection point. The technological moat that once seemed insurmountable has eroded. Competitors have caught up on capabilities while developing distinct advantages; Anthropic in enterprise reliability and coding precision, Google in distribution and infrastructure economics.

The code red response shows that OpenAI recognizes the urgency. Delaying advertising and autonomous agents to focus on core ChatGPT improvements represents a strategic choice to protect the primary revenue engine. The promised new reasoning model that beats Gemini 3 would demonstrate continued technical leadership if delivered.

Yet the fundamental challenges persist. A $500 billion valuation demands extraordinary growth against competitors with deeper pockets (Google) and higher-margin business models (Anthropic). The path to 2029 profitability requires revenue to grow roughly 15x while managing trillion-dollar infrastructure commitments. The race to AGI that Altman has championed for years now includes well-funded competitors on every side.

The reversal from Google’s 2022 code red to OpenAI’s 2025 code red serves as a tale of both caution and possibility. Three years ago, OpenAI proved that an underdog could reshape the industry. Today, the question becomes whether it can sustain leadership when the giants have mobilized and the upstarts have sharpened their focus. As competitors close the gap, OpenAI faces a steep upward battle to protect its market share. Only time will tell if they make the right moves to stay on top.

This article was written by Max Kozhevnikov, Data and Software Engineer at Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2025 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

This is Part 2 of our AI Giants series, where we examine the successes and shortcomings of today’s largest AI firms. In Part 1, we dove into Claude’s recent reliability crisis and the pitfalls that come with relying on cloud-hosted AI systems.

In October 2025, Google achieved something remarkable, posting its first-ever $100 billion quarterly revenue. With cloud services accelerating at 34% year-over-year growth, AI products drawing in more users and revenue, and the early success of their newly released Gemini 3.0, Google seems primed to capture a large share of the AI market. Yet before 3.0’s release, Google’s top model, Gemini 2.5, ranked a distant eighth on SWE-bench Verified, the industry’s premier software engineering benchmark, lagging behind (former) first place Claude Sonnet 4.5 by 17 percentage points. Google had built an AI juggernaut that exceled at analyzing six-hour videos and processing two million token context windows, but developers would increasingly turn to Claude when mission-critical code was on the line.

This was Gemini’s blurry vision problem. It provided extraordinary capabilities in some dimensions and showed concerning gaps in others, all while the parent company printed money. Gemini 3.0 promises to fill these gaps, with early reviews showing promising progress and unmatched model testing scores. Google now has the benchmark supremacy to attract more users while controlling the world’s largest digital ecosystem. If Gemini 3.0 continues to impress, it may cause a massive pivot in the AI ecosystem.

The AI Born from DeepMind’s Merger

To understand how Google reached this point, we must first explore Google’s history of AI development, diving into the advantages and limitations of Gemini that landed them in the market position they’re seeking to break out of.

Google’s path to Gemini began in 2023. By merging Google Brain and DeepMind into a single organization (Google DeepMind), they ended years of internal AI competition. DeepMind brought the pedigree of AlphaGo and AlphaFold, breakthrough systems that respectively defeated world champions in the game of Go and solved protein folding, while Google Brain contributed TensorFlow and the Transformer architecture that powers modern AI. The merger aimed to focus Google’s resources on competing with OpenAI and Anthropic rather than competing with itself.

Gemini 1.0 launched December 2023 with bold claims about multimodal superiority. Google marketed it as “built from the ground up for multimodality,” trained simultaneously on text, images, audio, and video rather than stitching separate models together. The technical approach differed fundamentally from competitors who added vision capabilities to text-first models. Gemini 2.5 Pro arrived March 2025, topping the LMArena leaderboard with the largest debut score jump in benchmark history. The model came in three sizes. Ultra for complex tasks, Pro for versatility, and Flash for speed and cost efficiency.

By early November 2025, Gemini reported 650 million monthly active users and powered 1.5 billion AI Overview interactions in Google Search. The company processed 7 billion API tokens per minute and counted 1.5 million developers who had tried the models. Google embedded Gemini across Search, Android, YouTube, Gmail, Maps, and Workspace, pursuing an “ambient AI” strategy rather than standalone chatbot dominance.

Where Gemini Has Thrived

Google’s models have achieved genuine technical differentiation in two domains: multimodal capabilities and context window size. Multimodal architecture is of particular importance, as Google pre-trained Gemini on text, images, audio, video, and code simultaneously.

Gemini 2.5 Flash Image generates images natively within conversations, edits uploaded photos conversationally, and maintains character consistency across generation rounds. DALL-E 3 requires separate workflows; Midjourney operates through Discord; Stable Diffusion demands technical setup. Only Gemini combines generation and analysis in natural conversation.

Video capabilities create the starkest competitive gap. Gemini 2.5 accepts video uploads of up to five minutes, processes YouTube URLs directly via API, and analyzes up to six hours of content through its two-million-token context window. The model achieves 81.3% accuracy (with subtitles) on VideoMME’s overall benchmarks, demonstrating temporal reasoning like counting discrete events across timelines. GPT-4o offers real-time camera interaction but cannot process uploaded video files. Claude doesn’t support video at all.

Veo 3, Google’s text-to-video model integrated into Gemini Advanced, achieved synchronized AI-generated audio alongside video in May 2025. The model generates 8-second clips at 1080p in under two minutes. Comparisons with OpenAI’s Sora 2 show mixed results. Sora produces longer clips with smoother motions, while Veo offers excellent cinematic coherence and faster generation. Gemini remains the only major LLM platform offering both video understanding and video generation in unified interface.

Context window supremacy provides a quantitative advantage. Gemini 2.5 Pro accepts 1,048,576 tokens as standard input with experimental support extending to two million tokens (the equivalent of 1,500 pages of text or 30,000 lines of code), while performance benchmarks show near-perfect retrieval accuracy even at maximum context length. In comparison, GPT-4 Turbo handles 128,000 tokens, with Claude supporting 200,000 tokens. Gemini’s context window isn’t merely larger – it enables qualitatively different applications, allowing legal teams to load entire depositions, developers to process complete codebases without chunking, and researchers to synthesize massive document collections.

Fourth Place Where Developers Care Most

Gemini 2.5 was not without its faults, however. The benchmark reality check arrived throughout 2025 as competitors raised the bar. On SWE-bench Verified, which tests AI on authentic GitHub issues requiring multi-file edits and systems thinking, Gemini 2.5 Pro achieved a 53.6% accuracy score, while Claude Sonnet 4.5 reached 70.6%, a gap representing hundreds of failed pull requests in production environments. Terminal-Bench showed Claude 4.5 scoring 42.8% on command-line automation while Gemini 2.5 managed just 32.6%.

However, Gemini dominated elsewhere. Humanity’s Last Exam showed 21.6% for Gemini 2.5 versus Claude’s 13.7%, proving itself as a leading model on graduate-level scientific reasoning and mathematics competitions. Google had built the world’s best AI for expert knowledge while failing to break the top 3 in practical engineering tasks.

The competitive landscape intensified throughout 2025. Claude Opus 4.1 arrived August 5, and Claude Sonnet 4.5 launched September 29, both demonstrating significant coding capability improvements. Google’s response was lackluster, pushing out incremental Flash model updates rather than fundamental leaps. Developers consistently reported that Claude delivered fewer bugs, more consistent architecture, and better test scaffolding in head-to-head comparisons.

Powerful But Unreliable

Developer sentiment oscillated between impressed and frustrated. The most consistent praise centered on capabilities competitors could not match. Developers routinely loaded 30,000+ lines of code into Gemini’s million-token context window for whole-codebase analysis, generated complete presentations with images in single prompts, and analyzed multi-hour videos without preprocessing.

But reliability concerns dominated criticism. The most frequent complaint was responses that terminated mid-sentence, not from token limits, but from apparent completion signaling bugs. One experienced developer articulated the frustration precisely: “Reliability matters more than peak performance. I’d rather work with a model that consistently delivers complete responses than one that gives me half-thoughts I have to constantly prompt to continue.”

An October 2025 study published by Deutsche Welle and 21 other international public broadcasters compared Gemini 2.5, ChatGPT, Perplexity, and Copilot’s ability to accurately represent the news. The study found Gemini 2.5 performed the worst, with 72% of its responses having “significant sourcing issues.” The model hallucinated with sufficient frequency that multiple reviews report the Deep Research feature will sometimes fabricate references. Users reported incorrect calculations despite strong performance on formal mathematics benchmarks.

The September 2025 introduction of aggressive usage limits triggered community backlash. Free users received just five Gemini 2.5 Pro prompts daily. AI Pro subscribers ($19.99/month) faced 100-prompt daily caps. Developers accustomed to extensive coding sessions hit limits rapidly, perceiving this change as calculated monetization pressure precisely when Google needed developer mindshare.

Tom’s Guide crystallized the competitive positioning after testing Claude 4.5 against Gemini 2.5. “Claude consistently excelled when the task required precision, structure, or atmospheric storytelling, while Gemini shined in situations that called for creativity, playfulness, or practical developer workflows.” Essentially, mission-critical work should go to Claude, everything else could go to Gemini.

The 3.0 Upgrade

Despite these past issues, Gemini 3.0 might trigger a change of opinion. As developers have experimented with the new model over the last 48 hours, many have reported massive improvements in its agentic capabilities and usefulness as a coding tool. A report from Google DeepMind shows Gemini 3.0 dominates the benchmarks that once held it back.

The test pitted Gemini 3.0 against Gemini 2.5, Claude Sonnet 4.5, and ChatGPT-5.1. The result was Gemini 3.0 beating the other models in 19 of the 20 different industry benchmarks they were tested against. Some key comparisons are outlined below.

• Humanities Last Exam (General Test of 2,500 Questions)

  • Gemini 3.0 Pro – 37.5%

  • ChatGPT-5.1 - 26.5%

  • Claude Sonnet 4.5 - 13.7%

• Terminal-Bench (Agentic Terminal Coding)

  • Gemini 3.0 Pro – 54.2%

  • ChatGPT-5.1 - 47.6%

  • Claude Sonnet 4.5 - 42.8%

• LiveCodeBench Pro (Compteitive Coding Problems)

  • Gemini 3.0 Pro – 2,439 ELO

  • ChatGPT-5.1 - 2,243 ELO

  • Claude Sonnet 4.5 - 1,418 ELO

• Simple QA Verified (Simple Question Fact Checking)

  • Gemini 3.0 Pro – 72.1%

  • ChatGPT-5.1 - 34.9%

  • Claude Sonnet 4.5 - 29.3%

According to Google, the only test in which Gemini 3.0 was beaten was SWE-bench Verified, losing to Sonnet 4.5 and GPT-5.1 by 1% and 0.1% respectively. Yet according to SWE-bench’s own website, Gemini 3.0 ranks first with a 74.2% rating compared to second place Sonnet 4.5’s 70.6%. Seeing as we are still in the early stages of Gemini 3.0’s release, discrepancies are to be expected, and the numbers are bound to change, but all signs point to Gemini matching or surpassing the competition’s coding capabilities. A shift in AI market share appears imminent.

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Third Place in the Market… For Now

Gemini currently holds 13.5% market share in AI chatbots as of early November 2025, ranking third behind ChatGPT’s commanding 61% and Microsoft Copilot’s 14%. That understates actual reach, as Google embeds Gemini across properties with billions of users. Monthly active users grew to 650 million, while daily active users hit over 40 million.

Enterprise adoption shows the strongest traction. 46% of U.S. enterprises deploy Gemini in productivity workflows, doubling 2024’s levels. Fortune 500 penetration reached 41%, with Gemini being used in at least one department. Through the first half of 2025 alone, Google hosted 27 million enterprise users processing 2.3 billion document interactions through Workspace, with 92% of accounts including Gemini features. Their value proposition initially centered on seamless integration, with the raw capabilities now arriving.

The developer ecosystem remains Google’s struggle point, but as more users experiment with Gemini 3.0, that will likely change. Google counts 420,000 active API users, up 61% annually, with 310 million daily API requests. Developer adoption skews toward startups attracted by cost advantages. Gemini 1.5 Flash costs $0.07 per million tokens compared to GPT-4’s $3 per million, providing superior price-performance and multimodal breadth over specialized excellence. Gemini 3.0 cannot deliver the same price advantage as 1.5 Flash, but its improved capabilities will surely attract developers looking to improve their workflow. Time will tell if Google captures more of this market, but the continued praise of Gemini 3.0 is a strong indicator it will.

Clearing Gemini’s Blurry Vision

Gemini occupies an intriguing position in late 2025. The model leads in benchmarks measuring expert knowledge, multimodal reasoning, and mathematics. It offers the largest context windows commercially available, processes video better than competitors, and integrates across Google’s ecosystem. And it now appears to have finally found the key to success in software engineering, with Gemini 3.0 leading developer benchmarks, providing quality terminal automation, and freeing itself of the accuracy and reliability issues that long frustrated developers.

Gemini 3.0’s release revealed Google’s strategy. They first optimized Gemini for ecosystem integration and multimodal breadth over specialized coding excellence. They focused on building a model that excelled at tasks that leverage Google’s unique advantages, like analyzing YouTube videos, understanding context across Workspace, and processing massive documents. Now, Gemini can seemingly handle tasks requiring sustained logical reasoning through complex codebases, the precise area where other competitors, such as Claude, dominate.

Google’s financial strength and ecosystem control allowed strategic patience competitors could not afford. Alphabet prints money while investing billions in AI infrastructure. The company didn’t need immediate benchmark supremacy because it monetizes AI through subscriptions, advertising, and enterprise cloud services. OpenAI and Anthropic must justify their lofty valuations through direct AI product revenue.

This strategy has set Google up for success. Competitors can no longer offer capabilities that Gemini cannot match, while Workspace integration, cost efficiency, and multimodal capabilities make its array of models perfect for enterprise customers. Real demand existed even before Gemini 3.0’s release, exemplified by expansive enterprise deployments, such as KPMG reaching 90% employee adoption within two weeks, and the November 2025 Apple deal valuing Gemini at $1 billion annually.

All that remains to be seen is if the optimism surrounding Gemini 3.0’s coding abilities continues as more developers test it for themselves. Developer sentiment matters because individual choices compound into enterprise standards. Google controls distribution through Workspace and Android but doesn’t yet control developer mindshare. Initial reactions, however, tell us they may have it very soon.

The next six months will show how well Google’s bet pays off. The coding benchmark gaps appear to have been closed while preserving multimodal advantages. Enterprise customers deploying Gemini Enterprise must demonstrate productivity gains justifying premium pricing. The Apple partnership launching Spring 2026 must succeed. And developer sentiment must continue to shift in Gemini’s favor.

Google enters this position with financial strength, record revenues, and industry-leading cloud growth. The parent company thrives while the AI product gains ground on the competition. Other large cloud-based models remain prosperous, but Google seems to have found their corrective lenses. Gemini’s vision is now clear.

This article was written by Max Kozhevnikov, Data and Software Engineer at Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2025 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

In Part 1, we explored how privacy-preserving AI is transforming portfolio management, enabling hedge funds to optimize their portfolios in ways previously unimaginable. But portfolio optimization is only part of the equation. Hedge fund performance isn’t just about allocating capital to the right assets; it’s also about the human factor – how traders, portfolio managers, and analysts make decisions in real time.

Enter the concept of the Adaptability Quotient (AQ): a measure of how effectively a trader can adapt to new market conditions, shifting strategies, and evolving opportunities. In an industry where even the smallest edge can translate to millions, if not billions, of dollars, the ability to assess and optimize AQ in traders is a game-changer. And with today’s privacy-preserving AI, hedge funds can now do exactly that.

Why Adaptability Matters in Hedge Fund Performance

In the world of hedge funds, markets move fast, and no two trading days are the same. The ability of a trader to respond quickly and intelligently to changing conditions – whether it's a sudden volatility spike, a liquidity crunch, or a geopolitical shock – can be the difference between outperformance and costly losses.

Traditionally, a trader’s adaptability has been gauged through subjective measures: gut feelings, interviews, research, X/Twitter, by analyzing simple metrics like profit-and-loss (P&L) statements over time, etc. But these approaches miss crucial nuances in how traders behave under pressure, manage risk, and adjust their strategies. And are very prone to bias and human error.

That’s where AI comes in. With the right data and algorithms, we can now measure and optimize AQ, giving hedge funds deep insights into how their traders operate and, more importantly, how they can improve.

How Privacy-Preserving AI Analyzes Trader Behavior

Just as we discussed in Part 1, privacy-preserving AI allows hedge funds to leverage powerful machine learning models while keeping proprietary data safe. When it comes to analyzing trader behavior, this becomes critical. Many hedge funds would hesitate to share sensitive trading data with external AI vendors, but with technologies like federated learning and homomorphic encryption, AI models can be trained on distributed, encrypted data, meaning that the AI learns from each trader’s behavior without exposing their individual trading records.

This ability to analyze vast quantities of behavioral data in an automated way opens a new frontier for evaluating and improving AQ. By feeding trading data into AI algorithms, hedge funds can uncover patterns that might otherwise go unnoticed, such as:

• Reaction to Volatility: Does the trader maintain a level head and adjust strategies accordingly, or do they panic and overreact to market swings?

• Risk Management: Does the trader consistently adhere to risk limits, or do they take on more risk after a string of losses in a bid to recover?

• Strategy Shifts: How quickly does the trader abandon a failing strategy and pivot to a more successful one? Do they overstay positions when market conditions have clearly changed?

By identifying these patterns, AI doesn’t just highlight the issues. It suggests specific, actionable improvements. Perhaps one trader needs to be more disciplined in risk management, while another could benefit from increasing their focus on liquidity in turbulent markets. These tailored recommendations give hedge funds a roadmap for boosting individual and team performance. In some cases, external factors can be identified to help build up traders over time.

Real-Time Behavioral Analysis: An Always-On AI Analyst

One of the greatest advantages of AI is its ability to operate in real-time. While human coaches and supervisors can only assess trader behavior after the fact, AI can monitor trading activity live, flagging issues as they arise. It’s like having a 24/7 analyst or risk manager watching each trader’s every move, ensuring that behavioral risks are caught before they manifest in costly mistakes. And it does so in a very human acceptable kind of way.

Imagine a scenario where a trader has been riding a winning streak, only to see their strategy begin to falter in response to an unexpected geopolitical event. The AI notices that instead of cutting losses and pivoting, the trader is doubling down – a classic case of loss aversion. Or perhaps it is the other way around, the trader might run away when they should stay in or invest more. The context here is important. Neither is always the right choice. The AI flags this in real-time and suggests alternative strategies that better fit the new market conditions or regime. This isn’t just a theoretical capability; it’s happening now with today’s AI.

Moreover, the AI can take into account not just P&L data but other behavioral indicators as well, like reaction time to news events, how often a trader deviates from their typical strategy, consistency of trading behavior, or the frequency of holding positions past their risk threshold. By continuously monitoring and analyzing these metrics, hedge funds can fine-tune trader behavior before small issues snowball into bigger problems.

From Human Bias to Data-Driven Precision

Human biases are a well-documented challenge in trading. Whether it’s overconfidence, confirmation bias, or the classic tendency to “double down” after a loss, these biases can severely hurt performance. AI, with its data-driven objectivity, excels at spotting these biases and offering corrective strategies.

One of the most interesting uses of AI in this context is through reinforcement learning. AI can simulate various market scenarios, allowing traders to test different strategies in a risk-free virtual environment. Over time, the AI learns which behaviors lead to better outcomes, and it can nudge traders towards more adaptive, less biased decisions.

Imagine this: AI detects that a trader consistently struggles with loss aversion, leading to bad decisions when markets move against them. The AI recommends specific reinforcement learning exercises (like taking hypothetical losses in simulated environments and adapting in real-time) designed to help the trader overcome this bias. Over time, these exercises train the trader to act more rationally under pressure, improving their AQ in the process.

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Safeguarding Data Privacy While Enhancing Human Capital

Of course, the challenge of implementing AI for behavioral analysis comes back to privacy. Traders and hedge funds are understandably protective of their strategies and techniques. The beauty of privacy-preserving AI is that it enables the hedge fund to analyze its traders’ behavior without exposing sensitive data to external vendors or even other teams within the firm.

Technologies like secure multi-party computation allow AI models to collaborate across data sets from different teams or even different firms, all while keeping proprietary information safe. In many cases, everything we have discussed in this paper can run on a laptop, not connected to the internet. For hedge funds, this means you can benchmark trader behavior against industry norms or peer funds, without ever sharing proprietary data.

This approach also ensures that behavioral insights are generated at scale, across multiple traders and strategies, providing a rich, anonymized dataset for continuous improvement. It’s the perfect blend of improving human capital and maintaining the highest levels of privacy and security. This gives the Chief Investment Officer, Chief Risk Manager, and other key stakeholders and customers significant improvements in their visibility and consistency of behavior across the organization.

The Future of Hedge Fund Performance: Merging Human and Machine

The goal here is not to replace traders with machines. In fact, I believe the opposite: the most successful hedge funds will be the ones that leverage AI to enhance human performance, not eliminate it. Traders will always bring valuable intuition, experience, and creativity to the table – qualities that no machine can fully replicate.

But by combining human ingenuity with the raw analytical power of AI, hedge funds can unlock new levels of performance. Privacy-preserving AI provides a way to optimize trader behavior, measure adaptability, and mitigate human biases, all while keeping sensitive data secure.

The next generation of hedge funds will be those that not only optimize their portfolios but also actively enhance the Adaptability Quotient of their traders. AI can be the coach, the risk manager, and the behavioral analyst, allowing traders to reach their full potential in an increasingly complex market landscape.

Are your traders adaptable enough to thrive in tomorrow’s markets? With the right AI tools in place, the answer can be a confident yes.

Just like with portfolio optimization, the tools are here, and the future is now. The only question is, will your fund harness the power of AI to build a team of traders who can adapt to any market condition and consistently deliver alpha? The opportunity is clear, and those who seize it will find themselves at the forefront of a new era in hedge fund management.

This article was written by Sultan Meghji, CEO of Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2025 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

This is Part 1 of our AI Giants series, where we examine the successes and shortcomings of today’s largest AI firms.

In August 2025, Anthropic’s Claude, the developer’s favorite AI coding assistant, experienced a six-week reliability crisis that affected 30% of users, triggering a mass exodus to other major LLM competitors such as OpenAI’s ChatGPT and local AI systems. Three simultaneous infrastructure bugs degraded response quality just as the company rolled out controversial usage limits, exposing the fragility of cloud-dependent AI development. The crisis revealed a fundamental tension in modern AI: the trade-off between cutting-edge cloud models and the reliability, privacy, and control of local alternatives.

The incident marked a turning point. While Anthropic’s services have since stabilized and the company maintains a strong 32% enterprise market share, the crisis accelerated a shift already underway; developers are diversifying their AI infrastructure, embracing local models like Ollama, and questioning whether cloud AI providers can deliver the reliability that mission-critical applications demand.

The Safety-Focused Upstarts Who Left OpenAI

Anthropic emerged in early 2021 when seven researchers, led by siblings Dario and Daniela Amodei, left OpenAI in what Dario later described as “a fundamental loss of trust in the leadership’s sincerity.” The departure was driven by concerns that safety was being sidelined for “shiny products” and commercialization. Dario, who had led the GPT-3 project and written most of OpenAI’s original charter, felt that crucial decisions about governance and safety were made without proper consideration.

The founding team brought serious credentials—backgrounds from OpenAI, Google Brain, and academia, many with physics PhDs. They structured Anthropic as a Public Benefit Corporation with a unique Long-Term Benefit Trust, designed specifically to prevent the kind of board crisis that would later engulf OpenAI in November of 2023. This governance model legally allows the company to prioritize public benefit over shareholder profit maximization, with five financially disinterested trustees holding power to elect directors.

At the heart of Anthropic’s approach is Constitutional AI, a framework where AI systems are trained with explicit values drawn from sources like the UN Universal Declaration of Human Rights, rather than relying purely on user feedback to guide its behavior. The methodology aims to make AI systems “helpful, honest, and harmless,” with transparent, adjustable principles.

Financially, the bet paid off. Anthropic has raised over $27 billion from investors including Google ($3+ billion), Amazon ($8 billion), and Lightspeed Venture Partners, reaching a $183 billion valuation by September 2025. Revenue exploded from $1 billion annualized in December 2023 to $4.5 billion by July 2025, with CEO Dario Amodei calling it “the fastest growing software company in history at [this] scale.”

Why Developers Chose Claude Over ChatGPT

While OpenAI positioned ChatGPT as an all-purpose consumer assistant, equipped with image generation and custom GPTs, Anthropic deliberately focused on depth over breadth, particularly for developers and coding work. This strategy proved effective: software development accounts for over 10% of all Claude interactions, and coding-related revenue surged 1,000% in just three months.

Claude’s developer appeal centered on three key differentiators. First, its massive context windows allowed developers to load entire repositories with source code, tests, and documentation. The standard window supports 200,000 tokens (enough for ~150,000 words or a small codebase) and up to 1 million tokens in beta, empowering developers to maintain full project context without costly summarization or chunking workflows.

Second, Artifacts transformed how developers prototype. This interactive code preview feature displays live, editable code in a dedicated side panel, enabling “vibe coding,” where developers describe what they want and watch it get built in real-time. Developers could iterate on React components, landing pages, and dashboards with immediate visual feedback.

Third, Claude Code brought AI assistance directly to the terminal. This repository-level coding agent could analyze entire codebases, handle Git workflows through natural language commands, and execute multi-file refactoring with coherent changes. By July 2025, 115,000 developers were using Claude Code to process 195 million lines of code weekly, with some engineers reporting 2-10x productivity gains.

The developer community noticed the difference. In the Pragmatic Engineer’s 2025 survey, Claude earned 533 mentions, an 8x increase from the previous year. Stack Overflow’s survey found 45% of professional developers used Claude Sonnet, demonstrating serious adoption beyond experimentation. Even leading AI coding tools like Cursor IDE and Aider switched to Claude 3.5 Sonnet as their default model, a powerful industry endorsement.

Six Weeks of Empty Promises

On July 28, 2025, Anthropic announced new weekly rate limits for Claude Pro and Max subscribers, effective August 28. The company claimed the limits would affect “less than 5% of subscribers” and were designed to prevent account sharing and 24/7 background usage. Community reactions ranged from mixed to negative, but the worst was yet to come.

Unknown to users, three separate infrastructure bugs were already degrading Claude’s performance. The first appeared August 5—a routing error that sent short-context requests to servers configured for 1-million-token context windows. While initially affecting just 0.8% of requests, the bug was “sticky,” meaning once affected, users stayed on the wrong servers. A load balancing change on August 29 amplified the problem dramatically, eventually impacting 16% of Sonnet 4 requests at its peak on August 31.

Two more bugs landed on August 25. A TPU misconfiguration caused random Thai and Chinese characters to appear in English responses and syntax errors in generated code. An XLA compiler issue excluded the highest-probability tokens from generating, subtly degrading output quality across multiple models. The timing was catastrophic. These bugs coincided with the controversial rate limit rollout, leading users to suspect Anthropic was intentionally throttling quality to save costs.

The developer community documented the degradation methodically. Reddit posts described Claude as “significantly dumber,” “ignoring its own plans,” and “lying about code changes.” GitHub issues piled up. Twitter was flooded with screenshots of broken outputs. The top post on r/anthropic, titled “Claude Is Dead,” received over 841 upvotes and organized a mass cancellation campaign. Users felt betrayed, especially when Anthropic initially denied systematic issues.

Anthropic finally published a detailed technical “apology” on September 17, after fixes were deployed between September 2-18. The company acknowledged, “it’s been a rough summer for us, reliability wise,” and admitted its evaluations “didn’t capture the degradation users were reporting.” CEO Dario Amodei posted simply: “I’m very sorry for the problems and we’re working hard to bring you the best models.”

The transparency was unusually thorough for the AI industry, with specific percentages, dates, and technical details. But the damage was done. Claude Code’s usage on developer benchmarks dropped from 83% to 70%, with OpenAI’s Codex gaining ground. Trust, the foundation of Anthropic’s brand, had taken a serious hit.

The Local Alternatives That Don’t Break (and Keep Getting Better)

As Claude stumbled, developers rediscovered an obvious truth: AI running on your own machine can’t have cloud outages. Local LLM tools like Ollama and LM Studio, which had been gaining quiet traction, suddenly became the resilient alternative.

Ollama, with over 155,000 GitHub stars, had established itself as the “Docker for LLMs,” a command-line tool that made running powerful AI models as simple as “ollama run llama3.2.” Built on llama.cpp with sophisticated model management and optimization, Ollama supported everything from Meta’s Llama to Google’s Gemma to specialized coding models like CodeLlama and DeepSeek. It offered OpenAI-compatible API endpoints, automatic GPU support for NVIDIA, AMD, and Apple Silicon, and a massive ecosystem of integrations.

LM Studio took a different approach with a polished desktop GUI. Point-and-click model installation, a built-in ChatGPT-like interface, and zero configuration made it ideal for developers who wanted to experiment without touching the command line. Both tools were completely free: no subscriptions, no usage meters, and no rate limits.

The appeal went beyond just reliability. Local LLMs offer genuine privacy, as proprietary code and sensitive information never leave your machine, crucial for enterprises in regulated industries. They provided unlimited inference at fixed hardware costs, making them economical for heavy users facing mounting API bills. They also eliminated vendor lock-in, allowing developers to switch between models freely and fine-tune for specific domains.

During August and September 2025, articles about running local LLMs with Ollama proliferated across developer communities. Reddit and Hacker News discussions “lit up” as users shared their local setups. The message was clear: when cloud AI fails, local alternatives keep working.

The technology had matured enough to be practical. Modern open-source models like Llama 3.2, Mistral, and Qwen rivaled proprietary models for many tasks. Consumer hardware (Apple Silicon MacBooks and RTX 4000 GPUs) could run surprisingly capable models at reasonable speeds. The developer experience was polished, and, most importantly, local models never have bad weeks or months.

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The New Path for AI Development

The August-September crisis didn’t kill Anthropic. Services are stable, the company maintains its market-leading position, and new model releases continue. But the incident revealed vulnerabilities that developers won’t forget.

Cloud AI dependency is now a single point of failure for thousands of businesses. Startups built entirely on OpenAI or Anthropic APIs faced operational crises during outages. The smart response emerging across the industry is multi-model redundancy: abstraction layers that allow automatic failover between providers, with local models as the ultimate backup. As one architect put it: “load balancing your intelligence layer.”

The privacy dimension matters more than companies initially realized. According to Pew Research, 81% of Americans worry AI companies will misuse their data, and Anthropic’s decision to extend data retention from 30 days to 5 years in August 2025 heightened concerns. For enterprises handling financial, medical, or biometric data, the self-hosted approach isn’t just preferable; it’s often legally required under GDPR and HIPAA.

What does this mean for developers today? The landscape has fundamentally shifted. Hybrid architectures are becoming standard. Use Claude or GPT-5 for cutting-edge capabilities but have Ollama running Llama or Mistral locally for when cloud services falter, privacy demands it, or when costs mount. The best AI coding setup in 2025 isn’t a single model, but a resilient system that gracefully degrades rather than failing completely.

The path forward is clear: embrace redundancy, invest in local alternatives, and design systems that assume cloud AI will occasionally fail.

This article was written by Max Kozhevnikov, Data and Software Engineer at Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2025 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

As the CEO of an AI firm focused on revolutionizing hedge funds, I’ve witnessed firsthand how Artificial Intelligence is transforming the financial industry. We are at a critical juncture where privacy-preserving AI technologies are unlocking even more powerful applications for hedge funds. This is the first in a multi-part series where I’ll be diving into this topic, starting with portfolio management. We will also cover trader behavior, regime change and more in future articles.

For hedge fund managers, optimizing a portfolio has always been an intricate dance of balancing risk, return, and diversification. Traditional quantitative methods have helped, but these methods are increasingly insufficient. We now live in a world of exponentially growing data, where hidden patterns and correlations that could drive performance are often buried deep within complex datasets. The solution? The current generation of privacy-protecting AI, which promises to take portfolio optimization to unprecedented heights.

Why Privacy-Preserving AI Matters for Hedge Funds

You might ask: why does privacy matter when we're talking about AI? Hedge funds, by their very nature, deal with extremely sensitive data. This includes not just financial data, but also proprietary trading strategies, unique factor models, and client information. Any hint of a leak could result in massive losses – not just monetarily but also in competitive edge.

Traditionally, hedge funds have been reluctant to hand over sensitive data to third-party vendors or rely on cloud-based AI systems because of these inherent risks. But privacy-preserving AI changes that.

Technologies like federated learning and differential privacy ensure that data never needs to leave its origin. Hedge funds can now use cutting-edge machine learning algorithms to optimize portfolios while keeping sensitive data protected. These techniques allow models to learn from data securely without exposing proprietary information. It’s a game-changer.

The AI that Acts as an Analyst – But Better

The power of AI lies in its ability to continuously analyze vast datasets much faster than any human can, flagging issues and opportunities in a portfolio in real-time. For example, AI can constantly scan market trends, asset price correlations, economic indicators, and alternative data (like satellite imagery or social media sentiment). By automatically detecting mispricing, concentration risks, or underperforming assets, it becomes a relentless analyst working 24/7.

Here's where privacy-preserving AI truly excels: not only can it diagnose problems, but it can propose actionable alternatives.

Consider a hypothetical situation where your AI system flags a portfolio imbalance (let’s say there’s an over-concentration in a particular sector). Rather than just pointing out the problem, the AI suggests rebalancing by increasing exposure to an underweight sector that shows improving fundamentals. It could further recommend reducing exposure to assets that have strong historical correlations to that sector, reducing overall portfolio risk.

Because of its ability to analyze complex datasets without bias, AI can spot correlations and patterns in market data that would be invisible to human analysts. It can even simulate different market scenarios, stress testing the portfolio and allowing hedge fund managers to optimize for both returns and downside protection.

Reducing Human Error: Data-Driven, Not Gut-Driven

The allure of AI isn’t just in its speed or data processing power. One of the most compelling benefits for hedge funds is reducing human error and bias. We’ve all seen cases where overconfidence, herding behavior, or emotional trading erodes performance. Hedge fund managers often rely on intuition, backed by years of experience, but even the best make costly mistakes in judgment.

AI, on the other hand, is purely data driven. It doesn’t suffer from the cognitive biases that can distort human decision-making. By integrating AI into the portfolio management process, hedge funds can minimize errors caused by gut feelings or emotional decision-making. AI doesn't have "skin in the game" in the same way a human does – its decisions are backed by pure statistical rigor. This next generation of AI is also being trained in a more structured way, allowing for repeatability, explainability, and efficiency in operations.

Privacy-preserving AI allows hedge funds to integrate proprietary datasets securely with external datasets (like public market data or economic reports) to generate insights that wouldn’t be possible through manual analysis. And, crucially, these insights remain protected from external parties.

Scenario Planning: Optimization for Any Market Condition

Hedge funds pride themselves on thriving in volatile market conditions. Yet, market volatility is inherently unpredictable. What if you could run simulations that show how your portfolio would perform under hundreds of different macroeconomic scenarios, market shocks, or asset-specific events?

Using AI-driven scenario planning, managers can stress-test portfolios under a variety of conditions, enabling them to optimize for different market environments. The ability to proactively adjust portfolios for potential future conditions is crucial in avoiding drawdowns, enhancing risk-adjusted returns, and ultimately beating the market.

Our privacy-protecting AI algorithms can also monitor real-time changes in volatility and liquidity conditions. When AI detects, for example, increased downside risk in an asset class, it can instantly recommend hedging strategies such as shifting exposure to non-correlated assets or increasing allocations to high-quality defensive stocks.

One case study from earlier in the year had our system identify 3 specific behaviors in the overall portfolio that, when removed, showed up to multiple percent better returns when modeled out over the year. All 3 behaviors were ones that, when identified, we thought to have been managed by the existing human processes, but were, in fact, not due to the siloed nature of the different portfolios across the organization.

Now imagine being able to interact with such a system through a LLM – you start to get the picture of how powerful this kind of portfolio optimization can be and why, in the not-too-distant future, it will be a requirement to be effective in the markets.

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A Collaborative Future: Humans and AI

To be clear, AI isn’t here to replace hedge fund managers: it’s here to augment them. The most successful funds will be the ones that integrate AI as a collaborative partner, not just a tool. AI does the heavy lifting of data crunching, risk flagging, and alternative suggestion, while human managers bring the domain expertise, intuition, and experience to make the final call.

Imagine an AI system that doesn’t just provide general suggestions, but instead customizes its recommendations based on the specific investment philosophy of a hedge fund. Whether you’re running a long/short equity strategy, a macro fund, or a quant-driven multi-asset portfolio, AI can adapt its optimization models to fit the fund's approach.

Conclusion: AI-Powered Portfolio Optimization is the Future

In this hyper-competitive landscape, privacy-protecting AI will be the key differentiator that separates top-performing hedge funds from the rest. The combination of data-driven insights, speed, and the protection of proprietary data gives managers an unprecedented edge.

This is no longer a far-off dream – it’s happening now. Hedge funds that embrace privacy-preserving AI will be able to unlock deeper insights into their portfolios, minimize human error, and make smarter, faster decisions. Those that don't? Well, they might just be left behind, grappling with yesterday’s technology in tomorrow’s markets.

As someone who understands both the technology and the markets, I can confidently say that this is the future of hedge fund management. The most successful investors will be those who learn how to leverage AI to optimize portfolios in ways that were never before possible while keeping their most valuable data safe. The tools are here. The question is, are you ready to use them?

This article was written by Sultan Meghji, CEO of Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2025 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

Enterprise technology has long been defined by a series of centralizing advancements and decentralizing innovations. Large firms, driven by capital growth, develop new devices and frameworks to create a more universal technological landscape. Inversely, disruptors work to extract value from those advancements, seeking to improve the availability and utility of these technologies to better serve individual users. This push and pull dynamic of innovation has led to massive leaps in technological advancement throughout the past few decades, creating systems that continue to influence our use of technology to this day.

Mainframes to PCs: Decentralizing Hardware

The first major centralizing innovation came in 1964, with IBM’s creation of the first modern mainframe. Prior to this, computing was fragmented and highly localized — every machine operated as a standalone system requiring its own bespoke programming. Because there were no commercial software development companies, there was a massive entry barrier, preventing many from accessing computer technology. Mainframes were a revolutionary step forward, enabling firms to store all their data and software on a centralized piece of hardware, accessible to any terminal connected to it. Enterprises could now have multiple users work with the same data simultaneously and had a simple path to deploying their proprietary software at scale.

Mainframes were not without their flaws, however. Their massive size and high cost made them impractical for use outside of large corporations, and their centralized nature posed massive concentration risks — if the mainframe went down, every terminal it hosted went down. As computing technology improved however, smaller processors were created that could run individual devices, leading to the first PCs. This innovation allowed individuals and small businesses to make use of computing power without a mainframe, creating entirely new use cases outside of commercial enterprises. As PCs grew in popularity, the hardware necessary to access computing power became increasingly decentralized, spreading the technology beyond its commercial roots.

Cloud Computing and the Democratization of Software

The advent of cloud computing helped recentralize the control of technology back into the hands of enterprises. It offered a flexible infrastructure, allowing computers to access data and software remotely over the internet for the first time. This opened new opportunities for enterprises to sell digital services and host computing resources on their own servers. As cloud computing infrastructures like Amazon Web Services (AWS) grew, so did the availability of computing technology. This consolidated control back into the hands of a select few tech giants, as individuals and small businesses were forced to rely on these platforms. This structure exposes cloud computing infrastructures to the same concentration risks as mainframes, exemplified by AWS’ outage just this week — a single configuration issue caused outages for thousands of services.

Despite these downsides, cloud computing was crucial in improving the accessibility of new software, leading to a myriad of products that prioritized the cloud as a means of accessing applications. Devices like the Palm Treo and the Blackberry offered users new tools in a convenient package. Apple’s launch of the App Store, alongside their iPhone3G, was the most influential of these developments. For the first time, users were no longer restricted to software produced by their phone manufacturer, gaining access to an expansive library of mobile-specific, third-party apps. This new decentralized ecosystem not only expanded the utility of the iPhone but also helped monetize third-party developers, further democratizing the availability and use of software as competitors were forced to implement similar frameworks to compete with the Apple.

AI: Flexibility vs. Specificity

This brings us to today, with the rise of AI defining technological innovation over the last few years. Currently, the market is dominated by established tech companies creating massive LLMs and generative AI models. These models have been used for every use case imaginable, as developers seek to centralize the use and output of AI software to their own environments. The massive amounts of data used to train these models help it perform a wide variety of tasks, but it rarely provides the context needed to produce effective, personalized solutions to users’ problems. They too are vulnerable to centralization risks, with countless organizations relying on the stability of a single firm’s infrastructure. Like the previously discussed innovations, these advancements offer great opportunity, but in their current form fall short of providing real value on an individual basis.

In reaction to this, many firms have pivoted to implementing smaller, highly specialized models built for more explicit functions. They are designed to understand and meet the specific needs of the user, trained on their data to avoid the generic output and hallucination issues large models face. Custom models apply the power of AI on a user-centric scale, opting for specificity over an idealized goal of limitless functionality. In the coming years, large corporations will realize the benefits of these solutions and be pushed into a more granular information economy — one based on buying or renting an individual’s private data to train specialist, local AI models that prioritize user interest above all else.

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From Localization to Personalization

The advancement of technology is defined by the ebb and flow of these opposing forces of innovation. So long as there are attempts to centralize technology and people’s use of it, disruptors will find ways to extract even greater individual value. Just as the mainframe led to PCs, and the cloud to the endless stream of applications we use daily, large artificial intelligence models will lead to local, custom models focused on providing personalized value for each user.

How soon the big players will begin creating this type of model is unknown, as they continue to hone their efforts on achieving AGI (artificial general intelligence). However, if past innovations provide any indication, the most influential developments are those that seek to adapt technology to meet the needs of each user. Controlling your own destiny has become increasingly difficult in the complex technological landscape of today, but it is not impossible. The path forward is paved by your own custom models running on your own infrastructure, not shackling yourself to the interdependent web of frameworks that large AI companies rely on.

This article was written by Sultan Meghji, CEO of Frontier Foundry. Visit his LinkedIn here.

To stay up to date with our work, visit our website, or follow us on LinkedIn, X, and Bluesky. To learn more about the services we offer, please visit our product page.

© 2025 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.

In Part 3 of this series, we examined how shifts in technology and market behavior are reshaping financial strategy within an increasingly unpredictable economic landscape. Today, we turn to the deeper structural question beneath that transformation — how we model an economy that no longer behaves as a single, coherent system, but as a set of overlapping and often conflicting regimes. If the old models can no longer explain the world we’re in, what tools will?

Last week, I received two messages that perfectly encapsulate the economic schizophrenia of our times. The first: a father of young children, bewildered by grocery prices that have doubled, spring break flights that have tripled, and prescription costs that have quintupled. The second: a seasoned investment banker who matter-of-factly explains that we no longer live in a market-based economy—we live in a liquidity-drugged simulation where equity markets exist primarily to generate return streams, not capitalize companies.

Both are right. And both point to why our traditional economic models are failing us spectacularly.

The Regime We’re Actually In

At the FDIC, we spent considerable time modeling systemic risks that existed outside conventional frameworks. Now, as CEO of Frontier Foundry, I’m applying those same principles to a much larger question: How do we model an economy where the observable data (market performance, employment statistics, inflation indices) tells a completely different story than lived experience (or how to analyze historic, terrible data that was not captured in a way that allows for logical analysis)?

The answer lies in understanding we’re not in one regime—we’re operating in multiple simultaneously. Traditional models are unable to account for this complexity, treating each regime as mutually exclusive and failing to recognize their dynamic and constantly changing applications to different markets. For example, semiconductors, as an industry, is behaving radically different than healthcare. The former is in, charitably, a bubble based on over-the-top capital expenditures, contributing to over 90% of GDP growth so far in 2025, while the latter is a money printing machine based on an older recurring revenue model with a thinner bottom line. According to traditional models, both exist in the same regime, yet are affected in vastly different ways.

The impacts of a multi-regime system act as hidden currents beneath the surface of today’s economy — forces we can sense but not directly observe. Hidden Markov Models (HMMs) offer a way to map those unseen dynamics, translating what appears to be chaos into probabilistic structure.

Unlike conventional economic models that assume linear relationships and observable states, HMMs acknowledge that the true economic “state” is hidden from direct observation. What we see — market prices, economic indicators, policy announcements, employment data — are merely emissions from underlying structural realities we can only infer probabilistically.

Three Hidden States, One Visible Chaos

Consider our current economic reality through an HMM lens with three primary hidden states:

State 1: The Liquidity Regime

• Characterized by asset inflation disconnected from productive capacity

• Observable emissions: Record stock prices, low unemployment, “transitory” inflation

• Probability transitions: High persistence, low exit probability without external shock

State 2: The Scarcity Regime

• Characterized by supply-demand imbalances in essential goods and services

• Observable emissions: Explosive costs in housing, healthcare, education, food

• Probability transitions: Self-reinforcing through hoarding behaviors and supply chain concentration

State 3: The Debasement Regime

• Characterized by currency manipulation to manage unsustainable debt loads

• Observable emissions: Divergence between official inflation metrics and purchasing power reality

• Probability transitions: Accelerating as debt-to-GDP ratios approach mathematical limits

HMMs don’t require us to pretend these states are mutually exclusive. They understand our economy operates in all three simultaneously, with different transition probabilities affecting different sectors and demographics.

The AI Multiplier Effect

Here’s where it gets interesting from an AI perspective. Artificial intelligence isn’t just another technological advancement—it’s a regime-change accelerator that’s amplifying the hidden state transitions in unpredictable ways.

AI is simultaneously:

• Deflationary in information processing, customer service, and certain manufacturing

• Inflationary in energy consumption, specialized hardware, and human expertise that can’t be automated

• Disruptive to labor markets in ways that don’t show up in employment statistics for 18-24 months

Traditional economic models treat technological change as an exogenous variable. HMMs allow us to model AI as both a state-transition catalyst and an emission-pattern disruptor. The result? We can begin to predict when AI advancement will shift us between the three regimes described above.

Geopolitical State Dependencies

The Ukraine conflict, U.S.-China tensions, and Middle East instability aren’t separate from our economic modeling—they’re state-dependent variables affecting transition probabilities between regimes.

In the Liquidity Regime, geopolitical instability drives “flight to quality” which reinforces asset bubbles.

In the Scarcity Regime, it creates supply shock multipliers.

In the Debasement Regime, it accelerates the weaponization of currency systems.

An HMM framework allows us to model these dependencies without assuming they’re linear or predictable. Instead, we can assign probability distributions to different geopolitical scenarios and their economic regime impacts.

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Building the Model: From Theory to Implementation

At Frontier Foundry, we’ve developed the HMM-based economic modeling tools that financial institutions and policy makers need to navigate regime uncertainty. The key components:

Observable Variables (Emissions):

• Traditional economic indicators (CPI, unemployment, GDP)

• Alternative data streams (satellite imagery, credit card spending, social media sentiment)

• Geopolitical event frequencies and intensities

• AI adoption and displacement metrics

Hidden States:

• Liquidity dependency levels

• Resource scarcity intensities

• Currency debasement velocities

• Geopolitical stability coefficients

Transition Probabilities:

• Policy intervention likelihoods

• External shock probabilities

• Technological disruption rates

• Social stability thresholds

The Path Forward

The father concerned about his family budget and the investment banker analyzing market distortions are both observing emissions from the same underlying reality. Traditional economics tells them they’re experiencing different phenomena. HMMs tell us they’re seeing different aspects of the same multi-regime system.

This isn’t academic theorizing. Banks need these models to manage systemic risk. Businesses need them for strategic planning. Individuals need them to make rational decisions about everything from career choices to retirement planning. Frontier Foundry understands that traditional models can no longer be relied upon in today’s complex environment, and we’re building the HMM solutions needed to navigate it. Those who fail to take advantage of these tools are at the mercy of an unpredictable economy, exposing themselves not only to financial risk, but potentially disastrous outcomes.

The era of pretending we live in a market-based economy with predictable relationships between inputs and outputs is over. The era of probabilistic regime modeling—enhanced by AI capabilities—is just beginning.

The question isn’t whether our economic reality will become more complex. It’s whether we’ll use the tools needed to navigate that complexity intelligently.

Or whether we’ll keep pretending that $1,800 spring break flights in a “low inflation” environment make sense.

This article was written by Sultan Meghji, CEO of Frontier Foundry. Visit his LinkedIn here.

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© 2025 Frontier Foundry. All rights reserved. This article and its contents are the exclusive intellectual property of Frontier Foundry. Unauthorized reproduction, distribution, republication, or derivative use — in whole or in part — without express written permission is strictly prohibited and may result in civil and criminal penalties under U.S. and international copyright law.

Frontier Foundry builds deterministic, secured AI for financial services, life sciences, and U.S. federal law enforcement. Our work spans AI governance aligned to the NIST AI RMF and the EU AI Act, post-quantum cryptographic agility, and privacy-first deployment patterns for organizations where getting the answer wrong carries regulatory, safety, or reputational consequences. Founded and led by Sultan Meghji — former inaugural Chief Innovation Officer of the FDIC.