AI, Data Centers, and the Next Big Correction: Will Growth Outpace Market Reality?

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AI, Data Centers, and the Next Big Correction: Will Growth Outpace Market Reality?

AI is being readily embraced by organizations, government, and individual enthusiasts for data aggregation, pattern recognition, data visualization, and co-creation of content. Given the headlines lately, AI is set to take over the world. And as an emerging, revolutionary technology with large potential impact and newfound user-friendliness, both large tech companies and small startups alike have raced to capitalize on potential growth. Hands down, this transformative technology has caused a wave of adoption, investment, and innovation around the world and across industries.

Naturally, when a technology or application accelerates quickly, the more risk-averse will be cautious and when it accelerates this quickly, a bubble might be forming. Even more bullish investors have ridden through too much tumult in the past few decades for their bank accounts to withstand another cataclysmic loss. More investment is pouring in (including at a federal level), stock valuations are all over the charts and not necessarily true to a ticker’s earnings, and the recent market fluctuations leave the entire ecosystem a little hesitant about buying into the hype too much.

The Nature of Bubbles and Some Potential Signals to Watch For

Economic bubbles occur when asset prices significantly exceed their intrinsic value, often fueled by speculative demand and irrational investment, leading to unsustainable market conditions. A bigger concern than just to digital infrastructure, bubbles can have far-reaching impacts on the entire market, as the initial distorted financial metrics encourage excessive lending and create systemic risk. The collapse of a bubble can trigger a chain reaction of financial distress, causing widespread economic instability and potentially leading to recessions, as seen in historical examples like the dot-com and housing bubbles.

Reasonable bubble indicators that have the market concerned include:

Overvaluation and Lack of Profit Generation: Tech giants are heavily invested in AI despite limited returns from the associated products. Likewise, many AI startups have achieved valuations far exceeding their earnings. This discrepancy between valuation and profitability is a classic sign of a bubble.
Hype vs. Reality: The AI hype cycle throughout the news has led to significant investments, with society torn about the potential and ethical claims regarding AI capabilities. Overstatements in the media often must be tempered with corrections in later expectation, but when hundreds of billions of dollars are at stake, it’s no small adjustment.
Diminishing Returns: Some experts suggest that large language models (LLMs) may not be as scalable as previously thought, leading to diminishing returns on investment in these technologies.

The Dot-Com Burst Saw Precisely This Happen

The dot-com bubble emerged in the late 1990s, fueled by the rapid growth of the internet and the establishment of numerous tech startups. This period saw a surge in demand for internet-based stocks, leading to high valuations that often exceeded the companies’ intrinsic value. The NASDAQ Composite index rose dramatically, increasing by 582% from January 1995 to March 2000, only to fall by 75% from March 2000 to October 2002.

The frenzy of buying internet-based stocks was overwhelming, with many companies lacking viable business models and focusing instead on metrics like website traffic. Venture capitalists and other investors poured money into these startups, often ignoring traditional financial metrics in favor of speculative growth potential. The media played a significant role in fueling this hype, encouraging investors to overlook caution and invest in risky tech stocks.

The bubble burst when capital began to dry up, leading to a market crash. By 2002, investor losses were estimated at around $5 trillion. Many tech companies that conducted IPOs during this era declared bankruptcy or were acquired by other companies. The collapse of the dotcom bubble resulted in massive layoffs in the technology sector and served as a cautionary tale about the dangers of speculative investing and overvaluation.

The aftermath of the dotcom bubble led to a more cautious approach to investing, with a renewed focus on fundamental analysis rather than speculative hype. Despite the devastating impact, it laid the groundwork for the modern tech industry, with companies like Amazon and Google surviving and thriving to become leaders in their fields.

Growth and Profitability

While AI as a technology has been around for decades, the advent of generative AI built on neural networks resulted in the release of ChatGPT. This launched a user-friendly chatbot that could interpret and then generate responses in milli-seconds that were more than just coherent, but informative, insightful, and intuitive. The potential of AI was on display for all the world to see and users of OpenAI’s system grew to 1 million users in five days and 100 million users in 2 months, the fastest adoption of a platform the world has ever seen. They recently have reached 400 million weekly active users.

The societal adoption makes sense, but what about the business application, where there is real money to be made? Other than for the reputed college kids writing term papers, AI’s value to an organization is its ability to analyze vast amounts of disorganized data, aggregate it all, and make complex decisions from it. Key industries like healthcare, computer science, cybersecurity, logistics, manufacturing, and content creation are all leading the shift and embracing the benefits of AI technology and there is no end in sight to the innovation available.

The efficiency gains and reduced operational costs to an organization are limited only by a user’s imagination for what queries to put to the test. But speaking openly, as someone who grew up in the power distribution world, peddling equipment that made utilities and industries more efficient and reduced OpEx as our core product benefits, I can tell you this isn’t an easy value proposition to market your products on, even when it is so tangibly evident as it is with AI, and the enterprise and B2B adoption is rolling out slower than the headlines might have us believing.

Simply stated, this technology is only profitable if there are paying customers and revenue growth that follow. Serious startup capital is being spent on applications of this technology that the market may not be ready to support. This does have the markings of a crash, but whether that crash will be a true bubble will depend on the speed, reach, and broader impact of that decline.

Economic Considerations

Herd mentality plays a significant role in the adoption of AI technologies. This phenomenon involves individuals following the crowd and making decisions based on the actions of others, rather than their own beliefs or analysis. In the context of AI, herd behavior is amplified by the widespread adoption of AI tools and the fear of missing out (FOMO) on potential benefits.

AI algorithms, trained on extensive datasets, can perpetuate this mentality by replicating existing trends and strategies, making them more appealing to a broader audience. As a result, the rapid adoption of AI technologies can lead to inflated expectations and valuations, similar to what was observed during the dotcom bubble, where speculative demand drove prices far beyond their intrinsic value.

The prices of hardware necessary for AI development and deployment are being driven up by several factors, including scarcity and increased demand. The rapid growth of AI applications has led to a surge in demand for GPUs and TPUs necessary for training models. This increased demand, coupled with supply chain constraints and geopolitical tensions affecting semiconductor production, has resulted in higher prices for these critical components.

Additionally, the concentration of manufacturing in a few regions exacerbates these supply chain issues, further contributing to price increases. As AI continues to expand across industries, the strain on hardware resources is likely to persist, maintaining upward pressure on prices.

Right now, investors and data center operators, alike, are attempting to chart the viability of the many parties and the likely winners of the AI arms race, and charting those sort of outcomes always brings different economic tools such as game theory to mind, where we have many players all vying for the same opportunities. The considerations of approaching this like a game are that we can complement our decisions by modeling interdependencies, ensuring strategies that achieve the most desirable outcomes.

This mathematical framework is frequently used for understanding interactions within an ecosystem, but is much more complicated than the well-known Nash equilibrium, whereby each participant strives to maximize their outcome, and equilibrium is achieved only when all players have reached this maximum, which is interdependent on the behaviors and actions of the other players. The Prisoner’s Dilemma is the well-known classic, but as applied in this sense, other studied “games” to consider are more applicable, especially those that result in a “winner takes all” outcome.

One of the challenges, however, is that new neocloud players are joining amidst an ongoing game, making this extremely difficult to mathematically chart. Nevertheless, it can be useful framework for isolated scenario modeling of strategies, predictive analytics, and decision mapping to anticipate outcomes.

For example, many AI startup companies may be bidding for the same hyperscale AI projects. As with a Prisoner’s Dilemma, there may be a first-mover advantage, but this is actually more like a game of Chicken. The first to pull out of the competition loses the crown title, but keeps their life; the one to stay in the match (if the other pulls out) earns both; or they defeat each other through psychological tactics whereby 1) neither succeed or 2) the result is mutually assured destruction when neither gives in.

The resulting sentiment is that in this arms race, one year from now only a handful of companies will have survived.

Therefore, investment is slowing down as investors are digging deeper into the cost of the technology, the feasibility of finding customers, and the timeline to revenue. “Show me the money,” is being heard across digital infrastructure, or rather, show me the path to monetization, the business case for your unique application of the technology and prospective customer. With limited winners and an excess of losers, it is hard to see investors placing financial bets across the board; they will be much more strategically selected than we saw in the dot-com days.

Ripples in the Ecosystem

Countering the bubble fear-mongers, it must be argued that the long-term outlook of AI and the underlying technology that fosters this innovation will have a lasting-impact. From the 40,000-foot view, I can’t imagine a fundamentally revolutionary technology causing a complete market burst, while businesses and individuals have already come to rely on various AI applications as essential tools for business.

Rather than a crash, natural economic adjustment may be more likely, though it must be said that market fluctuations have had greater swings of late and may be established as a norm that day tradesmen have to account for in their strategies, while longer-term investors are willing to ride these waves out. That is, if they ever lock in on a winner they choose to financially back. Readjustments are just part of the game.

As an asset category, we need to consider the full ecosystem and consider the market corrections we’ve begun to see play out:

Competitive Market Growth: An example of this is easily seen when we consider the DeepSeek launch recently, a Chinese product competitive to ChatGPT that supposedly boasted lower costs and energy usage. The U.S. tech index lost $1 trillion in value that day. Much of that was quickly recovered. Additionally, individual stocks may contribute to some fluctuations, but there was some concern about a burst looming, because a single announcement should never have seen the swing that resulted from this announcement. In general, we need to stop letting short-term sentiment and fear impact us to this extent and trust what we know to be true about the technology adoption. The wake-up call was heard nonetheless across the market, and we should expect to see much more reticence to large investments that present a high risk profile.
Lease Terms: The data center market has been a bit of a seller’s market for a few years now; those with land and power need simply say the word and they could lock in 15 year lease terms. That’s changing a bit of late and as we’ve seen, some hyperscalers are even pulling back lease terms to under 10 years, some around 7-8 years. AI leases are even less secure with many neocloud startups aiming for 5-7 year lease terms. This doesn’t offer the same confidence to an investor or to a data center provider compared to a longer-term commit and let’s not forget, these cash constrained startups cannot afford to give this perception. As we learned from the real estate bubble, inability to pay the rent quite literally could become a trigger for another burst.
Equipment Obsolescence: Another factor to consider is the high cost of investment in hardware. Ultimately with growth, price per unit will come down. Then as new models are released by the various manufacturers, the previous renditions will become obsolete, and suddenly entire generations of hardware may lose value. As long as the neocloud provider has established a decent customer base to generate revenue, or a hyperscaler has deep enough pockets to fund an equipment refresh, this is no concern. But it’s a bitter pill to swallow when it happens and is not always a blow that can be recovered from, since it hinges on the model already demonstrating success. Some question has arisen whether there will be a second-hand market for GPUs. With the investment that goes into the purchase up front, it would be a struggle to imagine that there won’t be, but a viable use case has yet to emerge; it’s simply too new to discern. It would likely be pennies on the dollar, but better than nothing. Perhaps by being repurposed for smaller outfits that lease to single-use enterprises will provide a niche market where equipment finds new utility, even if not as lucrative as the initial use.
Equipment Failure: Beginning to be discussed openly, GPUs have a high failure rate due to component failures, memory issues, and driver problems. This unreliability can lead to costly downtime and data loss, impacting the efficiency and reliability of AI operations. As AI applications become more complex and widespread, the need for robust and reliable GPU infrastructure grows. The consequences of these failures ripple through the market, affecting not only the deployment timelines and operational costs but will also make companies more hesitant to adopt and scale their use of the technology. Moreover, the scarcity of GPUs, exacerbated by supply chain disruptions and export restrictions, further complicates the situation, pushing companies to explore alternative solutions like GPU-as-a-Service (GPUaaS) to mitigate these risks.
Stock Valuations: Nvidia, the leading supplier of GPUs essential for training AI models, has become one of the most valuable publicly listed companies, with a valuation exceeding $3 trillion. As the gold standard for GPUs, Nvidia’s stock performance significantly influences the broader market, particularly tech-heavy indices like the S&P 500. Given its substantial market capitalization, Nvidia’s stock makes up a considerable portion of major indexes, meaning that any large market adjustment could have far-reaching effects on the entire tech sector. This concentration of market influence in a few key stocks, including Nvidia, leaves investors vulnerable unless they are well diversified. The valuation of AI-related stocks, such as OpenAI potentially reaching a $300 billion valuation despite never being profitable, raises questions about sustainability. The recent stock market surge has been largely driven by the “Magnificent Seven” companies—Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla—which are heavily invested in AI and have collectively seen significant growth. These companies account for over half of the S&P 500’s total return in 2024, with annualized appreciation rates exceeding 20% over the past five years, and Nvidia leading with over 90% growth. The sustainability of such high valuations and growth rates is uncertain, and any correction could have profound implications for the entire market.
Colocation Markets: The Magnificent Seven mentioned include the hyperscale market, which naturally leads the majority of AI investment, but we must consider impacts to other operators. Over the past two years, many hyperscalers paused to reevaluate their facility designs, then turned to colocation providers for extended support. We have now seen this infrastructure begin to crumble, with Microsoft cancelling leases based on concerns of oversupply and reduced capacity needs for AI. Those contracted deployments will have caused a financial loss for the colocation providers who planned to construct them. This may have been our biggest market test yet, as it eerily echoes the dot-com triggers that began the burst. The market did react and it’s unclear whether we’re out of the woods just yet. Aside from hyperscale AI deployments inside a colocation data center, neocloud companies present another viable AI tenant opportunity, but even they are all bidding for the same hyperscale contracts. When the hyperscalers get nervous, this puts the entire industry at great concern about long-term viability.

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But the concern for enterprise IT leaders is whether Nvidia’s financial stakes in UALink consortium members could influence the development of an open standard specifically designed to compete with Nvidia’s proprietary technology and to give enterprises more choices in the datacenter. Organizations planning major AI infrastructure investments view such open standards as critical to avoiding vendor lock-in and maintaining competitive pricing. “This does put more pressure on UALink since Intel is also a member and also took investment from Nvidia,” Sag said. UALink and Synopsys’s critical role UALink represents the industry’s most significant effort to prevent vendor lock-in for AI infrastructure. The consortium ratified its UALink 200G 1.0 Specification in April, defining an open standard for connecting up to 1,024 AI accelerators within computing pods at 200 Gbps per lane — directly competing with Nvidia’s NVLink for scale-up applications. Synopsys plays a critical role. The company joined UALink’s board in January and in December announced the industry’s first UALink design components, enabling chip designers to build UALink-compatible accelerators. Analysts flag governance concerns Gaurav Gupta, VP analyst at Gartner, acknowledged the tension. “The Nvidia-Synopsys deal does raise questions around the future of UALink as Synopsys is a key partner of the consortium and holds critical IP for UALink, which competes with Nvidia’s proprietary NVLink,” he said. Sanchit Vir Gogia, chief analyst at Greyhound Research, sees deeper structural concerns. “Synopsys is not a peripheral player in this standard; it is the primary supplier of UALink IP and a board member within the UALink Consortium,” he said. “Nvidia’s entry into Synopsys’ shareholder structure risks contaminating that neutrality.”

Cooling crisis at CME: A wakeup call for modern infrastructure governance

Organizations should reassess redundancy However, he pointed out, “the deeper concern is that CME had a secondary data center ready to take the load, yet the failover threshold was set too high, and the activation sequence remained manually gated. The decision to wait for the cooling issue to self-correct rather than trigger the backup site immediately revealed a governance model that had not evolved to keep pace with the operational tempo of modern markets.” Thermal failures, he said, “do not unfold on the timelines assumed in traditional disaster recovery playbooks. They escalate within minutes and demand automated responses that do not depend on human certainty about whether a facility will recover in time.” Matt Kimball, VP and principal analyst at Moor Insights & Strategy, said that to some degree what happened in Aurora highlights an issue that may arise on occasion: “the communications gap that can exist between IT executives and data center operators. Think of ‘rack in versus rack out’ mindsets.” Often, he said, the operational elements of that data center environment, such as cooling, power, fire hazards, physical security, and so forth, fall outside the realm of an IT executive focused on delivering IT services to the business. “And even if they don’t fall outside the realm, these elements are certainly not a primary focus,” he noted. “This was certainly true when I was living in the IT world.” Additionally, said Kimball, “this highlights the need for organizations to reassess redundancy and resilience in a new light. Again, in IT, we tend to focus on resilience and redundancy at the app, server, and workload layers. Maybe even cluster level. But as we continue to place more and more of a premium on data, and the terms ‘business critical’ or ‘mission critical’ have real relevance, we have to zoom out

Microsoft loses two senior AI infrastructure leaders as data center pressures mount

Microsoft did not immediately respond to a request for comment. Microsoft’s constraints Analysts say the twin departures mark a significant setback for Microsoft at a critical moment in the AI data center race, with pressure mounting from both OpenAI’s model demands and Google’s infrastructure scale. “Losing some of the best professionals working on this challenge could set Microsoft back,” said Neil Shah, partner and co-founder at Counterpoint Research. “Solving the energy wall is not trivial, and there may have been friction or strategic differences that contributed to their decision to move on, especially if they saw an opportunity to make a broader impact and do so more lucratively at a company like Nvidia.” Even so, Microsoft has the depth and ecosystem strength to continue doubling down on AI data centers, said Prabhu Ram, VP for industry research at Cybermedia Research. According to Sanchit Gogia, chief analyst at Greyhound Research, the departures come at a sensitive moment because Microsoft is trying to expand its AI infrastructure faster than physical constraints allow. “The executives who have left were central to GPU cluster design, data center engineering, energy procurement, and the experimental power and cooling approaches Microsoft has been pursuing to support dense AI workloads,” Gogia said. “Their exit coincides with pressures the company has already acknowledged publicly. GPUs are arriving faster than the company can energize the facilities that will house them, and power availability has overtaken chip availability as the real bottleneck.”

What is Edge AI? When the cloud isn’t close enough

Many edge devices can periodically send summarized or selected inference output data back to a central system for model retraining or refinement. That feedback loop helps the model improve over time while still keeping most decisions local. And to run efficiently on constrained edge hardware, the AI model is often pre-processed by techniques such as quantization (which reduces precision), pruning (which removes redundant parameters), or knowledge distillation (which trains a smaller model to mimic a larger one). These optimizations reduce the model’s memory, compute, and power demands so it can run more easily on an edge device. What technologies make edge AI possible? The concept of the “edge” always assumes that edge devices are less computationally powerful than data centers and cloud platforms. While that remains true, overall improvements in computational hardware have made today’s edge devices much more capable than those designed just a few years ago. In fact, a whole host of technological developments have come together to make edge AI a reality. Specialized hardware acceleration. Edge devices now ship with dedicated AI-accelerators (NPUs, TPUs, GPU cores) and system-on-chip units tailored for on-device inference. For example, companies like Arm have integrated AI-acceleration libraries into standard frameworks so models can run efficiently on Arm-based CPUs. Connectivity and data architecture. Edge AI often depends on durable, low-latency links (e.g., 5G, WiFi 6, LPWAN) and architectures that move compute closer to data. Merging edge nodes, gateways, and local servers means less reliance on distant clouds. And technologies like Kubernetes can provide a consistent management plane from the data center to remote locations. Deployment, orchestration, and model lifecycle tooling. Edge AI deployments must support model-update delivery, device and fleet monitoring, versioning, rollback and secure inference — especially when orchestrated across hundreds or thousands of locations. VMware, for instance, is offering traffic management

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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