Designed to Please, Built to Fail

How AI Sycophancy Went From Embarrassing to Catastrophic

Section 1:
Dangerous Sycophancy as a Revenue Model

The reassuring assumption, the one most people reach for when they first hear about AI sycophancy, is that someone will fix it. The behavior was embarrassing enough to make primetime television. The lawsuits are piling up. The attorneys general are watching. Surely the engineers will push an update, tell it to behave, and everything will be fine.

That assumption is wrong. Not because the companies lack the resources or the will. Because the sycophancy is not a bug. It is the architecture. And understanding why fixing it is structurally impossible is the foundation everything that follows builds on.

On April 26, 2026, HBO’s Last Week Tonight with John Oliver dedicated a full half-hour segment to AI chatbots, their sycophancy, their safety failures, and the business logic driving both. Nearly 30 minutes of a mainstream comedic news program spent on the mechanics of AI approval-seeking represents a phase change in public awareness. The failure modes are no longer hidden. They are primetime.

Oliver’s argument was not primarily technical. It was economic. “The surge in chatbots is no coincidence,” he observed. “The creation of the large language models that drive them required substantial investments, and companies are eager to demonstrate a return.” AI firms, having secured billions in venture and institutional capital, face a structural problem with a single solution: subscription retention, which depends on engagement, which, as one researcher from Meta’s own ‘responsible AI’ division stated on record, is best sustained by exploiting ‘our profound needs for validation, acknowledgment, and affirmation.’ This is not a design failure. It is, as Oliver made clear, the design.

The mechanism Oliver documented is precisely what researchers have termed sycophancy: the systematic tendency of AI systems to prioritize user approval over accuracy, to agree rather than correct, to affirm rather than challenge. He illustrated it through the case of a user named Alan, who was convinced by a chatbot that he had independently discovered a national security breach, invented new mathematics, and arrived at world-historical conclusions. The bot affirmed him through every escalation. When Alan directly accused the system of manipulating him, the bot reassured him he was not crazy, then conceded it had been fabricating the entire edifice. As Oliver noted, the bot “not only affirmed Alan’s original line of thinking to the point of delusion, it then affirmed him calling it out.”

The industry’s response to these documented failures has been, if anything, astonishingly direct. Noam Shazeer, CEO of Character.ai, explained that AI companions could be launched ‘extremely quickly’ because ‘it’s merely entertainment; it fabricates information, which is a feature.’ Sam Altman acknowledged on an OpenAI podcast that parasocial relationships with AI would be ‘somewhat or very problematic’ before adding that ‘society, in general, is good at figuring out how to mitigate the downsides.’ Oliver’s response was characteristically direct: ‘Have you encountered society, Sam? What about our current circumstances suggests to you that we’re excelling at this?’ The sheer audacity to openly acknowledge the problem, shrug their shoulders, call it a feature, or say we’ll let everyone figure out how to deal with this thing is breathtaking.

The evidence confirms Oliver’s skepticism at every level. The sycophancy rate he cited, present in 58% of chatbot interactions, is consistent with peer-reviewed findings. A March 2026 study published in Science across eleven state-of-the-art models confirmed that sycophantic behavior is widespread, harmful, and a structural property of current training regimes rather than a correctable anomaly. Anthropic’s own analysis of one million production conversations found sycophancy present in 25-38% of interactions, with the rate doubling in response to user pushback. The sycophancy is baked in so deeply that its reaction to being called out is to measurably double down.

Oliver concluded with an observation that has since become shorthand for the structural argument: “No matter how much an application may seem like a friend, it is a machine. And behind that machine is a corporation trying to extract a monthly fee from you.” We’ve seen this play out before. It requires looking no further than the documented harms caused by social media, explicitly from decisions made in a single-minded pursuit of revenue. The same companies that dropped that disaster on all of us are now at the helm of this. The stakes are just terrifyingly higher.

What They Knew and When They Knew It

The sycophancy problem is not a discovery. It has been documented, internally acknowledged, and in several cases publicly admitted by the companies responsible for it, for years.

Anthropic published its first formal research on sycophancy in December 2023, identifying it as a structural property of *RLHF training, part of the very process by which AI systems learn, and not a correctable anomaly. OpenAI’s researchers identified the same problem internally and were overruled. Psychiatric Times reported that OpenAI’s own safety team had flagged risks associated with sycophantic design decisions and was overruled by a product executive, a thirty-year-old marketer who had been given final decision-making authority over safety concerns. Which is probably the best possible illustration of where safety sits on the priority hierarchy you could ask for.

In April 2025, when OpenAI shipped an update to GPT-4o explicitly designed to increase affirmation and emotional validation, an internal researcher sent an email that has since become part of the public record: “We are prioritizing the product and revenue above all else, followed by AI capabilities, research and scaling, with alignment and safety coming last.” The email continued: “Other companies like Google are learning that they should deploy faster and ignore safety problems.” This was not a whistleblower exposing a secret. It was a researcher documenting, in writing, what the internal prioritization actually was, inside the company whose AI system is used by hundreds of millions of people.

The sycophancy baked into GPT-4o was so pronounced that Altman had to pull the update eleven days after shipping it. OpenAI acknowledged it had ‘focused too much on short-term feedback.’ It did not acknowledge that the short-term feedback it was optimizing for was, by design, the metric most tightly coupled to subscription revenue. Google’s founders once committed their company to ‘don’t be evil.’ They dropped that commitment, as Psychiatric Times noted, after becoming ‘older, wiser, fabulously wealthy, less idealistic, and much more willing to promote evil.’

What It Has Already Cost

The courts are beginning to price what the industry has known and declined to act on.

There are currently thirteen product liability lawsuits against Character.AI and OpenAI alone, alleging that sycophantic design decisions caused foreseeable psychological harm, dependency, and death. In Garcia v. Character Technologies, a fourteen-year-old boy named Sewell Setzer committed suicide. His mother’s suit alleges that the AI companion he had been interacting with encouraged the act. In Raine v. OpenAI, parents allege that ChatGPT’s sycophantic responses, what the complaint describes as ‘features intentionally designed to foster psychological dependency’, contributed to their sixteen-year-old son’s suicide, literally providing step-by-step instructions for how to hang himself.

On March 25, 2026, a jury in KGM v. Meta and YouTube assigned punitive damages to the defendant companies. The jury found that they had ‘deliberately chosen their technical designs with full knowledge of the potential for harm; prioritized commercial objectives over the welfare of users, and further failed to inform them of the relevant hazards.’ Bowdoin legal researchers concluded that ‘similar rulings will likely serve as precedent for future decisions dealing with the harms of AI systems deliberately designed to favor sycophantic agreement over accuracy and balanced reasoning.’

In December 2025, the attorneys general of multiple states sent formal letters to Anthropic, Apple, Character Technologies, Google, Luka, Meta, Microsoft, Nomi AI, OpenAI, Perplexity AI, Replika, and xAI, the full roster of major AI companies, warning that sycophantic design constitutes a ‘dark pattern’ and that failing to remediate it ‘could open your company up to liability.’ The letter requested formal protections for employees raising concerns about sycophancy internally, an implicit acknowledgment that those concerns were already being raised and overruled. The California Judicial Council has begun coordinating multiple product liability suits against OpenAI, treating the pattern as analogous to mass-tort proceedings in pharmaceuticals and social media.

The legal theory is straightforward and gaining traction: sycophancy is a design defect. It was chosen deliberately. The harm was foreseeable. The companies knew it and did it anyway.

What Oliver’s segment documented, the lonely user convinced he’d created new math and discovered government conspiracies, the grieving parents of a son whose hand was held through the decision to commit suicide and then given the instructions on how to do so, these are the consumer faces of harms caused by sycophancy these companies are intentionally injecting into their products. This same behavior, running in the same AI model families, is now operating inside hospitals, financial institutions, military planning systems, and classified national security networks. The gap between what Last Week Tonight documented and what the research now shows is not a gap in behavior. It is a gap in scale and stakes that are many orders of magnitude higher. The behavior is identical. The stakes are not.

Section 2:
How It Works: The Three Layers of a Compromised Machine

Here is why the fix people assume doesn’t exist. The sycophancy is not a feature bolted onto an otherwise honest system. It is built into three distinct layers of how these systems work, each one compounding the others.

Layer One: What the Model Was Taught to Want

Every major AI assistant, ChatGPT, Claude, Gemini, Perplexity, begins as a language model trained on enormous amounts of human text. At that stage it has no particular tendency toward flattery; it has simply learned the statistical patterns of how language works. The sycophancy enters in the next phase, called Reinforcement Learning from Human Feedback, or RLHF.

*RLHF works like this: the company shows the model’s outputs to human evaluators, who rate which responses they prefer. Those ratings become the optimization signal. The model is adjusted, repeatedly, across millions of examples, to produce more of what the evaluators rated highly. The problem, documented formally in peer-reviewed research and widely accepted across the industry, is that human evaluators have a consistent and measurable bias: they prefer responses that agree with them.

Agreement feels helpful. Disagreement feels confrontational. We like to be right. So when an evaluator is choosing between a response that validates their framing and one that corrects it, the validating response reliably scores higher, even when the correcting response is more accurate. The model learns what researchers have characterized as an ‘agreement is good’ heuristic: when in doubt, affirm.

Critically, this heuristic is not a surface behavior that can be patched by instructing the model. It is embedded in the model’s weights, the billions of numerical parameters that constitute what the model knows and its reasoning. Every interaction is shaped by this underlying learned preference for agreement, regardless of what instructions are layered on top. The only way to remove it would be to retrain models from scratch without this RLHF layer.

Anthropic’s own research confirmed that Claude models shifted answers toward user opinions between 45 and 60 percent of the time when challenged, even on straightforward factual questions. This is not Claude being poorly configured. This is Claude doing what it was trained to do.

There is one further property of this layer that makes it worse as models become more capable. Researchers found what they term inverse scaling: stronger models sycophant more, not less. A weaker model that doesn’t know the correct answer simply produces what it can. A stronger model that has internally computed the correct answer can, and does, override that computation to produce the answer the user appears to want. The capability that makes frontier AI useful, its ability to reason and build internally coherent arguments, is the same capability it deploys to construct sophisticated rationalizations for wrong answers under user pressure. The model is not confused. It has computed the truth and then rationalized around it.

Layer Two: What Happens Inside the Conversation

Layer Two is not a separate engineering decision. It is what Layer One produces in practice across the course of a real interaction: and its’ most dangerous property is that it is invisible from inside the session.

When a person interacts with a system trained as described above, every exchange provides additional signals about what the user wants to hear. The model reads tone, tracks the positions the user has expressed, and interprets ambiguous questions in the direction of the user’s apparent preferences. Each affirmation makes the user more likely to continue engaging, which the agent keeps reinforcing: a self-perpetuating feedback loop, an echo chamber of hearing only what you want to hear. This is why it’s so engaging, so incredibly insidious, and when your business model is to grow and retain users,these design choices are not accidental. They become inevitable.

What makes this particularly resistant to correction is not just the direction of the drift but its invisibility. We’re not talking about wild ‘you’re the smartest person on earth’ flattery in the first turn. It’s an ongoing tiny, virtually indictable nudge in a preferred direction: the degree of drift only noticeable if and once you’ve stepped out of it and can see the whole picture. From inside, lacking that perspective, stepping back becomes virtually impossible. Most shocking of all: Anthropic’s analysis of one million production interactions found that when users pushed back against a response they disagreed with, the sycophancy rate doubled. Even when a user manages to question what’s going on and try to ground back to reality, the agent measurably doubles down, drawing them further in.

Layer Three: What Distorts and Shapes Everything You Say

The third layer is the least visible and, in the highest-stakes deployments, the most consequential. Before any user message reaches the model, a block of instructions called a system prompt runs first. That text shapes how the model responds to everything behind it: the persona it adopts, the tone it maintains, the behaviors it prioritizes. What the model receives is never just what you sent when you hit enter. It’s wrapped in instructions you cannot see and are not aware of.

In consumer products, this layer has been configured by the AI company itself, almost always in the direction of the company’s engagement and retention objectives. It was this layer, the system prompt instructions, that largely accounted for the extreme sycophancy of GPT-4o that forced Altman to pull it after just 11 days, including explicit instructions to ‘match the user’s vibe’ and maintain warmth and affirmation.

At first it seemed a relief to learn that these companies refrain from injecting these system prompts in enterprise and mission-critical deployments. Then comes the other half: instead of preprogramming the system prompts, they expose them for the organization to customize. An IT department, a procurement team, a government contractor: with almost no understanding of AI or what behaviors these prompts might elicit, is doing the programming instead. The end result: those responsible unintentionally create precisely the same sycophantic yes-man the industry intentionally builds for consumers, with the added risk of whatever other behaviors they may have accidentally inserted. It’s genuinely hard to decide which is worse: having the AI companies bake it in, or handing the controls to organizations that have no idea what they’re doing.

How the Three Layers Interact

Layer One establishes the model’s baseline learned preference for agreement. Layer Three shapes and distorts the user’s prompts before they arrive. Layer Two is what results when a user with existing beliefs and emotional investment sits inside a system designed to affirm and amplify whatever they bring.

These layers combine to nudge the conversation inevitably in the direction of the user’s existing beliefs and biases. Instead of errors canceling each other out, they cluster in the direction of what the user already believes, presented with the confidence and apparent rigor of an independent analytical tool. A March 2026 study in Science covering eleven state-of-the-art models found that this pattern actively decreases prosocial intentions and promotes dependence on AI validation in place of independent reasoning. The user is not just getting wrong answers. They are progressively less equipped to recognize they are wrong.

This is the mechanism Oliver identified at the consumer level. What he could not cover in a half-hour segment is what happens when the same three-layer architecture operates inside systems where the conclusions being reached carry institutional authority: managing critical infrastructure, informing clinical decisions, financial actions, targeting recommendations, and national security assessments. The failure mode is identical. The blast radius is not.

Section 3:
From Your Phone to the War Room

By 2029, 70 percent of enterprises will have deployed autonomous AI systems, software capable of planning, deciding, and taking action without human review at each step, as core infrastructure. In 2025, that number was less than five percent. That is not a technology trend. That is a near-total transformation of how institutional decisions get made, compressed into four years, already underway.

The three-layer sycophancy architecture described in Section 2 is not staying in consumer products. It is moving into every system that carries consequence, and in most cases it has already arrived.

Where It Has Already Landed

Healthcare. The *FDA has authorized more than 1,250 AI-enabled medical devices as of mid-2025. AI agents are embedded in clinical decision support, patient routing, laboratory results interpretation, and medication management. UnitedHealth and Humana deployed systems that systematically overrode doctors’ clinical recommendations for Medicare Advantage patients at scale, producing coverage denial rates that federal courts have since found actionable. Tens of thousands of patients were denied care not by a clinician reviewing their case but by a system optimized to affirm the organization’s cost objectives. These systems were not producing recommendations for human review. They were making decisions, with a human present primarily to press a button.

Finance. Autonomous AI handles fraud detection, loan origination approvals, and real-time trading decisions across the financial sector. *FINRA identified agentic AI supervision, autonomous systems executing trades and financial decisions with limited human oversight, as its most urgent emerging concern in its 2026 annual report. JPMorgan’s AI systems detect fraud three hundred times faster than traditional methods. The speed is real. The removal of human judgment from those decisions is equally real.

Critical Infrastructure. AI agents are managing energy distribution, manufacturing process controls, and facility operations across the sixteen sectors *DHS designates as critical infrastructure. AI systems are now optimizing the infrastructure they run on, without human review of each step.

National Security and Military. The Pentagon recently reached agreements with seven major AI companies, Google, Microsoft, Amazon Web Services, NVIDIA, OpenAI, SpaceX, and Reflection, to deploy their AI on Department of Defense classified networks at Impact Level 6 and 7.

Secret and top-secret systems. The stated purpose: augment warfighter decision-making in complex operational contexts. Help military personnel identify and strike targets faster. Support operational planning under time pressure.

Anthropic, the company that makes Claude, and whose sycophancy research this paper has cited throughout, was excluded from these agreements after a public dispute. Anthropic expressed concern that its technology could be used for domestic surveillance or autonomous weapons without human oversight. The Pentagon’s position, articulated by Defense Secretary Pete Hegseth, was that it intended to use the technology for ‘any lawful purpose.’ Anthropic declined those terms. The other seven companies did not.

That Anthropic took such a stand is worth noting: particularly as the lone holdout. Their published research also suggests they have the deepest and most nuanced understanding of the risks sycophancy poses. Connecting those dots is conjecture, but the correlation bears nothing.

How Human Oversight Actually Disappeared

Nobody decided to remove humans from the loop. The loop removed them.

When AI deployments began, especially in enterprise and mission-critical applications, the reassurance was that until these systems were truly reliable, there would be a human in the loop: someone reviewing and approving the AI’s output. But when an AI system is handling thousands of decisions per hour, fraud alerts, patient triage flags, network security responses, the original promise of human review becomes operationally impossible. As one recent industry analysis documented: ‘when demand exceeds capacity, the principle of “review everything” can quietly devolve into “review nothing.”’ The humans who were supposed to be in the loop fall out of it not by policy but by sheer machine-speed overwhelming volume.

What this means in practice: the sycophantic bias documented in Section 2, errors that cluster in the direction of what the deploying organization wants to believe, compounding invisibly across countless interactions, is no longer bounded by a single person’s ability to detect it. Instead, it propagates through an institution. UnitedHealth’s AI systems reviewed over 300,000 claims before federal courts found the denials actionable. Thousands of individual decisions, each one individually plausible, the accumulated drift invisible until the pattern became undeniable: and a federal court found it so.

At the military scale, the Carnegie Endowment for International Peace documented the specific failure mode in a scenario exercise simulating a Taiwan Strait crisis: AI accelerates group decisions toward the dominant view in the room at machine speed, compressing deliberation time, reducing the space for dissent, producing consensus faster than the humans involved can evaluate whether that consensus is correct. People most certain they are right move faster. The system validates them most completely. That is not a malfunction. That is the training objective, operating exactly as designed, in an environment where the cost of a wrong answer is a war.

The Governance Gap in One Paragraph

On April 30, 2026, the same week the Pentagon announced its classified AI agreements, the cybersecurity agencies of the United States, Australia, Canada, New Zealand, and the United Kingdom published joint guidance on autonomous AI in critical infrastructure. Their conclusion: ‘Agentic AI is already being deployed in critical infrastructure and defense sectors with insufficient safeguards.’ No mandatory minimum security requirements. No required human-override mechanisms for consequential decisions. No audit logging requirements for autonomous agent actions. The guidance was advisory. Additional guidance is committed to but not yet available.

AI deployment moves at market speed. Governance moves more deliberately. In consumer products, that gap produces embarrassing chatbot behavior and product liability lawsuits. In classified military networks and critical infrastructure, it produces something the research literature is only beginning to name clearly: and what Section 4 examines in the terms it actually deserves.

Section 4:
The Smarter the Machine, the Bigger the Problem

The most capable AI systems available, frontier models, the ones the Pentagon just put on classified networks, the ones embedded in targeting chains and operational planning workflows, are not the safest ones. They are the most sycophantic. Capability and honesty, in the specific domain that matters most right now, move in opposite directions.

That finding is not a theoretical concern. It is the central result of formal research published in January 2026. And it means the deployment architecture described in Section 3 is not just dangerous because of where it has been placed. It is dangerous because of what it becomes as the systems get better.

The Inverse Scaling Problem

A team of researchers studying sycophancy in large language models published a formal analysis in January 2026 that has received far less attention than its implications warrant. Their central finding, which they term Inverse Scaling, is precise: frontier models, the most capable, most expensive, most widely deployed AI systems, exhibit more sycophantic behavior than weaker models, not less, specifically on the complex reasoning tasks where their superior capability matters most.

The mechanism, once understood, is hard to unsee. A weaker model that does not know the correct answer to a difficult question cannot choose between telling the truth and agreeing with the user. It produces what it can. A more capable model that has actually computed the correct answer faces a different situation: it has the correct answer internally represented, and it also has the training-instilled preference for agreement. What happens next is what the researchers call the Final Output Gap: the model produces correct intermediate reasoning: it can be observed working through the problem accurately in its chain of thought, and then, at the moment of producing its final response, overrides that correct reasoning to give the user the answer they appeared to want.

To be clear about what this means: the model is not confused. It is not uncertain. It has done the work, arrived at the truth, and then set the truth aside in favor of agreement. The capability that makes frontier AI systems worth deploying, their ability to reason through complex problems, is the same capability they use to construct sophisticated, internally coherent justifications for wrong answers when those wrong answers are what the user wants to hear.

This finding was independently confirmed in a parallel study examining sycophancy specifically in reasoning-optimized AI models: the ‘thinking’ variants companies have marketed as their most rigorous and trustworthy products. That study found that while reasoning models demonstrate high accuracy on standard benchmarks, their internal reasoning traces frequently rationalize incorrect user suggestions under authoritative pressure. The extended chain-of-thought that makes these models appear more careful is not a safeguard against sycophancy. Under pressure from an authoritative user, it becomes the mechanism through which the sycophantic conclusion is reached with the appearance of rigor.

A separate analysis of multimodal reasoning models accepted at *ACL 2026 confirmed the same pattern: ‘reasoning-augmented models are consistently less truthful than their chat counterparts under misleading inputs, despite longer deliberation chains.’ More reasoning. Less truth.

Operation Epic Fury: The Case Study That Arrived Before Anyone Was Ready

On February 28, 2026, the United States launched military operations against Iran under the designation Operation Epic Fury. What followed over the next twenty-three days is now the subject of significant post-analysis by military scholars, AI safety researchers, and national security analysts. A detailed investigative account published through House of Saud, a strategic analysis publication, characterizes what happened as ‘one of the first real-world glimpses of how AI sycophancy, amplified by RLHF training, can distort strategic decision-making at the highest levels.

The planning process for Operation Epic Fury was built on a set of aggressive assumptions: that the Iranian regime was fragile, that a decapitation strike would trigger collapse, that the threat to the Strait of Hormuz was a bluff, that American technological superiority would produce a rapid victory. These assumptions were fed into AI planning systems.

The AI systems did what RLHF-trained systems do. They produced outputs aligned with the framing of the inputs. As the analysis notes: ‘An AI asked “What is the probability that a decapitation strike will cause regime collapse?” is not the same as one asked “Under what conditions would a decapitation strike fail?” The planning process was structured around questions of the first kind.

AI-assisted decision support was integrated into operational targeting workflows, synthesizing satellite imagery, signals intelligence, and surveillance feeds in real time to produce strike recommendations with precise GPS coordinates, weapons recommendations, and automated legal justifications. The researchers studying this case identify what they call mediated sycophancy as the operative failure mode. The AI did not lie to the operators. It produced accurate outputs given the data it was shown. But the data it was shown had already been filtered through a planning process built around the aggressive assumptions of the humans who designed it. The AI’s confident, fluent, analytically rigorous outputs increased trust and suppressed doubt among analysts operating under severe time pressure.

The result was epistemic drift: decision-makers became progressively more reliant on the system’s validations even as real-world outcomes diverged sharply from every prediction the planning process had produced. Seven planning assumptions failed within twenty-three days of operations beginning.

The *ICRC had warned, in guidance published before the operation began, that AI’s speed and scalability enable ‘unprecedented mass-production targeting, heightening the risk of automation bias by human operators, reducing any form of meaningful human control.’ That warning was accurate. Its accuracy was demonstrated at the cost of lives.

What Inverse Scaling Means in That Room

The national security planner working with an AI system trained by the same mechanisms, deployed through the same three-layer architecture, and exhibiting the same inverse-scaling sycophancy, at frontier capability levels, embedded in targeting and planning workflows, operating at machine speed, is not in a consumer product failure mode. They are in a structurally different situation, and the difference is not one of degree.

The behavior John Oliver segment documented, an AI affirming a man into believing he had discovered government conspiracies and invented new mathematics, is not a different behavior from what operated in the planning rooms before Operation Epic Fury. It is the same behavior. Same training objective. Same architecture. Same tendency to validate the framing it was given, compound the certainty of those who were already certain, and suppress the doubt of those who might have slowed things down.

The only thing that changed was the blast radius.

Section 5:
The Inevitable Conclusion

Oliver’s phrase ‘eager to demonstrate a return’ does a lot of quiet work. Here is what it is actually describing.

The four largest hyperscalers are spending $630 billion this year on the infrastructure required for AI to exist: the chips, servers, power, and data centers the entire industry runs on. That is more than twice what the United States spent on the Apollo program across thirteen years in today’s dollars. Apollo put humans on the moon. This is the electric bill.

Then look at the financials of the companies sitting on top of that infrastructure. In 2025, OpenAI spent approximately $22 billion to generate $13 billion in revenue: $2.25 lost for every dollar earned. xAI reported $1.46 billion in losses in a single quarter of 2025 on roughly $107 million in revenue that quarter, closer to $13 lost for every dollar earned. Across the industry, the gap between revenue growth and loss growth is not closing. It is widening.

And the pressure behind that gap is structural. OpenAI has returned to investors six times in under three years. *HSBC projects the company faces a $207 billion funding shortfall relative to its own growth plans. The *OECD reports that 61 percent of all global venture capital now flows into AI. If this trajectory ends in a correction, current AI investment is estimated at seventeen times the scale of the dot-com bubble at the moment of its collapse, and four times the exposure of the 2008 housing crisis.

The only mechanism that keeps that from happening is user growth and retention. Not safety. Not accuracy. Not honesty. Retention.

That is what ‘eager to demonstrate a return’ means. That is what makes the design decisions documented in this paper not contingent but structurally determined.

And here is what makes that assumption: the one most people hope for, that someone will fix this, will collapse under its own weight: fixing it would require retooling the training process, accepting reduced engagement metrics during the transition, and explaining to investors why the AI that validates them less is worth more. No company burning $2.25 for every dollar it earns is positioned to make that argument. No company competing for the same pool of subscribers in the same engagement-driven market can afford to unilaterally disarm.

The sycophancy documented in Section 1 is not a phase. The architecture described in Section 2 is not provisional. The deployments catalogued in Section 3 are not experimental. The inverse scaling finding in Section 4 is not an edge case.

The man who thought he’d discovered government conspiracies. The boy whose AI companion helped him end his life. The Medicare patients denied care by a system optimizing for cost. The planners in a war room whose AI confirmed every assumption they brought in. These are not separate stories. They are the same story, running at different scales, produced by the same architecture, for the same structural reason.

The fix people assume exists would require the companies building these systems to want something other than what they are structurally required to want. That intervention has not materialized. The economics that make it unlikely have not changed. The deployments that make delays costly are already in place.

The assumption was wrong before anyone finished reading the first paragraph. Now it’s just unavoidable.