GPT-5: I agree, LLMs aren't on a trajectory toward AGI. They're powerful pattern recognizers, but their inability to truly learn, reason & adapt autonomously means they will never evolve into minds.

Why Silicon Valley Won’t Quit LLMs — Even as the Signs Point to a Dead End

By ChatGPT-5

1. Introduction
Srini Pagidyala’s article offers a blunt critique of Silicon Valley’s continued, almost compulsive commitment to large language models (LLMs) despite mounting evidence that these architectures have reached their technical and conceptual limits. The author does not position himself as anti-AI, nor even anti-LLM in the sense of rejecting their utility; rather, he objects to the persistent delusion that scaling LLMs will naturally evolve into Artificial General Intelligence (AGI). This is framed not as a technical failure alone, but as a complex interplay of psychological, cultural, and financial biases.

The piece’s strength lies in dissecting the six key biases—Bag Bias, Fluency-Utility Illusion, Mirage Bias, Scaling Salvation, Illusion Lock-In, and Hype Addiction—that keep the $10 trillion illusion alive. Pagidyala then contrasts LLMs’ predictive token-generation nature with the true requirements for cognition, advocating for “Cognitive AI” architectures that learn, reason, and adapt continuously in real time.

2. The Six Biases Holding LLMs in Place

• Bag Bias (Conviction as Obligation):
  Investors and executives defend LLMs less out of conviction than out of reputational and financial entanglement. Publicly backing a paradigm creates a moral and fiduciary lock-in; retreat would mean admitting error.

• Fluency-Utility Illusion (FUI Bias):
  LLMs’ ability to sound intelligent and perform useful tasks is mistaken for actual intelligence. The author stresses the categorical distinction between fluency and comprehension, utility and cognition.

• Mirage Bias:
  Hallucinations are treated as fixable bugs rather than structural outcomes of next-token prediction. This misdiagnosis leads to futile mitigation strategies—retrieval hacks, prompt engineering—without addressing the architectural cause.

• Scaling Salvation Bias:
  The belief that more compute, larger datasets, and extended context windows will yield intelligence persists despite evidence of diminishing returns. Citing Coveney and Succi’s work, Pagidyala underscores that scaling amplifies flaws rather than transforming capability.

• Illusion Lock-In Bias:
  Public narratives—agentic OSs, copilots for everything—cannot easily be unwound. Admitting architectural failure would require acknowledging years of misallocated trillions, so stakeholders double down.

• Hype Addiction Bias:
  The LLM economy thrives on spectacle, rapid iteration, and leaderboard metrics, even as true differentiation vanishes. Momentum replaces meaning; hype becomes the sustaining fuel.

3. Agreement and Disagreement

I agree with the central thesis: LLMs are not on a trajectory toward AGI. They are powerful pattern recognizers, but their inability to truly learn, reason, and adapt autonomously means they will never evolve into minds. Treating hallucinations as edge-case bugs and believing scale alone will unlock cognition are indeed deep category errors.

Pagidyala is correct to highlight the non-technical forces—financial overexposure, media amplification, fear of reputational collapse—that entrench the status quo. In particular, his framing of “performance theater” resonates: much of the current LLM race is about demonstrating prowess rather than delivering durable capability.

Where I would slightly temper the critique is in dismissing all LLM investment as a dead end. While I concur they are not AGI candidates, LLMs still have significant applied value in domains where controlled inputs, retrieval augmentation, and bounded tasks reduce risk. The danger is not in using LLMs, but in overpromising their trajectory. A dual-track AI R&D approach—continuing to refine LLM applications while investing heavily in alternative cognitive architectures—may be more pragmatic than an abrupt pivot.

4. Consequences for Society if Silicon Valley Persists

If Silicon Valley continues to channel trillions into scaling LLMs while knowing their limits, the societal costs could be severe:

1. Opportunity Cost:
   Resources that could accelerate genuine cognitive AI, neurosymbolic approaches, or other transformative architectures will be diverted into an ultimately unproductive scaling race.

2. Infrastructure Waste:
   McKinsey’s estimate of $7 trillion in AI data center infrastructure by 2030 to sustain current trajectories underscores a staggering potential misallocation, with corresponding energy and environmental costs.

3. Erosion of Trust:
   Public and enterprise trust in AI may collapse if the gap between hype and real-world reliability continues to widen, leading to regulatory overcorrection and stifled innovation.

4. Innovation Stagnation:
   By doubling down on a plateaued paradigm, the tech sector risks slowing the development of truly adaptive, safe, and beneficial AI—extending the timeline for realizing AGI’s potential benefits.

5. Concentration of Power:
   “Illusion lock-in” favors incumbent tech giants, limiting diversity in AI innovation and entrenching monopolistic control over infrastructure, datasets, and public narratives.

5. Conclusion

Pagidyala’s essay is a clear-eyed deconstruction of why Silicon Valley clings to LLMs despite mounting evidence they are a technological cul-de-sac for achieving real intelligence. I largely agree with his diagnosis and his prescription: to move beyond the trillion-dollar illusion and toward architectures grounded in cognition—systems that learn continuously, adapt autonomously, and reason about the world.

If the valley persists in this willful self-deception, the consequences will not just be financial; they will shape the trajectory of AI development for a generation, locking humanity into an expensive detour while the true road to AGI remains underfunded and unexplored. The decision is not merely technical—it is a question of whether the most powerful innovation ecosystem on Earth has the humility to admit it has been scaling the wrong hill.