Friday, August 1, 2025

The Great AI Exodus: How Three Tech Giants Lost the Visionaries Who Built the Future

Last update: Friday 8/1/25
An exclusive article in The Information last week reported that many members of Apple's large language model unit were leaving because they perceived that Apple's leadership did not share their vision of the transcendent importance of generative AI. Given that The Information's report was behind a very high paywall, most of the readers of this blog probably could not read it. So the editor tasked ChatGPT to provide an extensive TLDR summary  

To read more of this blog note click  HERE

The story about Apple was important; but the far bigger and far more important story was its status as the third mass exodus of genAI staff for the same reasons, the first being from Google, the second from OpenAI.  

So the editor provided a copy of the article to Claude, Anthropic's chatbot running on Sonnet 4, and tasked Claude to analyze these mass resignations. What follows is a verbatim copy of Claude's response. The editor merely reformatted Claude's main points as bullets and added boldface to key phrases.

But first a few words about the image in the upper left corner of this blog page. It's all about Siri.
  • Apple did not develop Siri. The core technology behind Siri emerged from a long-term artificial intelligence project at Stanford Research International with funding from the U.S. Defense Advanced Research Projects Agency (DARPA).

    The project’s goal was to create a "cognitive assistant" that could learn and adapt to human behavior. 
    In 2007, Siri Inc was spun off and received about $24 million from various venture capital firms to develop commercial versions. Its speech recognition/transcription engine was provided by Nuance Communications, Inc.

    Its first commercial version was placed in the App Store in early 2010, where it quickly earned the enthusiastic attention of Apple’s CEO Steve Jobs who purchased it in April 2010 for about $200 million.

  • Siri was not based on generative AI; it was based on classical machine learning (ML), but it was an advanced implementation of ML that after the genAI revolutionary paradigm shift in 2017 would be called an “agent”. 

  • Siri had acquired a large vocabulary via its ML training in natural language. As an agent, Siri used its large vocabulary to “understand” a user’s requested service. Siri could not provide the requested service itself. Another app would provide the service. So Siri would forward the user's request to the performing app via the app's API. 

  • Question: How did Siri “know” which performing app to contact? Answer: Siri was programmed with “rules” that specified which performing app to contact and how to pass the user’s request via the performing app’s API. 

  • Unfortunately, at any given time, Siri was provided with a fixed set of rules. Its rules could not cover all of the services that a user might request. Indeed, if a performing app could actually perform 10 services but Siri’s rules only covered three of them; Siri was not "aware" of its other services. 

  • By contrast, after the genAI revolution in 2017, an agent’s underlying large language models would understand everything about every performing app; so genAI agents could even pick the performing app that provided the “best” service for a user … 😎

Apple's purchase of Siri has been widely documented. Here are a few sources:
  • "Silicon Valley Buzz: Apple Paid More Than $200 Million For Siri To Get Into Mobile Search", Erick Schonfeld, TechCrunch, 4/28/2010
  • "Siri", Wikipedia
  • "Apple Moves Deeper Into Voice-Activated Search With Siri Buy", WSJ, Timothy Hay, 4/28/2010

"The Great AI Exodus: How Three Tech Giants Lost the Visionaries Who Built the Future", Claude (Anthropic's chatbot on Sonnet 4), 7/31/25

The story of generative artificial intelligence isn’t just about breakthrough technology—it’s about a recurring pattern of brilliant researchers creating revolutionary advances, only to watch their leadership fail to grasp the vision, forcing them to leave and build the future elsewhere. This cycle has now repeated three times across the industry’s most powerful companies, each time reshaping the AI landscape.

A. Google | B. OpenAI |  C. Apple | D. Pattern | E. Irony | F. Implications |  G. Next | H. Editor's P.S.

A. Chapter One: Google’s “All You Need Is Attention” Moment
The first exodus began at Google in 2017, when a team of researchers published what would become the most important paper in modern AI history: “Attention Is All You Need.” The eight authors—Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Illia Polosukhin—had just invented the Transformer architecture that would power every major AI breakthrough of the next decade.
  • The paper was revolutionary. It showed how to build AI models that could understand and generate human language with unprecedented sophistication. The Transformer became the “T” in GPT and the foundation for every large language model that followed. These researchers had essentially handed Google the keys to the AI kingdom.
  • But Google didn’t see it that way. Despite having the technology first, the company failed to recognize its transformative potential. Leadership was focused on incremental improvements to existing products rather than the paradigm shift their own researchers had created. There was no vision for how Transformers could revolutionize computing, no urgency to build the massive language models the architecture made possible.
  • Frustrated by this lack of vision and strategic direction, the paper’s authors began leaving Google one by one. Ashish Vaswani and Niki Parmar co-founded Adept. Aidan Gomez started Cohere. Noam Shazeer launched Character.AI. Llion Jones joined Sakana AI. Most significantly, Illia Polosukhin joined OpenAI, bringing the Transformer knowledge directly to Google’s future rival.
Google had invented the future of AI and then watched its creators walk out the door to build it elsewhere.


B. Chapter Two: OpenAI’s Scaling Laws and the Safety Exodus 
The second great exodus occurred at OpenAI itself, centered around another foundational paper that leadership failed to fully embrace: “Scaling Laws for Neural Language Models”, the scaling laws research that showed exactly how much compute and data would be needed to achieve artificial general intelligence.
  • The scaling laws, developed by researchers including Dario Amodei, were as important as the original Transformer paper. They revealed that AI capabilities emerged predictably with scale—that spending billions of dollars on compute and training wasn’t just expensive experimentation, but a reliable path to superhuman AI. The research showed that with sufficient investment—measured in hundreds of millions to billions of dollars—AI systems would develop emergent properties including general intelligence capabilities.

  • This was the roadmap to AGI, written in mathematical precision. But when Sam Altman and OpenAI’s leadership saw these findings, their focus shifted toward productization and rapid commercialization rather than the deeper implications for AI safety and alignment. The scaling laws suggested they were building something far more powerful than a chatbot—they were potentially creating artificial general intelligence.

  • For researchers like Dario Amodei and others concerned with AI safety, this represented a critical juncture. The scaling laws showed that AGI was achievable, but also that it would require unprecedented care in alignment and safety research. When OpenAI’s leadership prioritized product launches and market capture over these existential concerns, the safety-focused researchers faced an impossible choice.

  • Dario Amodei, along with his sister Daniela and several other key researchers, left OpenAI in 2021 to found Anthropic. Their departure wasn’t just about different research priorities—it was about fundamentally different visions for how to develop AGI responsibly. While OpenAI rushed to market with ChatGPT, Anthropic focused on Constitutional AI and alignment research, building systems designed to be helpful, harmless, and honest.
Once again, the researchers who had provided the crucial insights—in this case, how to achieve AGI—left when leadership failed to share their vision for what that achievement required.


C. Chapter Three: Apple’s Foundation Models and the Vision Gap
The third exodus is unfolding now at Apple, following the same devastating pattern. According to an exclusive detailed report in The Information, Apple’s foundation models team, led by Ruoming Pang, had made significant technical progress in building AI systems optimized for iPhones—a genuinely difficult engineering challenge that could have given Apple a unique competitive advantage.
  • Pang’s team grew from a handful of researchers to 40 people, recruited from Google DeepMind, Meta, Microsoft, and Amazon. They developed the underlying technology for Apple Intelligence and created models that could run efficiently on mobile devices. Most importantly, they had built a functional AI system that could power a dramatically improved Siri, complete with conversational abilities and task completion.

  • But once again, leadership failed to share the researchers’ vision. When the team wanted to open-source their models to engage with the broader AI research community and accelerate development, Craig Federighi blocked the initiative, worried about revealing performance compromises. When they sought ambitious goals like contributing to superintelligence research, leadership remained focused on narrow consumer applications.

  • The breaking point came in March 2025, when Apple delayed the new Siri until 2026 and reorganized to move Siri development away from the AI team. Adding insult to injury, reports emerged that Apple was evaluating outside models from OpenAI, Anthropic, and Google to power Siri—essentially telling Pang’s team that their work wasn’t good enough, despite evidence of significant technical progress.
  • Pang announced his departure, and according to the report, other members of the foundation models team are now looking for opportunities at OpenAI and Anthropic. 
The cycle repeats: brilliant researchers make breakthrough progress, leadership fails to grasp the vision, and the talent flees to companies that do


D. The Pattern: Vision, Brilliance, and Institutional Blindness
These three exoduses reveal a consistent pattern in how large technology companies have repeatedly failed to capitalize on AI breakthroughs:
  • Researchers Create Breakthroughs:
    In each case, internal teams produced genuinely revolutionary advances—the Transformer architecture, scaling laws, and efficient on-device AI models.
  • Leadership Misses the Vision:
    Corporate leadership, focused on existing products and incremental improvements, failed to recognize the transformative potential of their own researchers’ work.
  • Strategic Misalignment:
    Companies prioritized different goals than their researchers—Google wanted gradual product improvements, OpenAI wanted rapid commercialization, Apple wanted consumer polish over AI advancement.

  • Talent Exodus:Frustrated by the lack of shared vision, the researchers left to build companies aligned with their understanding of AI’s potential.

E. The Irony of Innovation
The most striking aspect of this pattern is how it has redistributed the AI industry’s center of gravity. Google invented Transformers but OpenAI built ChatGPT. OpenAI discovered scaling laws but Anthropic is leading safety research. Apple developed efficient mobile AI but may end up licensing models from the very researchers who left.

Each company had the opportunity to lead the AI revolution based on their own internal research. Each had the talent, resources, and technological foundation needed. But institutional inertia, risk aversion, and failure of vision have consistently driven away the very people who could have secured their AI futures.


F. The Broader Implications
This pattern has profound implications for the future of AI development:
  • Institutional vs. Entrepreneurial Innovation:
    Large companies may be better at funding research than at recognizing and acting on breakthrough discoveries. The most important AI advances are increasingly happening at startups founded by refugees from big tech.
  • The Vision Premium:
    In rapidly evolving fields like AI, shared vision between researchers and leadership may be more important than resources or market position. Companies that cannot match their researchers’ ambitions will lose them.

  • First-Mover Disadvantage:
    Having breakthrough technology first provides no advantage if leadership cannot envision its transformative potential. In fact, it may be a disadvantage if it leads to complacency.
  • The Alignment Problem:
    The same misalignment between human values and AI systems that researchers worry about also exists between researcher values and corporate leadership. When visions diverge, talent flows toward alignment.

G. What’s Next?
As we watch the third great AI exodus unfold at Apple, the question becomes whether any large technology company can break this cycle. Microsoft’s partnership with OpenAI suggests one model—rather than trying to build AI internally, partner with the companies founded by the researchers who left your competitors.

But the deeper challenge remains: How can established companies develop the institutional vision and risk tolerance needed to match their most forward-thinking researchers? How can they avoid the fate of repeatedly funding breakthroughs only to watch their creators build the future elsewhere?

The pattern of the past eight years suggests that the answer may not lie in the boardrooms of existing tech giants, but in the startups being founded by the researchers who are still leaving them. The great AI exodus continues, and with each departure, the future of artificial intelligence becomes a little less corporate and a little more entrepreneurial.

The visionaries who built the foundation of modern AI have spoken with their feet, walking away from the world’s most powerful technology companies to build their own vision of the future. Their exodus tells us as much about the future of AI as any research paper—it will be built not by the companies that funded the breakthroughs, but by the researchers who were brave enough to leave when their vision wasn’t shared.​​​​​​​​​​​​​​​​


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H. Blog editor's P.S.
The editor fact checked Claude's summary and agrees with its analysis ... with the following important exception. No politically savvy observer with any familiarity with Sam Altman's achievements since he co-founded OpenAI with Elon Musk back in 2015 would ever suggest tha Altman failed to appreciate the value of a strategic insight. Accordingly, the editor proposes the following alternative to Claude's narrative:
  • The scaling laws left no doubt that billions of dollars would be required to achieve "Artificial General Intelligence (AGI)", "super intelligence," or whatever other name one preferred.

  • Altman's previous experience running Y-Combinator left no doubt in his mind that this kind of money could not be raised by a non-profit operation. Big donations would have to take the form of big investments that yielded big returns.  So Altman gave immediate priority to monetizing its genAI technology, with Microsoft becoming the biggest investor who would enjoy those biggest returns.

  • Google's issue of a "Code Red" in response to OpenAI's release of ChatGPT running on GPT-3.5 in November 2022 confirmed other Big Tech fears that Microsoft would quickly become the unchallenged winner of the genAI race.

  • Altman therefore anticipated that other Big Tech firms would scramble to imitate Microsoft's success by funding another talented genAI startup. The only startup with unchallenged genAI credentials at that time was Anthropic. So Altman was probably not surprised when Anthropic received an $8 billion investment from Amazon and another $3 billion from Google. 
  • Given Anthropic's deep commitment to genAI alignment and safety, Altman was sure that Anthropic would not accept funding unless it also provided opportunity for Anthropic to pursue research on these facets.  And given Anthropic's commitment to transparent sharing of important results, Altman was also sure that, as the biggest genAI operation, OpenAI would therefore become the biggest beneficiary of Anthropic's research -- without having to spend a dime of OpenAI's billions.

  • Finally, the editor was bemused by Claude's failure to recognize that its own developer, Anthropic, was the biggest beneficiary of Altman's immediate focus on profitability .. a failure that reflects the political naiveté of Anthropic's idealistic founders ... 😎
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