In May 2023, Geoffrey Hinton, the "Godfather of AI," did something remarkable. After a decade at Google, he quit. Not for a better offer or a startup opportunity, but to warn the world about the very technology he helped create. "I console myself with the normal excuse," he told the New York Times, "if I hadn't done it, somebody else would have."

This moment encapsulates the paradox at the heart of the Large Language Model revolution: the creators are simultaneously the prophets and the Cassandras, the builders and the warners, the accelerators and the brakes. Understanding who these players are, how they relate to each other, and what games they're playing is essential to grasping where this technology is headed.

The Architects: When Gods Leave Olympus

The story of modern AI cannot be told without three names: Geoffrey Hinton, Yoshua Bengio, and Yann LeCun. These aren't just researchers; they're the intellectual architects of deep learning, sharing the 2018 Turing Award for work that made today's AI boom possible.

By May 2023, their trajectories had diverged in fascinating ways. Hinton's departure from Google wasn't just a retirement; it was a manifesto. He wanted to speak freely about AI risks without corporate constraints. The man who helped invent backpropagation, the mathematical foundation of neural networks, now worried his life's work might enable humanity's downfall.

Bengio took a different path. Named Knight of the Legion of Honour by France and joining the UN's scientific advisory council, he positioned himself as the bridge between research and policy. His approach was surgical: support specific legislation like California's SB 1047, lead international safety reports, maintain credibility in both camps.

LeCun, meanwhile, remained at Meta as Chief AI Scientist, becoming the optimist's champion. While his former collaborators warned of existential risk, LeCun argued these fears were overblown. His position wasn't mere contrarianism; it reflected Meta's strategic bet that open source AI would democratize the technology and prevent any single entity from gaining too much power.

The split among the "godfathers" reflects a deeper schism in the AI community. It's not just about optimism versus pessimism; it's about fundamentally different visions of how this technology should develop and who should control it.

Then there's Ilya Sutskever, the younger generation's standard bearer. As OpenAI's co founder and chief scientist, he bridged the academic and commercial worlds. His receipt of the Guardian Award alongside Hinton in 2023 symbolized a passing of the torch, from the generation that invented the tools to the generation deploying them at scale.

The Labs: From Non Profit Dreams to Billion Dollar Reality

The research lab landscape of 2023 tells a story of idealism meeting capitalism, and capitalism winning.

OpenAI: The $11 Billion Metamorphosis

OpenAI's journey from non profit to Microsoft backed juggernaut is Silicon Valley's most consequential pivot. Founded in 2015 with a mission to ensure AGI benefits all humanity, by 2023 it had raised $11.3 billion and become Microsoft's de facto AI division.

The transformation wasn't just financial; it was philosophical. The organization that once promised to open source its research now charged for API access to GPT 4. The non profit structure remained, technically overseeing a capped profit subsidiary, but the cap was set at 100x returns for investors. That's not a cap; it's a moonshot.

Microsoft's $13 billion investment wasn't just funding; it was a strategic acquisition in all but name. Microsoft got exclusive cloud rights, GPT integration across its products, and 49% of profits. OpenAI got the resources to compete in the increasingly expensive AI arms race. The deal structure was brilliant: Microsoft could claim it was supporting AI safety through a non profit, while effectively owning the commercial rights to the most advanced AI systems.

Anthropic: The $40 Billion Safety Play

If OpenAI was the prodigal child, Anthropic positioned itself as the responsible alternative. Founded by former OpenAI researchers who left over safety concerns, Anthropic raised $1.5 billion by May 2023, with valuations reaching $40 billion by year end.

The company's Constitutional AI approach wasn't just marketing; it represented a fundamentally different philosophy. Rather than relying solely on human feedback, Anthropic's models were trained to follow a set of principles, a "constitution" for AI behavior. This appealed to enterprises worried about unpredictable AI outputs and regulators concerned about AI alignment.

The funding structure was equally strategic. By taking investments from both Google ($2 billion) and Amazon ($4 billion), Anthropic avoided the exclusive partnership trap that bound OpenAI to Microsoft. This gave them leverage and optionality, crucial advantages in a rapidly evolving market.

DeepMind: The Sleeping Giant Awakens

Google's DeepMind had all the ingredients for dominance: top researchers, unlimited computing resources, and integration with the world's largest internet company. Yet by May 2023, they seemed perpetually behind. ChatGPT had captured the public imagination while Google scrambled to respond with Bard.

The announcement of PaLM 2 at Google I/O represented more than a product launch; it was Google's declaration that the search giant wouldn't cede the AI future without a fight. The parallel development of Gemini suggested Google's strategy: throw everything at the wall and see what sticks.

But DeepMind's real contribution wasn't products; it was infrastructure. Their work on MLCommons benchmarks and safety evaluations created the testing frameworks the entire industry would use. This was classic Google: if you can't dominate the market, define the standards.

The Enterprise Players: Cohere and AI21

While OpenAI and Anthropic fought for headlines, Cohere and AI21 Labs quietly built the boring but lucrative enterprise business. Cohere's $435 million funding round wasn't for building AGI; it was for helping Fortune 500 companies integrate LLMs into their workflows.

This represented a bet that the real money wouldn't be in consumer chatbots but in enterprise transformation. Every company needed AI, but most couldn't build it themselves. Cohere and AI21 positioned themselves as the arms dealers in the AI revolution, providing the tools without taking sides in the larger battles.

The Platform Wars: When Titans Collide

The Big Tech response to the LLM revolution revealed more about corporate strategy than any McKinsey report ever could.

Microsoft: The Fast Follower Wins

Microsoft's OpenAI partnership was CEO Satya Nadella's masterpiece. For $13 billion, Microsoft got something money usually can't buy: a two year head start in the AI race. While Google debated, Meta open sourced, and Amazon waited, Microsoft shipped.

The integration was swift and comprehensive. Bing got ChatGPT. Office got Copilot. Azure got exclusive OpenAI hosting rights. Every Microsoft product became an AI product, and every AI product drove Azure consumption. The strategy was elegant: use OpenAI's technology to enhance existing products while building Azure into the indispensable AI infrastructure.

Google: The Innovator's Dilemma Personified

Google faced the classic innovator's dilemma. They invented the transformer architecture that powered all LLMs. They had more AI researchers than anyone. Yet they were paralyzed by the risk to their search monopoly.

Bard's rushed launch showed the tension. It wasn't as good as ChatGPT, but it had to exist. Google couldn't afford to be absent from the conversation, even if participation might accelerate search's disruption. The $500 million Anthropic investment hedged their bets, ensuring Google Cloud had a competitive offering regardless of which models won.

Meta: The Open Source Gambit

Meta's decision to open source LLaMA was either brilliant or desperate, depending on your perspective. Unable to compete with OpenAI's products or Microsoft's distribution, Meta chose to flip the table.

Zuckerberg's Linux analogy was apt. Just as Linux commoditized operating systems, open source LLMs could commoditize AI models. If models became free, the competition would shift to applications and distribution, areas where Meta's three billion users gave them an advantage.

The strategy had another benefit: it positioned Meta as the good guy. While OpenAI and Google hoarded their models, Meta shared theirs with the world. This was particularly clever given Meta's reputation challenges; AI philanthropy was good PR.

Amazon: The Infrastructure Play

Amazon's approach was characteristically Amazonian: focus on infrastructure and let others fight over models. Bedrock wasn't about picking winners; it was about being the platform where all models could compete.

The $4 billion Anthropic investment secured a flagship model for AWS, but it was non exclusive. Amazon wanted to be the Switzerland of AI, neutral territory where everyone could build. This matched their cloud strategy: maximum optionality for customers, maximum lock in through infrastructure.

Apple: The Silent Treatment

Apple's AI strategy in 2023 was notable for its absence. While competitors made grand announcements, Apple said nothing. This wasn't neglect; it was strategy. Apple's focus on on device AI and privacy meant they needed different solutions than cloud based competitors.

The silence also reflected Apple's product philosophy: announce nothing until you have something extraordinary to ship. While others rushed half baked products to market, Apple waited. The question wasn't whether Apple would enter the AI race, but whether they'd waited too long.

The Insurgents: Unicorns in the Making

The startup ecosystem around LLMs in 2023 resembled the early internet: too much money chasing too few ideas, but with a handful of genuine breakthroughs.

Inflection AI: The $4 Billion Personality

Inflection AI's $1.3 billion raise at a $4 billion valuation was remarkable for what it wasn't building. Pi, their personal assistant, wasn't trying to be AGI or revolutionize search. It was trying to be a friend.

This focused approach attracted Microsoft and Nvidia as investors. The bet was that emotional intelligence, not raw capability, would drive consumer adoption. Pi's conversational style, more therapeutic than transactional, suggested a future where AI relationships might matter as much as AI capabilities.

Character.AI: The Billion Dollar Playground

Character.AI's billion dollar valuation was built on a simple insight: people want to talk to characters, not assistants. By letting users create and interact with AI personalities, they turned LLMs into entertainment.

Google's $3 billion licensing deal validated the approach. This wasn't about building better models; it was about finding better use cases. Character.AI proved that consumer AI didn't need to be useful if it was engaging enough.

Stability AI: The Open Source Crusader

Stability AI's approach was radically different: give everything away and figure out the business model later. Their release of Stable Diffusion democratized image generation, forcing Midjourney and DALL E to compete with free.

The $100 million funding suggested investors believed in the Linux model for AI. If Stability could become the default open source AI platform, monetization would follow. Services, support, and enterprise features could generate revenue even if the core models remained free.

Hugging Face: The GitHub of AI

Hugging Face's $4.5 billion valuation made them the most valuable pure platform play. They weren't building models; they were building the infrastructure for everyone else's models.

The investor list read like a who's who of tech: Google, Amazon, Nvidia, Salesforce, AMD, Intel, IBM, Qualcomm. Everyone needed Hugging Face to succeed because everyone needed a neutral platform for model distribution. This was the rare startup that competitors could all support because it threatened none of them directly.

Adept AI: The Action Hero

Adept's focus on action, not just generation, represented a crucial evolution. Their models didn't just answer questions; they used software, filling out forms, clicking buttons, navigating interfaces.

The $1 billion valuation reflected the market's recognition that AI needed to do, not just say. If Adept could crack reliable AI agents, they'd unlock trillions in economic value by automating knowledge work.

The Money Game: Following the Capital

The funding patterns of 2023 revealed the market's bets about AI's future.

The $50 Billion Gold Rush

Global AI startup funding reached nearly $50 billion in 2023, growing 9% while overall VC funding fell 38%. This divergence wasn't just optimism; it was fear. VCs who missed the internet boom wouldn't miss the AI revolution.

The concentration was extreme. OpenAI alone raised more than most countries' entire startup ecosystems. The top ten AI startups captured over 80% of funding. This wasn't a rising tide lifting all boats; it was a tsunami lifting a few yachts.

The Business Model Question

Despite the massive funding, sustainable business models remained elusive. OpenAI's API revenue was growing but still far from justifying an $80 billion valuation. Anthropic had impressive technology but limited commercialization. Most startups had impressive demos but no clear path to profitability.

The dirty secret was that most AI startups were losing money on every query. The computational costs were enormous, and prices were falling as competition increased. The bet was on scale and efficiency improvements, but the timeline remained uncertain.

Enterprise vs Consumer

The market was split on where value would accrue. Enterprise focused startups like Cohere argued that businesses would pay premium prices for reliable AI. Consumer plays like Character.AI bet on viral adoption and eventual monetization through subscriptions or ads.

Both were probably right, but for different reasons. Enterprises would pay for AI that demonstrably improved productivity. Consumers would pay for AI that provided entertainment or companionship. The mistake was assuming one model would dominate.

The Geopolitical Dimension: The New Cold War

The AI race wasn't just about companies; it was about countries. The US China dynamics around AI resembled nothing so much as the space race, with similar national security implications.

The Silicon Curtain

The October 2023 export controls weren't just trade policy; they were technological containment. By blocking China's access to advanced chips and chip making equipment, the US aimed to maintain a permanent AI advantage.

The strategy was both brilliant and risky. Brilliant because it targeted China's key weakness: semiconductor manufacturing. Risky because it might accelerate China's push for self sufficiency. China's quadrupling of lithography equipment imports before restrictions took effect showed they saw it coming.

The Rare Earth Response

China's restrictions on gallium, germanium, and antimony exports were the predictable counterpunch. These materials were essential for semiconductor manufacturing, and China controlled most global supply. The message was clear: you have chips, we have materials, let's not escalate.

The Innovation Dilemma

The restrictions created an innovation paradox. By cutting China off from advanced technology, the US might force them to develop alternatives. SMIC's achievement of 5 nanometer manufacturing despite restrictions suggested this was already happening.

For US companies, the restrictions meant losing the world's largest market. Nvidia's GPUs tripling in price in China helped nobody; it just created gray markets and resentment. The long term cost to US tech dominance remained unclear.

Strategic Implications: The Next Moves

Looking at the board in May 2023, several strategic imperatives emerged:

The Talent War Intensifies

With key researchers like Hinton leaving, talent became the constraining factor. Companies needed not just engineers but AI researchers who understood the theoretical foundations. This explained the astronomical salaries and equity packages being offered.

Universities became battlegrounds, with companies funding labs and endowing chairs to maintain research pipelines. The brain drain from academia to industry accelerated, raising questions about long term innovation.

The Safety Performance Trade off

The split between safety focused Anthropic and capability focused OpenAI would define the industry's development. Companies would need to choose: prioritize safety and risk being overtaken, or push capabilities and risk regulatory backlash or worse.

The smart players would try to have both, but the tension was fundamental. Every resource spent on alignment was a resource not spent on capabilities, and vice versa. The market would ultimately judge which approach won.

The Platform Lock In

As companies integrated AI into their stacks, switching costs would increase dramatically. Microsoft's integration of OpenAI into Office created massive lock in. Once companies built workflows around Copilot, moving to Claude or Bard became extremely expensive.

This suggested first mover advantage would matter more than in previous platform shifts. The company that got enterprises to commit first would have enormous advantages, explaining the rush to market with imperfect products.

The Regulatory Wild Card

May 2023 was the last moment before regulation became real. The EU's AI Act was coming. The US was debating various proposals. China had its own framework. The regulatory landscape would shape everything from model development to deployment strategies.

Smart companies were hiring policy teams and engaging with regulators. The naive view that AI would develop without government intervention was ending. The question wasn't whether regulation would come, but what form it would take.

The View from May 2023

Standing in May 2023, the AI landscape resembled the early railroad era: transformative technology, massive capital investment, unclear business models, and the promise of reshaping society. Like the railroads, there would be boom and bust, consolidation and failure, but the technology would fundamentally change how the world worked.

The players were positioned, the capital was deployed, and the race was on. OpenAI had the lead but Microsoft had the distribution. Google had the talent but faced the innovator's dilemma. Anthropic had the safety story but needed commercial success. Meta had the open source strategy but lacked a clear product. Amazon had the infrastructure but no proprietary models.

The startups were wildcards. One of them might crack the killer application that made AI indispensable. Or they might all be acquired or crushed as the giants consolidated the market. The only certainty was uncertainty.

What made May 2023 special wasn't the technology itself but the moment of possibility it represented. The future was still malleable. The winners weren't yet determined. The rules weren't yet written. Everyone from graduate students to Fortune 500 CEOs could still shape what came next.

Hinton's departure symbolized this inflection point. The creators were stepping back, warning about their creations, while a new generation rushed forward. Whether they were rushing toward utopia or catastrophe remained to be seen. But one thing was certain: the world after large language models would never be the same as the world before them.

The great AI game of 2023 wasn't just about building better models or raising more money. It was about defining humanity's relationship with artificial intelligence. Every player, from the godfathers to the startups, from the investors to the regulators, was participating in this definition. The stakes couldn't be higher, and the game was just beginning.