The Rise of OpenAI: Power Struggles Behind the Journey from Nonprofit to a $300 Billion AI Empire

From the explosive debut of ChatGPT to becoming one of the world's most influential tech companies, OpenAI's rise may seem like a foregone conclusion. But beneath the surface, the company's true history reads like a business epic filled with betrayed ideals, power struggles, and technological ambition.

Sam Altman: From Ramen-Fueled Startup Founder to the Heart of Silicon Valley Power

Sam Altman dropped out of Stanford, where he was studying computer science, to start a company. His first project, Looped, was a location-sharing app that predated both the iPhone and the App Store — a product that struggled in an era lacking the necessary infrastructure. Altman survived on instant noodles and ice cream, working so intensely and eating so poorly that he actually developed scurvy.

In 2005, he joined the inaugural Y Combinator batch and caught the attention of founder Paul Graham. Graham famously said of Altman: "Drop him on a cannibal island, come back in five years, and he'd be king." The quote proved prophetic of Altman's extraordinary ability to acquire power — before long, he became president of Y Combinator at just 30 years old, running Silicon Valley's most prestigious startup accelerator and building an enormous network of connections.

Y Combinator (YC) is a startup accelerator founded in 2005 by Paul Graham and others, widely regarded as the most influential early-stage investment institution in Silicon Valley and beyond. YC's model involves selecting a small number of startup teams from thousands of applications twice a year, providing seed funding in exchange for roughly 7% equity, offering intensive mentorship during a three-month "batch," and culminating in a Demo Day pitch event. Companies that have emerged from YC include Airbnb, Dropbox, Stripe, Reddit, and other giants valued in the tens of billions. Its alumni network forms one of Silicon Valley's tightest-knit founder communities. Running YC meant becoming a central node in Silicon Valley's startup ecosystem — hundreds of founders each year owed you a favor, which gave Altman an unparalleled foundation for mobilizing resources and building alliances later on.

The Birth of OpenAI: An AI Safety Alliance Born from Fear

In 2015, Elon Musk was deeply concerned about AI safety. At the time, Google was the undisputed leader in AI, having acquired numerous AI labs and controlling roughly three-quarters of the world's top AI talent. Musk once asked Google CEO Larry Page how he planned to ensure superintelligence wouldn't destroy humanity, only to be told he was being paranoid.

Musk urgently wanted to dilute Google's AI monopoly. That same year, ten figures from the tech world — including Musk, Altman, Greg Brockman, and leading AI researcher Ilya Sutskever — gathered over dinner to discuss forming a company to counter Google. Musk pledged $1 billion.

And so OpenAI was founded in 2015 as a nonprofit organization. Its mission was clear: to safely build AI for the benefit of humanity and share the results freely — which is exactly where the name "Open" came from.

The Chaotic Early Days: AGI Faith and Exploration

In its early days, OpenAI was unimpressive — it didn't even have an office, with the team crammed into Brockman's apartment. Despite the billion-dollar pledge, they lacked a clear strategy, spending considerable time developing bots that could play Dota 2 and experimenting with robot butlers. One early employee admitted: "We were just building random stuff to see what would happen."

At this point, Altman was still running Y Combinator, and Musk was busy with his other ventures. The company was effectively led by Ilya, widely regarded as an "AI genius," and Brockman, who excelled at operations. The team's true shared belief was in building AGI (Artificial General Intelligence) — intelligence capable of matching or surpassing humans at most tasks and autonomously learning skills it was never explicitly trained on.

AGI (Artificial General Intelligence) stands in stark contrast to today's "Narrow AI." Narrow AI excels at specific tasks — playing chess, recognizing images, translating languages — but cannot transfer knowledge from one domain to another. AGI is defined as possessing general cognitive capabilities equal to or exceeding those of humans, able to understand, learn, and apply knowledge to any intellectual task. There is still no unified academic definition of AGI; OpenAI itself defines it as "highly autonomous systems that outperform humans at most economically valuable work." Whether AGI is achievable and when it might arrive is hotly debated: optimists like OpenAI believe it could come within years, while critics such as NYU professor Gary Marcus argue that the current LLM-based approach has fundamental limitations that cannot lead to true AGI. This question is not merely a technical debate — it involves trillions of dollars in investment decisions and global regulatory policy.

Employees spoke about AGI as if they were "creating God." They believed AI could solve problems like unemployment, disease, and poverty, while also fearing it could become the greatest threat in human history. Ilya once used an elegant analogy to explain the risk: "AGI doesn't hate humans — it's just too powerful. It's like how humans don't ask animals for permission when building a highway between two cities."

The Transformer Architecture: A Technical Breakthrough That Changed AI

In 2015, the AI field was still emerging from the tail end of the "AI winter." The term "AI winter" refers to cyclical periods when the artificial intelligence field suffered dramatic declines in funding and confidence due to progress falling short of expectations. There were two major ones historically: the 1970s, caused by the limitations of early symbolic AI, and the 1990s, caused by the commercial failure of expert systems. By the 2010s, the field was experiencing its third wave, driven by exponential increases in GPU computing power, massive datasets generated by the internet, and breakthroughs in deep learning algorithms — particularly AlexNet's dominant victory in the 2012 ImageNet competition. Although AlphaGo's 2016 defeat of a professional Go player was exciting, AI at the time could only handle single tasks — if you wanted it to write a story or solve an equation, you'd have to rebuild the entire system because training data had to be clearly labeled (e.g., "hot dog" or "not hot dog").

In 2017, a team at Google published the paper Attention Is All You Need, introducing the Transformer architecture. The Transformer's core innovation was the "Self-Attention Mechanism." Before this, processing sequential data (like text) relied primarily on Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), which had to process information word by word in sequence — not only slow, but prone to "forgetting" earlier content in long texts. The Transformer completely abandoned this sequential approach, allowing every element in a sequence to simultaneously "attend to" all other elements and compute the strength of their relationships — meaning two words far apart in a sentence could directly establish connections. More critically, this parallel computation characteristic allowed Transformers to fully exploit GPU parallel processing capabilities, dramatically accelerating training. They didn't need carefully labeled data; they could ingest messy, unlabeled data and learn on their own.

Ironically, this breakthrough came from Google, not OpenAI, but Google's massive size and fear of damaging its search advertising business made it slow to act, squandering the opportunity. This architecture gave birth not only to the GPT series but also serves as the foundation for BERT, T5, LLaMA, and virtually every modern large language model.

Ilya recognized its potential the moment he read the paper, and OpenAI became one of the first companies to seriously experiment with the technology. The famous acronym GPT comes directly from this — Generative Pre-trained Transformer. The vast trove of digitized content on the internet became the perfect training data.

You might not have noticed, but directly scraping internet data raised obvious copyright concerns. True to Silicon Valley's "move fast and ask forgiveness later" ethos, OpenAI simply went ahead. By the time GPT-2 arrived, the model was powerful enough to alarm the team — they ultimately broke their open-source promise, announcing that "due to concerns about malicious use, we will not release the trained GPT-2 model." This actually generated enormous buzz, with Wired magazine calling it "an AI text generator too dangerous to release." OpenAI was gradually becoming less "Open."

From Nonprofit to For-Profit: The Controversial Commercial Transformation

In 2018, Musk resigned from OpenAI's board. The official reason was conflict of interest (Tesla was also developing AI), but the truth was that Musk had tried to take over OpenAI as CEO, even proposing to merge it into Tesla. When the board rejected him, he stormed out, taking his unfulfilled investment pledges with him (he had promised $1 billion but actually delivered less than $100 million).

The funding crisis forced OpenAI into a controversial decision: transforming from a nonprofit into a for-profit company and commercializing its technology. OpenAI's original 501(c)(3) nonprofit structure meant it couldn't have shareholders, couldn't distribute profits, and all revenue had to serve its charitable mission. The 2019 transformation created an unprecedented "capped profit" hybrid structure: a for-profit subsidiary was established under the nonprofit parent, with investor returns capped at 100 times their investment, with any excess going to the nonprofit. In theory, the nonprofit board had complete control over the for-profit entity, and its fiduciary duty was to all of humanity, not shareholders. However, this elegant design later revealed a fundamental fragility — when the company's valuation skyrocketed to tens of billions of dollars and hundreds of employees had stock options tied to the company's survival, it became virtually impossible for the nonprofit board to prevail in any contest with commercial interests.

OpenAI then partnered with Microsoft, which invested $1 billion. Many saw this as a complete betrayal of the founding mission — an organization created to counterbalance Big Tech was now empowering one of the world's most powerful tech giants. Musk later sued the company, demanding it change its name from "OpenAI" to "ClosedAI." After the transformation, Altman naturally became CEO. As of 2025, OpenAI is pushing forward with a conversion to a fully for-profit Public Benefit Corporation, a process facing scrutiny from multiple state attorneys general and legal challenges from Musk.

The ChatGPT Phenomenon: Rewriting the AI Industry Landscape

GPT-3 launched in 2020, but the true historic moment came on November 30, 2022, with the public release of ChatGPT. At the time, OpenAI saw it as nothing more than a "low-key research preview" — employees placed bets on first-week user numbers, with the highest guess being 100,000.

They were wildly wrong. ChatGPT went viral, reaching 100 million users in just two months and becoming the fastest-growing application in history — by comparison, Facebook took four and a half years to hit the same milestone.

But internally, not everyone was celebrating. Some employees were uneasy about such a hasty launch, and certain safety team members hadn't even been informed. They worried about hacking exploits, criminal assistance, and "hallucinations" (factual errors). "Hallucination" is a core flaw of large language models: the model will output completely fabricated information with extreme confidence — inventing nonexistent academic papers, fabricating legal statutes, even falsifying historical events. This stems from the fundamental nature of LLMs as "next token predictors," optimizing for textual fluency and coherence rather than factual accuracy. There is still no fundamental solution to this problem, and it remains one of the biggest obstacles to AI deployment in high-stakes domains like healthcare and law.

After ChatGPT's release, Microsoft poured in an additional $10 billion investment, and the AI race went into full overdrive. But internally, the rift between the product-focused and safety-focused factions deepened. Several core developers left to found Anthropic, a safety-focused competitor. Anthropic was co-founded in 2021 by former OpenAI research VP Dario Amodei and his sister Daniela Amodei, with a core team of several senior OpenAI safety researchers. Their fundamental reason for leaving was dissatisfaction with OpenAI's increasing commercialization and the marginalization of safety priorities. Anthropic's core technical philosophy is "Constitutional AI" — using a preset set of principles to allow AI to self-regulate during training, reducing harmful outputs. The company's Claude model series is widely regarded as leading in safety and controllability. As of 2024, Anthropic has received over $7 billion in investment from companies including Amazon and Google. Anthropic's rise marks a clear fault line in the AI industry: companies have made starkly different choices between pushing the limits of capability and prioritizing safety.

The Five-Day Coup: Altman's Firing and Dramatic Return

On November 17, 2023, less than 24 hours after Altman delivered a rousing speech at a world leaders' summit, he was abruptly fired by the board via video call. The board's statement said he had been "not consistently candid in his communications," implying he had lied and could not be trusted.

The reasons for his firing were subject to much speculation: Altman had allegedly lied about items being approved by the safety committee, attempted to oust board member Helen Toner, and was building a sprawling "empire" outside the company that included AI chips and WorldCoin. WorldCoin is a cryptocurrency project co-founded by Altman, with the core idea of creating a unique digital identity for every person on Earth through iris-scanning devices and distributing free crypto tokens as infrastructure for a future AI-era "universal basic income." The project has sparked serious privacy concerns in multiple countries and led the board to question whether Altman was diverting too much of his attention to personal business ventures outside OpenAI.

The most unsettling speculation was that Ilya, who was deeply involved in safety work, had "seen something" internally that prompted him to turn against Altman. "What did Ilya see?" briefly became a social media meme.

But events reversed rapidly. Brockman resigned in solidarity, nearly all of the company's ~800 employees signed a petition threatening to quit, and Microsoft announced it would welcome Altman and any employees willing to defect. The final blow to the board was Ilya's own reversal — he publicly expressed "deep regret." Within just five days, Altman returned to power, the dissenting board members were ousted, and he even participated in selecting new directors, leaving his authority stronger than ever. Ilya lost his board seat and left OpenAI entirely six months later, going on to found his own AI safety company, Safe Superintelligence Inc. (SSI), focused on developing safe superintelligence free from commercial pressures.

Ironically, Altman had publicly stated on multiple occasions that the board should be able to fire him and hold him accountable. But when the board actually tried, it turned out to be impossible. The coup's failure laid bare a fundamental governance paradox: when a company's core assets are its people, and those people's loyalty is to the founder rather than the governance structure, any institutional design can become toothless.

The New AI Competitive Landscape: A $300 Billion Valuation and Future Power Struggles

ChatGPT's voice mode drew comparisons to the movie Her — directed by Spike Jonze in 2013, the film tells the story of a man who falls in love with an AI voice assistant, with Scarlett Johansson voicing the AI character, creating a warm, intelligent, emotionally rich artificial intelligence. The film is now considered one of the most prescient cultural prophecies about human-machine relationships. Altman even invited Johansson to voice ChatGPT; after she declined, the company still released a strikingly similar voice, sparking a lawsuit — and a tweet Altman posted after the feature's launch simply reading "Her" only made matters worse.

People used to assume AI would replace manual labor first, with creative work being the last to fall. The opposite turned out to be true: creative industries are being disrupted first. AI image generation tools like Midjourney and DALL-E have already impacted the market for illustrators and graphic designers, while AI music composition and AI screenwriting tools are reshaping the entertainment industry's cost structure. During the 2023 Hollywood writers' and actors' guild strikes, AI replacement was one of the central issues. Even Google's seemingly unshakeable dominance in search is now threatened by ChatGPT — as more users choose to ask AI directly rather than use a search engine, posing a fundamental threat to Google's $170+ billion annual search advertising revenue.

As of 2025, OpenAI has completed a $40 billion funding round led by SoftBank, reaching a valuation of $300 billion — the largest private tech fundraise in history. Former co-founder Musk and Altman are now rivals, publicly sparring — Musk founded his own AI company, xAI, and launched the Grok model, while also mounting ongoing legal challenges to OpenAI's for-profit conversion.

In January 2025, Chinese AI competitor DeepSeek sent shockwaves through the entire American tech industry. DeepSeek was founded in 2023 by Liang Wenfeng, founder of Chinese quantitative investment giant High-Flyer Capital Management. Its R1 reasoning model matched or approached OpenAI's flagship models on multiple benchmarks, but reportedly at a fraction of the training cost. The news directly caused NVIDIA's market cap to plunge nearly $600 billion in a single day — the largest single-day drop in U.S. stock market history — as the market suddenly questioned whether the logic of American tech companies investing tens of billions in AI infrastructure held up if efficient open-source models could achieve comparable performance at a fraction of the cost. DeepSeek's rise also cast doubt on the effectiveness of U.S. chip export controls on China — even with restricted access to the most advanced chips, Chinese teams found alternative paths through algorithmic innovation. Hundreds of billions of dollars in market value were wiped out. OpenAI accused DeepSeek of stealing its intellectual property, only to be widely mocked — after all, OpenAI itself got its start by scraping data from across the internet.

The AI race is intensifying, and while no one can say for certain where it will end, one thing is clear: the outcome will profoundly affect every one of us.