The OpenAI Story
From Nonprofit Research Lab to the World’s Most Valuable Private Company
A Comprehensive Analysis | Technology History and Industry Intelligence
Executive Summary
In little more than a decade, OpenAI has gone from a modest nonprofit research lab to arguably the most consequential technology company of the 21st century. It was founded in December 2015 with a pledge of $1 billion and a stated mission to develop artificial general intelligence for the benefit of all humanity. From there, the organization pioneered the large language model revolution, kicked off the global generative AI boom, and pushed every major technology company on earth to rethink its strategic roadmap. By early 2026, OpenAI had surpassed $20 billion in annualized revenue, attracted total investments exceeding $174 billion, and reached a valuation of $730 billion, making it the most valuable privately held company in the world. This is the story of how it got there.
1. Origins: The Founding of OpenAI
The Founding Vision
On December 11, 2015, a small group of technologists, entrepreneurs, and investors gathered in San Francisco to announce the formation of OpenAI. The founding roster was remarkable. It included Sam Altman, then president of Y Combinator; Greg Brockman, formerly CTO of payments giant Stripe; Ilya Sutskever, a researcher who had worked directly under deep learning pioneer Geoffrey Hinton at Google Brain; Elon Musk, already well known for his work at Tesla and SpaceX; and a number of other luminaries including Reid Hoffman, Peter Thiel, Jessica Livingston, and Amazon Web Services. Together, they pledged $1 billion to the new organization, though only a fraction of that would be deployed in its early years.
The founding mission was straightforward in its language but staggering in its ambition. OpenAI declared that its goal was to develop artificial general intelligence, meaning AI systems capable of performing virtually any intellectual task a human could, in a way that was safe and broadly beneficial to all of humanity. Crucially, unlike the AI labs embedded inside Google, Meta, and Microsoft, OpenAI was incorporated as a nonprofit. According to its charter, the organization would be “unconstrained by a need to generate financial return.” The choice was deliberate and ideological. The founders were worried that if AGI were developed inside a profit-driven corporation, its benefits might flow to shareholders rather than to humanity at large.
The Founders and Their Motivations
Each founding figure came to the project with distinct motivations. Elon Musk was partly driven by concern that Google’s DeepMind project was racing toward transformative AI without sufficient safeguards. He reportedly proposed that OpenAI could serve as a counterweight: a well-resourced, safety-conscious lab that would ensure the technology remained democratically accessible. Sam Altman saw a real organizational design opportunity. As a nonprofit, OpenAI could recruit world-class researchers by appealing to mission rather than salary, placing it alongside universities and research institutes as a destination for people motivated by impact rather than financial gain.
Ilya Sutskever, recruited from Google Brain where he had co-created foundational deep learning breakthroughs, gave the lab its initial scientific credibility. Greg Brockman took on the role of technical architect and operator. The combination of Musk’s public profile, Altman’s operational skill, and Sutskever’s research excellence created an organization that was uniquely positioned for what was to come. Musk departed the board in 2018, citing conflicts of interest with his own AI ventures. Even so, he continued to cast a long shadow over the company. He later launched a rival lab called xAI and filed multiple lawsuits against OpenAI alleging betrayal of its founding mission.
2. Early Research and Development (2016 to 2019)
Building the Research Foundation
OpenAI’s early years were shaped by a productive tension between pure research ambition and the practical need to produce results that justified the founders’ investment. The lab was deliberately eclectic. Researchers explored reinforcement learning, natural language processing, robotics, and game-playing AI all at the same time, publishing their work openly in keeping with the nonprofit’s transparency mandate.
In April 2016, OpenAI released OpenAI Gym, an open-source toolkit that enabled researchers and developers to build and test reinforcement learning algorithms. The release mattered less for any single breakthrough than for its community-building effect. By providing a standardized platform for RL experimentation, Gym accelerated academic progress around the world and positioned OpenAI as a generous contributor to shared scientific progress.
The Dota 2 Experiments and OpenAI Five
Between 2017 and 2018, OpenAI drew widespread attention with an ambitious project: training AI agents to play Dota 2, one of the most strategically complex competitive video games in the world. The resulting system, called OpenAI Five, operated entirely through reinforcement learning and played the equivalent of 180 years of game time every single day. In a landmark demonstration in August 2018, OpenAI Five defeated a team of professional Dota 2 players in a best-of-three series. The project made a clear case for the power of large-scale computational training and reinforced what was becoming a defining belief at OpenAI: that scale matters enormously.
The Structural Shift to Capped-Profit
By 2019, the tension between OpenAI’s nonprofit structure and the growing cost of frontier AI research had become impossible to ignore. Training advanced models required hundreds of millions of dollars in computing power. To address this, OpenAI created a for-profit subsidiary called OpenAI LP, which it structured as a “capped profit” entity. Investors could receive returns on their capital, but those returns were capped at 100 times the original investment. The nonprofit board kept ultimate control. This restructuring allowed OpenAI to attract venture capital and, critically, to lock in a transformative partnership that would change the trajectory of the entire organization.
3. The GPT Revolution
GPT-1: The First Signal (2018)
In June 2018, OpenAI published a paper introducing GPT-1, which stood for Generative Pre-trained Transformer. The model was trained on 117 million parameters across a large body of internet text. It demonstrated that a single large neural network, pre-trained on massive amounts of text data and then fine-tuned for specific tasks, could reach state-of-the-art performance across a wide range of natural language benchmarks. GPT-1 was not a consumer product. It was a research demonstration. But the underlying principle it confirmed would prove to be world-changing.
GPT-2: The Model Too Dangerous to Release (2019)
When OpenAI published GPT-2 in February 2019, it made an unusual choice: it withheld the full model from public release. The stated reason was concern that the technology could be used for misinformation campaigns, spam, and synthetic propaganda. Trained on 1.5 billion parameters, the model could generate coherent and persuasive text so fluently that OpenAI’s safety team judged it a meaningful dual-use risk. The decision sparked fierce debate in the AI research community. Critics accused OpenAI of sensationalism, while supporters treated the move as a valuable precedent for responsible disclosure. OpenAI eventually released the full model in November 2019, and no catastrophic misuse emerged in the aftermath.
GPT-3: The Watershed Moment (2020)
In May 2020, OpenAI released GPT-3, and the AI landscape shifted in a way it had not before. With 175 billion parameters, which was more than 100 times the scale of GPT-2, GPT-3 could write essays, generate functional code, compose poetry, answer factual questions, and hold coherent multi-turn conversations with a fluency that surprised even veteran researchers. The model’s capabilities came not from any architectural breakthrough but mainly from scale: more parameters, more training data, and more compute. This “scaling hypothesis,” the idea that intelligence could be reliably improved simply by increasing model size and training, became a guiding principle for the entire field. GPT-3 was made accessible through an API, which allowed developers to build products on top of it. The resulting ecosystem showed clearly that large language models had genuine commercial value well beyond academic benchmarks.
GPT-4 and the Multimodal Era (2023)
Released in March 2023, GPT-4 represented another meaningful leap forward. OpenAI did not publish the model’s parameter count, which itself signaled a shift in the company’s posture toward openness, but GPT-4 showed substantially better reasoning, fewer hallucinations, and for the first time, multimodal capabilities. The model could process both text and images as inputs. In benchmark testing, GPT-4 scored in approximately the 90th percentile of human test-takers on the bar exam, performed at expert level on AP examinations across multiple subjects, and outperformed earlier models on virtually every standardized measure of language understanding. GPT-4 became the engine behind ChatGPT’s most capable tier and Microsoft’s Copilot suite of productivity tools.
The GPT lineage continued to evolve rapidly. GPT-4o, released in May 2024, introduced native voice and image generation. GPT-4.1 followed in April 2025. GPT-5 launched in August 2025 with extended reasoning and “thinking” modes that dramatically reduced errors on complex logical and mathematical problems. By early 2026, GPT-5.4 represented the frontier of the GPT architecture, combining reasoning, coding, and agentic workflow capabilities in a single model.
4. The Launch of ChatGPT: The Fastest-Growing App in History
November 30, 2022
November 30, 2022 is now a landmark date in technology history. On that day, OpenAI released ChatGPT as a free research preview. It was a conversational interface built on top of GPT-3.5, and it allowed any person with an internet connection to interact with a capable language model through plain, natural dialogue. There was no elaborate launch event and no advertising campaign. The product was posted to OpenAI’s website and shared on social media. Within five days, it had attracted one million registered users. Within two months, it had surpassed 100 million monthly active users, making it the fastest-growing consumer software application in the history of the internet. The previous record had been held by TikTok, which took nine months, and Instagram, which took two and a half years.
“ChatGPT is the best artificial intelligence chatbot ever released to the general public.” — Kevin Roose, The New York Times, December 2022
The Numbers That Defined a Phenomenon
The scale of ChatGPT’s adoption was genuinely unlike anything seen before in consumer technology. By December 2022, approximately 57 million users had signed up. By early 2024, the service was processing more than 1.6 billion website visits per month. By August 2024, ChatGPT had reached 200 million weekly active users. By late 2025, that figure had climbed to approximately 800 million weekly active users, a user base equivalent to more than twice the population of the United States engaging with the product every week.
| Milestone | Value |
|---|---|
| Time to 1 million users | 5 days |
| Time to 100 million users | About 2 months |
| TikTok comparison | 9 months |
| Instagram comparison | 2.5 years |
| Weekly active users (Late 2025) | About 800 million |
Impact Across Industries
ChatGPT’s arrival did not just create a new product category. It triggered a widespread reassessment of AI’s role across virtually every knowledge-intensive industry. In education, universities around the world scrambled to update their academic integrity policies as students began using the tool for essays, problem sets, and research. A Pew Research Center survey from March 2023 found that 14 percent of U.S. adults had already tried it. In software development, the impact was immediate. Developers reported generating, debugging, and explaining code through simple conversational prompts. Stack Overflow temporarily banned AI-generated answers as the platform struggled to handle the volume of machine-generated content. In law, consulting, marketing, and finance, early adopters reported productivity gains that, if sustained at scale, pointed to fundamental shifts in how knowledge work is priced and organized.
5. The Microsoft Partnership and the Funding Machine
The Strategic Alliance That Changed Everything
The relationship between OpenAI and Microsoft is one of the defining corporate partnerships of the technology era. Microsoft made its first investment in OpenAI in 2019, committing $1 billion in a combination of cash and Azure cloud computing credits. The deal gave OpenAI access to the computing infrastructure needed to train frontier models while giving Microsoft early access to the world’s most capable AI technology.
The dynamic shifted significantly in January 2023 when Microsoft announced a follow-on investment reportedly in the range of $10 billion, bringing its total commitment to approximately $13 billion. The announcement came just weeks after ChatGPT’s viral launch and reflected Microsoft CEO Satya Nadella’s belief that AI was the most important platform shift since the rise of mobile computing. In exchange, Microsoft received the right to integrate OpenAI’s models into its entire product suite and eventually a 27 percent equity stake in the restructured for-profit entity.
Copilot: AI Woven Into the Fabric of the Enterprise
The commercial payoff of the Microsoft partnership became visible with the launch of Microsoft Copilot, an AI layer embedded across Word, Excel, PowerPoint, Outlook, Teams, the Bing search engine, and the Windows operating system itself. The product represented the most ambitious enterprise AI deployment in history, putting GPT-4 functionality within reach of hundreds of millions of Microsoft 365 users. For OpenAI, the partnership provided not only revenue from API usage but also a distribution channel of unparalleled scale, ensuring its technology was embedded in the daily workflows of businesses and governments worldwide.
The Funding Escalation
OpenAI’s valuation history reads like a record-setting sprint. The company was valued at roughly $29 billion in early 2023. By October 2023, that had risen to $80 billion following a tender offer transaction. A $6.6 billion funding round in October 2024 pushed the valuation to $157 billion. In March 2025, SoftBank led a $40 billion round at a $300 billion valuation. By February 2026, a $110 billion round led by Amazon, Nvidia, and SoftBank, the largest private funding round in recorded history, pushed the valuation to $730 billion, making OpenAI the most valuable privately held company in the world.
| Date | Valuation | Key Event |
|---|---|---|
| Early 2023 | $29 billion | Post-ChatGPT launch |
| Oct 2023 | $80 billion | Tender offer |
| Oct 2024 | $157 billion | $6.6B funding round |
| Mar 2025 | $300 billion | SoftBank $40B round |
| Feb 2026 | $730 billion | Amazon, Nvidia, SoftBank $110B round |
6. Products and Technologies Developed by OpenAI
DALL-E: The Image Generation Pioneer
In January 2021, OpenAI released DALL-E, a neural network that could generate images from natural language text prompts. The name is a portmanteau of the surrealist artist Salvador Dali and the Pixar robot WALL-E. The model could create photorealistic images, artistic renderings, and fantastical visual scenarios on demand. DALL-E 2 arrived in April 2022 and DALL-E 3 in October 2023, each delivering substantially better resolution, artistic coherence, and prompt accuracy. The models sparked an entire industry of text-to-image competitors and fundamentally disrupted stock photography, graphic design workflows, and advertising creative production.
Codex: Teaching Machines to Code
OpenAI Codex was introduced in August 2021 as a descendant of GPT-3 specifically fine-tuned on public code repositories. The model could translate natural language instructions into functional code across dozens of programming languages. GitHub partnered with OpenAI to deploy Codex as the engine behind GitHub Copilot, which launched in technical preview in 2021 and became generally available in 2022. Copilot has since been adopted by millions of developers and stands as one of the most commercially successful applications of generative AI to date. By 2025, an updated version of Codex had evolved into an autonomous software engineering agent capable of writing code, debugging, running tests, and creating pull requests with minimal human involvement.
Sora: Text-to-Video and the Next Frontier
In February 2024, OpenAI unveiled Sora, a text-to-video generation model capable of producing high-definition, physically coherent video clips up to one minute in length from written prompts. The demonstration videos, which included a bustling Tokyo street scene, a corgi splashing in the surf, and a drone flight over a gold rush-era settlement, were indistinguishable from professional cinematography to many viewers. Sora’s release generated immediate concern among filmmakers, visual effects artists, and advertising creative directors about the long-term impact on their industries, while at the same time triggering a wave of investment in competing video generation systems at Google, Meta, and a range of startups.
The API and Developer Ecosystem
Behind and beneath all of OpenAI’s consumer products is a developer platform that has become one of the most widely used technology APIs in the world. First launched in 2020 alongside GPT-3 and expanded through each successive model generation, the OpenAI API lets developers integrate language, image, code, and voice capabilities into their own applications. By 2024, the platform was supporting five million business users and an ecosystem of tens of thousands of third-party applications spanning customer service automation, content creation, legal research, medical documentation, and educational tools. The API’s usage-based pricing became a major revenue driver alongside ChatGPT subscriptions.
7. Controversies and Challenges
The November 2023 Boardroom Crisis
On November 17, 2023, OpenAI’s board of directors fired CEO Sam Altman. The board cited a determination that he had not been “consistently candid” in his communications with them. What followed was five days of extraordinary corporate chaos that captivated Silicon Valley and the broader technology world. Altman was removed effective immediately, and co-founder Greg Brockman resigned in protest. Within hours, reports emerged that Microsoft was offering Altman a position to lead a new AI research division within the company.
What happened next became legendary in business history. Within 24 hours, nearly every OpenAI employee had signed an open letter threatening mass resignation unless Altman was reinstated. Microsoft, which had not been informed of the firing in advance and watched its stock price drop in real time as the drama unfolded, applied enormous pressure. On November 22, five days after his removal, Altman was reinstated as CEO. The board was reconstituted, and most of the members who had voted for his ouster departed. Subsequent reporting revealed that the underlying causes included concerns about Altman’s alleged failure to disclose certain conflicts of interest, allegations of misleading the board about safety review processes, and deep ideological disagreements about how fast to move. The episode exposed, with unusual public clarity, the central fault line running through the AI industry.
The Safety Exodus
The boardroom crisis turned out to be a precursor to a broader departure of safety-oriented researchers. In May 2024, Ilya Sutskever announced he was leaving OpenAI. On the same day, Jan Leike, who had co-led OpenAI’s “superalignment” team, resigned and published a public statement declaring that “safety culture and processes have taken a backseat to shiny products.” Leike alleged that the superalignment team had been denied the computing resources it had been promised and that months of work had been undermined by internal resistance. Shortly afterward, chief research officer Bob McGrew and VP of research Barret Zoph also departed. CTO Mira Murati, a respected technical leader who had served as interim CEO during the five-day crisis, resigned in September 2024.
Elon Musk and the Ongoing Legal War
Elon Musk, one of OpenAI’s co-founders, became its most prominent public critic. In February 2024, he filed a lawsuit alleging that OpenAI had abandoned its nonprofit mission by commercializing its technology in partnership with Microsoft. OpenAI countersued in April 2024, alleging that Musk had acted in bad faith to slow the company’s progress for his personal benefit and to advance his own AI venture, xAI. In February 2025, a Musk-led consortium submitted an unsolicited $97.4 billion bid to acquire the nonprofit entity that controls OpenAI. OpenAI rejected the offer outright.
Copyright, Privacy, and Regulatory Pressure
OpenAI faced a wave of copyright litigation starting in 2023. The New York Times filed suit in December 2023, alleging that millions of its articles had been used to train GPT models without authorization or compensation. Dozens of authors and publishers filed related suits. These legal challenges raised fundamental questions about whether training large models on internet-scraped data is legally permissible, and those questions remained unresolved in courts across multiple jurisdictions as of early 2026. Regulatory pressure also intensified globally. Italy temporarily banned ChatGPT in March 2023 over data privacy concerns, and the European Union’s AI Act established new compliance requirements for high-risk AI systems that directly affected OpenAI’s European operations.
8. OpenAI’s Influence on the Global AI Industry
Catalyzing the Generative AI Race
No single event in recent technology history triggered a competitive response as swift or as massive as the launch of ChatGPT. Google, whose research teams had actually pioneered the transformer architecture that underpins GPT models and who employed the authors of the landmark 2017 paper “Attention Is All You Need,” found itself in the unusual position of playing catch-up. CEO Sundar Pichai reportedly issued an internal “code red” and accelerated the deployment of Google Bard, which was later rebranded as Gemini. DeepMind, Google’s London-based AI research subsidiary, was merged with Google Brain in 2023 to form Google DeepMind, with a mandate to move faster on competitive model development.
Meta, under Mark Zuckerberg, pursued a different strategy. Rather than building a closed commercial model like ChatGPT, Meta developed the LLaMA family of open-source models and released them publicly, including the weights. This allowed anyone to download, modify, and deploy capable language models without API fees or usage restrictions. The decision transformed the competitive landscape and created a thriving open-source AI ecosystem that rivaled OpenAI in certain domains while fundamentally challenging the commercial advantages of closed-model providers.
Anthropic: The Safety-Focused Offshoot
Perhaps the most direct consequence of OpenAI’s internal safety debates was the founding of Anthropic in 2021. Dario Amodei, OpenAI’s VP of Research, and Daniela Amodei, VP of Operations, left to start a new AI safety company alongside several other former OpenAI researchers. Anthropic explicitly positioned itself as a safety-first alternative, developing its own alignment techniques and publishing research on constitutional AI and interpretability. By February 2026, Anthropic had raised $69.1 billion in total funding, reflecting strong investor appetite for a safety-oriented alternative to OpenAI’s approach.
The Global Market Transformation
OpenAI’s success triggered a cascade of investment that reshaped the global AI industry. The generative AI market expanded from approximately $191 million in 2022 to $25.6 billion in 2024, a trajectory driven primarily by the commercial proof of concept that ChatGPT provided. Hundreds of AI startups were funded, acquired, or incubated in its wake. Cloud computing providers including Amazon Web Services, Google Cloud, and Microsoft Azure made AI infrastructure their central competitive battleground, committing hundreds of billions of dollars to data center expansion, custom silicon, and AI-specific hardware. Nvidia, whose GPUs provide the computational substrate for training large models, saw its market capitalization surge from under $400 billion in early 2023 to briefly surpass $3 trillion in 2024, the largest market cap increase in the history of publicly traded companies.
9. Key Statistics and Data
| Metric | Value | Source / Date |
|---|---|---|
| ChatGPT Weekly Active Users | About 800 Million | OpenAI, Nov 2025 |
| ChatGPT Paid Subscribers | 20 Million | OpenAI, Apr 2025 |
| Enterprise Business Users | 5 Million | OpenAI, 2025 |
| Annualized Revenue (Dec 2025) | $20 Billion | OpenAI / Bloomberg |
| Revenue (Full Year 2024) | $3.7 Billion | OpenAI financials |
| Revenue in 2020 | $3.5 Million | Public filings |
| Net Loss 2024 | About $5 Billion | OpenAI / WSJ |
| Valuation (Feb 2026) | $730 Billion | Funding round |
| Total Funding Raised | $174+ Billion | PitchBook, Mar 2026 |
| Generative AI Market Share | About 17% | AIPRM, 2025 |
| GPT-1 Parameters (2018) | 117 Million | OpenAI paper |
| GPT-2 Parameters (2019) | 1.5 Billion | OpenAI paper |
| GPT-3 Parameters (2020) | 175 Billion | OpenAI paper |
10. Future Outlook: Toward AGI and Beyond
The Road to Artificial General Intelligence
OpenAI’s stated mission to develop artificial general intelligence is no longer the abstract philosophical aspiration it appeared to be back in 2015. The company has outlined an internal framework for thinking about AGI milestones, describing successive levels of AI capability in terms of increasing autonomy and generality. By 2025, OpenAI’s o-series reasoning models were achieving expert-level performance on scientific, mathematical, and coding benchmarks that would have seemed impossible just five years earlier. One experimental model achieved a perfect score on problems from the International Collegiate Programming Contest. Another won a gold medal at the International Mathematical Olympiad. Internal projections cited in press reporting suggested OpenAI believed it could achieve AGI, broadly defined as a system capable of outperforming human experts across most cognitive tasks, within the current decade.
The Stargate Project and Infrastructure Ambition
In January 2025, President Donald Trump announced the Stargate Project, a joint venture between OpenAI, Oracle, SoftBank, and MGX to build an AI infrastructure system in coordination with the U.S. government. The estimated cost was $500 billion over four years, making it the most ambitious public-private AI infrastructure investment in history. In July 2025, the U.S. Department of Defense awarded OpenAI a $200 million contract for AI applications in military contexts, a development that signaled how far the company had moved from the margins of technology toward its geopolitical center.
The Existential Balance Sheet
OpenAI projects cumulative cash losses of $115 to $143 billion between 2024 and 2029, with profitability not expected until 2029 or 2030. It is simultaneously the world’s fastest-growing major technology company and one of its most prolific cash consumers. The gap is being bridged by an extraordinary and ongoing willingness among the world’s largest investors to fund a company whose product may be transformative enough to justify losses at any historical scale. At the same time, the safety concerns that first animated OpenAI’s founding remain unresolved and have arguably grown more pressing. The departure of Sutskever, Leike, Murati, and others raises genuine questions about whether the organization that once led the field in safety research still has the institutional culture to ensure its most powerful systems are deployed responsibly.
Conclusion: The Company That Remade the World
OpenAI’s story is, at its core, a story about what happens when technological capability races ahead of the human institutions designed to govern it. In just one decade, the organization went from a well-intentioned nonprofit with a $1 billion pledge to a $730 billion corporation sitting at the center of geopolitical competition, legal battles, safety debates, and a global technological transformation with no real historical parallel.
Its achievements are undeniable. ChatGPT made sophisticated AI accessible to hundreds of millions of people for the very first time. The GPT model family pioneered the scaling paradigm that now drives the entire field. DALL-E, Sora, and Codex each created new industries and disrupted existing ones. The Microsoft partnership deployed AI at enterprise scale in ways that genuinely changed how hundreds of millions of people work every day.
Its controversies are equally hard to ignore. The tension between its nonprofit origins and its commercial ambitions produced governance failures that played out in real time in front of a global audience. Safety researchers who helped build the organization left, citing concerns that the mission had been pushed aside in favor of competitive pressure. Copyright holders argued their intellectual work had been used without consent. The very capabilities that made GPT-4 and ChatGPT so transformative, including their fluency, their persuasiveness, and their ability to generate synthetic content at scale, also created risks that are still not fully understood.
What is certain is that the world after OpenAI is a different world from the one before it. The questions that now face humanity, including how to share the benefits of AI fairly, how to govern systems that may exceed human cognitive abilities, how to balance innovation with safety, and how to preserve human agency in an increasingly automated world, are questions that OpenAI forced into mainstream consciousness. Whatever history ultimately decides about the organization’s choices, its importance to the story of technology and to the shape of the 21st century is already secure.
The company that once promised to benefit all of humanity, free from the pressure of profit, now sits at the apex of global capitalism. Whether it can honor both commitments at once, and whether any organization realistically could, remains the defining question of the AI age.
So, this was the BigStory of OpenAI, the lab that started with a bold promise to benefit all of humanity and went on to reshape artificial intelligence, business, and the way the world thinks about the future. At BigStories, our goal is to bring you the journeys behind the companies and ideas that are building the modern era. If you enjoyed reading this story, consider sharing it with others who use ChatGPT or follow the AI revolution every day, and explore more BigStories that reveal how today’s world is truly being built.
Sources: Wikipedia (OpenAI), Britannica, AIPRM, PitchBook, The Washington Post, NPR, TIME, CNBC, HISTORY.com, Search Engine Journal, VisualCapitalist. Data current as of March 2026.
