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On 8 June 2026 OpenAI confirmed it had filed confidentially for an IPO with the US SEC, against a 2025 operating loss of $20.92 billion and annualised revenue above $20 billion. The listing was later pushed to 2027.
A jury in Oakland ruled for OpenAI, Sam Altman, Greg Brockman and Microsoft on 18 May 2026, finding that Elon Musk's claims were filed too late under the three-year statute of limitations. Musk says he will appeal.
On 6 November 2025 Mustafa Suleyman announced the MAI Superintelligence Team inside Microsoft AI. Its stated goal is "humanist superintelligence": problem-oriented AI with limits on autonomy that keeps humans in control.
On 30 June 2025 Mark Zuckerberg announced Meta Superintelligence Labs (MSL), combining several AI teams under Alexandr Wang. Its first model, Muse Spark, was released on 8 April 2026.
The first International AI Safety Report, published on 29 January 2025, was led by Yoshua Bengio and written by over 100 experts with backing from 30 countries; it sets out the science of general-purpose AI capabilities, risks and mitigations without making policy recommendations.
In early 2025, 01.AI denied reports that it was selling its pre-training team to Alibaba; large-model training moved into a joint lab with Alibaba, while 01.AI focused on smaller, cheaper models and enterprise platforms built around DeepSeek.
In September 2024 the Hugging Face Hub passed 1 million downloadable public models for the first time; by October 2025 it held more than 2 million.
On August 13, 2024, OpenAI released SWE-bench Verified, a 500-sample human-validated subset of SWE-bench built with the benchmark's original authors from Princeton, after filtering ambiguous tasks and unreliable tests.
On 19 June 2024 Ilya Sutskever announced Safe Superintelligence Inc. on X, a company with one stated goal: building safe superintelligence. It raised $1 billion in September 2024.
An IMF staff analysis published in January 2024 estimated that almost 40 percent of global employment is exposed to AI, with effects uneven across income levels and a risk of widening inequality.
On 12 July 2023 Elon Musk publicly announced xAI, a company he had incorporated in March 2023 with a founding team of eleven to build what he called a maximally truth-seeking AI.
In July 2023 OpenAI created a team co-led by Ilya Sutskever and Jan Leike to solve superintelligence alignment within four years, committing 20% of its compute and proposing AI systems to help supervise AI.
On 20 April 2023 Google announced that its Brain team and DeepMind would merge into one unit, Google DeepMind, led by Demis Hassabis. Google said the goal was to speed up its AI progress and build capable systems safely and responsibly.
A Goldman Sachs research note published in March 2023 estimated that generative AI could expose the equivalent of 300 million full-time jobs to automation and add 7% to global GDP over a decade.
On 28 July 2022, DeepMind and EMBL-EBI expanded the AlphaFold Protein Structure Database from nearly 1 million to over 200 million predicted protein structures, covering nearly all catalogued proteins known to science.
On 11 March 2019 OpenAI announced OpenAI LP, a "capped-profit" company that let it raise outside capital while limiting investor returns. The structure lasted until October 2025.
In December 2015 a group of entrepreneurs and AI researchers founded OpenAI in San Francisco as a non-profit research company whose stated aim was to advance digital intelligence for humanity's benefit rather than financial return.
In 2011 Microsoft Research reported that modelling thousands of senones with deep neural networks, trained on GPUs, sharply cut word errors compared with conventional Gaussian-mixture systems in large-vocabulary speech recognition.
In 2011 ACM gave Judea Pearl the A.M. Turing Award for a calculus of probabilistic and causal reasoning, work that began with his invention of Bayesian networks and later produced the do-calculus for causal inference.
In 2007 IBM Research began building a computer that could take on Jeopardy! champions. The project produced the DeepQA architecture and Watson, which beat the show's two top-ranked players in 2011.
Geoffrey Hinton dated the current popularity of "deep learning" to a 2006 paper on deep belief nets, but the same words had earlier uses, including Rina Dechter's work in 1986.
