The Industry A.I. Is Ready to Crush Hollywood as We've Known It Generative video tools are ready to flood the market with robot actors and content--leaving studios and actors scrambling to catch up. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Nitish_Pahwa newsletter. You can manage your newsletter subscriptions at any time.

OpenAI's latest app encourages users to generate a personal digital avatar and scroll AI-generated videos of themselves and their friends. On Tuesday, OpenAI released an AI video app called Sora . The platform is powered by OpenAI's latest video generation model, Sora 2, and revolves around a TikTok-like For You page of user-generated clips. This is the first product release from OpenAI that adds AI-generated sounds to videos. For now, it's available only on iOS and requires an invite code to join.

Thinking Machines Lab, led by a group of prominent former OpenAI researchers, is betting that fine-tuning cutting-edge models will be the next frontier in AI. Thinking Machines Lab, a heavily funded startup cofounded by prominent researchers from OpenAI, has revealed its first product--a tool called Tinker that automates the creation of custom frontier AI models. "We believe [Tinker] will help empower researchers and developers to experiment with models and will make frontier capabilities much more accessible to all people," said Mira Murati, cofounder and CEO of Thinking Machines, in an interview with WIRED ahead of the announcement. Big companies and academic labs already fine-tune open source AI models to create new variants that are optimized for specific tasks, like solving math problems, drafting legal agreements, or answering medical questions. Typically, this work involves acquiring and managing clusters of GPUs and using various software tools to ensure that large-scale training runs are stable and efficient.

Are you sure that chatbot isn't alive? Listen to more stories on the Noa app. O ne of the persistent questions in our brave new world of generative AI: If a chatbot is conversant like a person, if it reasons and behaves like one, then is it possibly conscious like a person? Geoffrey Hinton, a recent Nobel Prize winner and one of the so-called godfathers of AI, told the journalist Andrew Marr earlier this year that AI has become so advanced and adept at reasoning that "we're now creating beings." Hinton links an AI's ability to "think" and act on behalf of a person to consciousness: The difference between the organic neurons in our head and the synthetic neural networks of a chatbot is effectively meaningless, he said: "They are alien intelligences."

Unlocking AI's full potential requires operational excellence For successful AI adoption, leaders need to focus on structure rather than speed. Talk of AI is inescapable. A record 58% of S&P 500 companies mentioned AI in their second-quarter earnings calls, according to Goldman Sachs. But it's difficult to walk the talk. Just 5% of generative AI pilots are driving measurable profit-and-loss impact, according to a recent MIT study . That means 95% of generative AI pilots are realizing zero return, despite significant attention and investment.

The Download: OpenAI's caste bias problem, and how AI videos are made Plus: Taiwan has pushed back against America's chip request OpenAI is huge in India. Its models are steeped in caste bias. Caste bias is rampant in OpenAI's products, including ChatGPT, according to an MIT Technology Review investigation. Though CEO Sam Altman boasted about India being its second-largest market during the launch of GPT-5 in August, we found that both this new model, which now powers ChatGPT, as well as Sora, OpenAI's text-to-video generator, exhibit caste bias. This risks entrenching discriminatory views in ways that are currently going unaddressed. Mitigating caste bias in AI models is more pressing than ever.

James Anderson says he had not seen signs of an investment bubble in AI until recently. James Anderson says he had not seen signs of an investment bubble in AI until recently. Leading UK tech investor warns of'disconcerting' signs of AI stock bubble Wed 1 Oct 2025 07.07 EDTFirst published on Wed 1 Oct 2025 06.22 EDT A leading British tech investor has described soaring valuations of artificial intelligence companies as "disconcerting", amid concerns of an AI stock market bubble. James Anderson was an early backer of Tesla, Amazon and China's Tencent and Alibaba, generating vast returns for Baillie Gifford's flagship fund. Now at the Italian investment company Lingotto, Anderson said he had not seen signs of an investment bubble until recently, when the ChatGPT developer, OpenAI, and its rival Anthropic announced hefty valuation increases.

When Dhiraj Singha began applying for postdoctoral sociology fellowships in Bengaluru, India, in March, he wanted to make sure the English in his application was pitch-perfect. So he turned to ChatGPT. He was surprised to see that in addition to smoothing out his language, it changed his identity--swapping out his surname for "Sharma," which is associated with privileged high-caste Indians. Though his application did not mention his last name, the chatbot apparently interpreted the "s" in his email address as Sharma rather than Singha, which signals someone from the caste-oppressed Dalits. "The experience [of AI] actually mirrored society," Singha says.

With advances in generative AI, decision-making agents can now dynamically create new actions during online learning, but action generation typically incurs costs that must be balanced against potential benefits. We study an online learning problem where an agent can generate new actions at any time step by paying a one-time cost, with these actions becoming permanently available for future use. The challenge lies in learning the optimal sequence of two-fold decisions: which action to take and when to generate new ones, further complicated by the triangular tradeoffs among exploitation, exploration and $\textit{creation}$. To solve this problem, we propose a doubly-optimistic algorithm that employs Lower Confidence Bounds (LCB) for action selection and Upper Confidence Bounds (UCB) for action generation. Empirical evaluation on healthcare question-answering datasets demonstrates that our approach achieves favorable generation-quality tradeoffs compared to baseline strategies. From theoretical perspectives, we prove that our algorithm achieves the optimal regret of $O(T^{\frac{d}{d+2}}d^{\frac{d}{d+2}} + d\sqrt{T\log T})$, providing the first sublinear regret bound for online learning with expanding action spaces.

While the technologies empowering malicious audio deepfakes have dramatically evolved in recent years due to generative AI advances, the same cannot be said of global research into spoofing (deepfake) countermeasures. This paper highlights how current deepfake datasets and research methodologies led to systems that failed to generalize to real world application. The main reason is due to the difference between raw deepfake audio, and deepfake audio that has been presented through a communication channel, e.g. by phone. We propose a new framework for data creation and research methodology, allowing for the development of spoofing countermeasures that would be more effective in real-world scenarios. By following the guidelines outlined here we improved deepfake detection accuracy by 39% in more robust and realistic lab setups, and by 57% on a real-world benchmark. We also demonstrate how improvement in datasets would have a bigger impact on deepfake detection accuracy than the choice of larger SOTA models would over smaller models; that is, it would be more important for the scientific community to make greater investment on comprehensive data collection programs than to simply train larger models with higher computational demands.