mlbwhf@yahoo.com Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced.…
Source: MIT Technology Review
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This week, I covered the story of Casey Harrell—a man with ALS who is “the first power user” of a brain implant, according to the researchers who worked with him. Harrell is paralyzed and unable to speak coherently without the device. He has now spent…
Source: MIT Technology Review
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arXiv:2606.10616v4 Announce Type: replace Abstract: Long-horizon language agents accumulate observations, reasoning traces, and retrieved facts exceeding context windows, making memory retention a fundamental resource-allocation problem. Existing systems treat retention as local and do not model long-term consequences under observability constraints. To fill this gap, we…
Source: cs.AI updates on arXiv.org
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arXiv:2606.20457v1 Announce Type: cross Abstract: Classifier guidance is a way to control diffusion generation by using a noise-conditioned classifier to steer the sampling process toward a target class. One drawback of classifier guidance is that it requires two separately trained models: a classifier and a…
Source: cs.AI updates on arXiv.org
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arXiv:2510.27568v2 Announce Type: replace Abstract: Solving mathematical reasoning problems requires not only accurate access to relevant knowledge but also careful, multi-step thinking. However, current retrieval-augmented models often rely on a single perspective, follow inflexible search strategies, and struggle to effectively combine information from multiple sources.…
Source: cs.AI updates on arXiv.org
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OpenAI introduces new spend controls and usage analytics for ChatGPT Enterprise, helping organizations manage costs and scale AI with confidence.
Source: OpenAI News
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Enterprise AI has crossed from experiment to infrastructure — but the gap between adopting AI and profiting from it has never been wider. Here is where things actually stand, drawn from the most authoritative industry data and updated as new figures land.
Last updated: June 2026. Every statistic below links to its primary source.
88% of organizations report using AI in at least one business function, up from 78% a year earlier — with two-thirds now using it across multiple functions (McKinsey, The State of AI, November 2025). For context, Stanford’s data shows organizational AI use jumped from just 55% in 2023 to 78% in 2024, and generative-AI use in at least one function more than doubled — from 33% to 71% — over the same period (Stanford HAI, AI Index Report 2025).
Adoption hasn’t translated into broad financial impact. Only 39% of respondents attribute any EBIT impact to their AI use, and among those, most say AI accounts for less than 5% of EBIT. A small group of “AI high performers” — roughly 6% of organizations — is pulling away by rewiring workflows and governance around AI rather than bolting it on (McKinsey, The State of AI, November 2025).
The gap between AI adoption and AI impact has never been wider — most organizations are still experimenting without capturing meaningful enterprise-level value.
2025–26 is the year agentic AI moved from concept to deployment. 23% of organizations report scaling an agentic AI system somewhere in the enterprise, and another 39% are experimenting with AI agents — a combined ~62% already engaged (McKinsey, The State of AI, November 2025). Governance remains the leading concern as agents gain the ability to take actions, not just generate text.
Corporate AI investment reached $252.3 billion in 2024, a 26% increase over 2023, while the number of newly funded generative-AI startups nearly tripled (Stanford HAI, AI Index Report 2025). The money is following capability — and increasingly, the enterprise.
One number explains much of the adoption curve: the cost of AI inference fell roughly 280-fold in under two years — from about $20 per million tokens in late 2022 to around $0.07 by late 2024 (Stanford HAI, AI Index Report 2025). What was once a budget-line question is now, for many use cases, a rounding error.
By mimicking how the brain operates, neuromorphic computing can use dramatically less energy than conventional electronic AI chips. However, even the most sophisticated neuromorphic devices today are still quite simple, using only a small fraction of the number of connections found in human neurons. Now,…
Source: IEEE Spectrum
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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The search for dark matter has been blown wide open For decades, physicists have hunted for weakly interacting massive particles (WIMPs),…
Source: MIT Technology Review
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Learn how GPT-5.5 Instant improves ChatGPT’s health and wellness responses with stronger reasoning, better context, clearer communication, and physician-informed evaluations.
Source: OpenAI News
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