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Big Tech and VCs Pour Billions into World Models to Make Physical AI a Reality
The race to build AI that understands physical space is drawing record levels of funding and talent. Go to Source
How UnitedHealth Group sets its AI stack, targets 1,000 use cases | Constellation Research
UnitedHealth Group has more than 1,000 AI use cases in play, key technology partnerships and a solid governance framework. The bet: AI returns on individual use cases can add up to millions of dollars and ultimately billions of dollars that UnitedHealth's scale. Go to...
Mark Cuban says AI is harder than anyone admits — and that gap is where you build : r/AgentsOfAI
His argument: if AI were actually "done," you wouldn't see Microsoft hiring 6,000 people. You wouldn't see Anthropic and OpenAI deploying forward-deployed engineers to enterprise clients. The fact that they need humans to implement it tells you AI is hard....
Stop Overengineering Your Agent Harness
The conversation around harness engineering is dominated by problems from coding and personal agents such as OpenClaw, but most agents are simpler. Builders should avoid over-engineering for capabilities that newer models may absorb anyway, the “Kirby effect”, and...
Yohei Nakajima: The Next AI Agent Breakthrough Isn’t a Better Model — It’s an Immutable Log — BigGo Finance
Yohei Nakajima, creator of BabyAGI and a venture capitalist at Untapped Capital, is making a provocative bet: the future of autonomous AI agents is not about building smarter models, but about giving agents an unforgeable diary. In a recent interview, he detailed...
Agentic AI in supply chain: automate order management | McKinsey
Agentic AI could fundamentally reshape supply chain operations, but realizing its potential will require companies to move beyond isolated use cases and orchestrate work across people, processes, and technology. Go to Source
Model Context Protocol prepares to break with its stateful past
On July 28, MCP's maintainers plan to finalize the protocol's 2026-07-28 revision, bringing many changes – including some that aren't backward compatible – that reflect some "hard lessons" the core MCP team learned over the past two years, said Anthropic...
Managing AI Agents Is An HR Problem Wearing An Engineering Badge
The most consequential addition to your engineering team isn't a hire you'll make next year. It's already here, and it isn't a person. It's a team of AI agents, and the humans in charge of them need a skill set that most computer science programs still aren't...
The Operating Model Is the Real AI Harness
When generative AI first entered the enterprise, almost every conversation revolved around models. Which model was smartest? Which benchmark mattered? Which provider was winning? Capability was treated as though it lived inside the model itself, so choosing the right...
Atlassian: Research shows organizations should approach AI at the team level, not the individual level, to achieve true ROI | VentureBeat
Most companies are approaching AI adoption backwards by optimizing how individuals use AI instead of how teams work together, said Dr. Molly Sands, head of the Teamwork Lab at Atlassian, during a fireside chat with VentureBeat senior technology contributor Sam...
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Recent Briefings
Open Models, Technology Diffusion & the Shift from Renting to Owning AI
The launch of the Chinese AI model Kimi K3 challenges the winner-take-all strategy of the frontier model providers OpenAI and Anthropic, but it also has wider implications for enterprises currently building out their AI stacks. Kimi 3 is a 2.8T parameter Mixture of...
The Token Apocalypse & Agentic Ecosystem Design
The path towards reliable agentic AI infrastructure in the enterprise continues to offer up new surprises, and the recent panic around token costs is a case in point that has focused minds on the cost-benefit analysis of different models. But perhaps model choice is...
Context, Codification & Cognitive Capabilities
Context & Codification are still hard problems A lot of enterprise AI investment has gone into models and prompt quality. But there is a growing body of evidence that points to the main constraints on reliability, cost, and organisational capability still being...
Don’t Outsource Agentic Capability Design
For the past couple of weeks, I have been interviewing senior function heads and operational leaders in a large hi-tech firm as part of an AI literacy programme. What struck me most was not their enthusiasm for AI, but the degree to which they already know what they...
Agents are Easy; the Agentic Enterprise is Not
Agentic AI is showing great promise in coding and personal productivity, but the shift from personal to organisational agent usage in the enterprise is harder and more complicated than it looks. It also heralds a profound shift in what we consider to be ‘work’ and...
CHROs as Systems Architects not Programme Owners
Enterprise AI-led transformation is changing the focus of most leadership roles to a greater or lesser extent, but one of the most impacted is likely to be the HR function and CHRO roles in particular. CHROs are being asked to lead AI transformation, ensuring...
Embrace the Human to Overcome the AI Capability Absorption Gap
The Capability Absorption Gap in enterprise AI is widening, not narrowing, as model and tool development outstrips the ability of incumbent business leaders to adapt to what it makes possible. Adoption programmes are not cutting it, and their focus on getting people...
Agents at the Ready? Yes and No…
Agentic AI capabilities are developing within several pace layers at once - economic, infrastructure, capability readiness, and knowledge engineering - and it is getting harder to stay on top of these developments whilst tracking their interdependence. But...
Agents of Progress or Agents of Chaos?
The OpenClaw moment we covered a few weeks ago was a wild ride. But in the age of YOLO, Hodl and r/wallstreetbets, it should come as no surprise that there is an apparently limitless supply of people willing to hand over control of their personal computer to AI agents...
Agents on the Night Shift
Andrej Karpathy’s new autoresearch tool recently ran 700 experiments on his nanochat codebase in two days. It found 20 improvements he had missed, delivering an 11% uplift in output. Tobi Lütke at Shopify tried it on his own hand-tuned model: 19% improvement,...