Four years into the generative AI revolution, consulting giant McKinsey reckons we’ve finally started the engine and are officially “on the road to ROI.” Whether that road leads to actual profit-making and how long it takes to travel is anyone’s guess, because the firm’s data suggests most respondents still aren’t reporting an enterprise-level earnings contribution from AI.

McKinsey surveyed 1,719 professionals and business leaders from around the world and across industries for its report on the State of AI in 2026, and what it found sounds a lot like what similar studies have determined in the past couple of years. According to the report, more businesses are deploying more AI in the belief that their investments will start paying off, but the number of people reporting an actual earnings boost from their AI initiatives has remained flat.  — Read More

Few human creations in the world get more hate than data centers. In the US, retaliatory action is now bipartisan, meaning everyone seems to be against them.

Nonetheless, a Reuters/Ipsos poll found that just 14% of Americans supported a data center in their community, compared with 36% who approved of US strikes against Iran.

— Because it raises electricity bills.
— Because it wastes water like there’s no tomorrow.
— Because they are noisy..

Choose your preferred reason to hate; all are valid these days. Whether they are accurate or not is another thing. — Read More

Three AI pioneers—Geoffrey Hinton, Fei-Fei Li, and Andrew Ng—debated AI’s future at Ai4 2026, revealing deep divisions despite agreeing on its transformative power. Hinton warned of AI’s rapidly advancing dangerous capabilities and significant white-collar job displacement, citing call centers as vulnerable. He views regulation as a crucial “steering wheel” and is wary of open-weight models’ misuse. Ng countered, arguing AI redefines job tasks rather than eliminating roles, and accused large companies of exaggerating threats to stifle competition, advocating for open models to prevent gatekeeping. Li called for a nuanced discussion, focusing on task transformation over job replacement, emphasizing that productivity gains don’t automatically lead to shared prosperity. She urged sector-specific regulation and public investment, cautioning against fear-mongering that paralyzes innovation. Their divergent views highlight a field entering a complex new phase regarding risks, benefits, and societal impact. — Read More

#strategy

As companies mature in their AI deployment and double down on their previous investments into the technology, pressure on executives has reached a fever pitch

Early 2026 was the era of tokenmaxxing, or ramping up use of AI compute units as much as possible to appear productive. But momentum from AI providers to transition from flat-rate subscriptions to consumption-based pricing has ramped up in the last few months.

… The “use-AI-for-everything” mindset many companies adopted in 2025 and into 2026 now bears a much larger price tag.   — Read More

Nearly four years after the generative AI wave began, enterprise deployments have settled into a familiar pattern: models are capable, pilots are convincing, but production rollouts plateau on accuracy, drift on outputs, and fail to compound productivity gains the way anyone expected. The diagnosis is increasingly consistent: The bottleneck is the context layer.

… Managing Director Lonne Jaffe sat down with Prukalpa Sankar, cofounder and co-CEO of Atlan, to work through the fundamentals: What context actually means in an enterprise setting, what it takes to build a layer that AI agents can reliably consume, and why the hardest part of all may have nothing to do with technology. — Read More

Thinking Machines released a model called Inkling on July 15, 2026. .. Before Inkling, the company shipped Tinker, a service for fine-tuning open models [4]. Inkling is the company’s first model trained from scratch [1]. The weights sit on Hugging Face under an Apache 2.0 license [2], so anyone can download them and retrain the model on their own data.

In this article, we will work through the various choices Thinking Machines made while building Inkling.  — Read More

In 1975, Fred Brooks’s The Mythical Man-Month warned us that adding individuals to a late project makes that project even later rather than speeding it up. However well-understood this is, it remains extremely common for leaders to repeat this mistake, even today. My personal theory is that most leaders performing the “ask for more staffing” gambit intuitively know they are optimizing to “get stuck” in a way that shifts responsibility off them because they didn’t get the requested funding, rather than that there is no way to accomplish the task at hand.

Even if teams avoid the simplest versions of the mythical man-month’s trap, organizations that operate through strict ownership of defined boundaries introduce the same problem. Well, of course your team can’t launch that without approval from the architecture team and the security team and performing a user research study. … Even with strong AI-enhanced workflows, I still find human review of load-bearing technical decisions (e.g. a decision that will meaningfully impact subsequent decisions) to be extremely valuable.  — Read More

Inside the labs building the next generation of AI, a phrase has been gaining weight: world models. A large language model like ChatGPT, Claude, or Gemini predicts what comes next in text. World models, in contrast, learn dynamics from observation, then simulate forward to test what happens next. They model the world itself, rather than just descriptions. 

Yann LeCun, who left Meta in late 2025 to launch Advanced Machine Intelligence Labs, has built his research program around it. Demis Hassabis, who runs Google DeepMind, has made world models central to its push toward more general AI. Sam Altman has called OpenAI’s Sora a world simulator, a claim that is contested. Fei-Fei Li raised a billion dollars for her company World Labs to pursue what she calls “spatial intelligence.” Jensen Huang, meanwhile, is building the simulation platforms and compute behind the next wave of AI, as NVIDIA did for large language models. — Read More

Optimize your homepage for AI traffic by treating it as the front door for a visitor who already knows what you do, not one who’s hearing about you for the first time. AI systems cite your deep pages, but the click almost always lands on the homepage instead, so most sites are handing their best new traffic to the one page still built for a stranger.

That mismatch in how people find and visit websites has gotten worse over the past year, and it did not happen gradually. Average monthly web visits across generative AI platforms worldwide reached 9.5 billion between June 2025 and May 2026, up 70% year over year, according to Similarweb’s 2026 Generative AI Landscape report. — Read More