… AI will either be the greatest equalizer ever invented, or the worst source of injustice. The challenge is monumental. Even under the best circumstances, the transition to this new AI era will be one of the most turbulent times in human history. How will we use this technology to make the world a fairer place and keep it from widening the divide between rich and poor? How will we protect the people who are most vulnerable to the harms caused by artificial intelligence, including those who lose their livelihoods and the sense that they are in control of their future?
I believe that answering these questions and acting on the answers should be the world’s top priority. If the world takes the right steps AI will be a force for good and leave everyone better off. — Read More
Recent Updates
Strategy in the age of AI – Eight points beyond the obvious
What happens when AI changes not only how companies compete, but the very game they are competing in?
[V]iewing AI through the traditional mechanisation/automation lens traps us in looking for operational benefits (much of the discourse today), when the real advantage lies in understanding its impact on the rules of competition. It changes where scarcity lies, how industries are organized, and what constitutes a defensible advantage.
As AI makes intelligence within individual modules abundant, advantage increasingly moves to those who can connect activities, learn across their boundaries, and improve the whole system together.
Vertical integration may therefore be returning, but in a new form. The prize is no longer necessarily ownership of every asset. It is ownership of the feedback loops that connect them. — Read More
SaaS Isn’t Dead. Sameness Is.
Everyone suddenly wants to tell you SaaS is dead.
Even Gartner now estimates that as much as $234 billion in enterprise application spending could be exposed to what it calls “agentic arbitrage” by 2030, roughly 20 percent of enterprise SaaS spending.
But Gartner itself hedges the apocalypse. It calls what is coming less an apocalypse than a metamorphosis.
I think that’s right.
The next SaaS company may run one service underneath ten thousand different applications. — Read More
How to evaluate LLMs before production
A language model can perform well on a clean benchmark and still struggle with the cases that matter in production.
But as a system moves closer to production, the evaluation problem changes.
Real inputs are often ambiguous. Labels may be inconsistent. Important context may be missing or truncated.
We encountered these challenges while evaluating an LLM-based system designed to reduce false positives in GitHub secret scanning.
Rather than determine whether an LLM could classify a string correctly, we needed to understand whether the system could reduce noisy alerts while preserving enough recall to remain safe for a security workflow. — Read More
Anonymous Ox Alpha processes 26T tokens on OpenCode, breaks OpenRouter launch record
Dax Raad (@thdxr)’s OpenCode said its users processed 26 trillion tokens through Ox Alpha during the anonymous AI model’s first four days, turning a free preview into one of the largest model trials on the coding agent.
The August 24th disclosure covered 327,000 unique users and 8,328,244 completed sessions, according to OpenCode’s usage dashboard. Ox Alpha ranked second among models tracked by OpenCode, behind DeepSeek V4 Flash at 33 trillion tokens and ahead of Xiaomi’s MiMo-V2.5 at 12 trillion. — Read More
OpenAI’s Jalapeño chip is built for fast inference at scale, benchmarks show
At the Hot Chips conference on Tuesday, OpenAI shared a more detailed look at Jalapeño, including the first batch of benchmark results for the new system. Tested on SemiAnalysis’ InferenceX benchmark, Jalapeño registered both more tokens per user and more throughput per kilowatt than the currently available state-of-the-art inference processors.
“The bottom line is that the results show a very, very significant performance advance over state of the art,” said Richard Ho, OpenAI’s head of hardware, in a press call. — Read More
McKinsey says enterprise AI is finally ‘on the road to ROI’
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
The Truth & Lies of Data Centers
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
AI Chip Architectures
At the 2018 International Symposium on Computer Architecture, John Hennessy and David Patterson delivered their Turing Lecture: “A New Golden Age for Computer Architecture”. … The closing prediction: “the next decade will see a Cambrian explosion of novel computer architectures.”
This prediction came true. Today, we now have dozens of architectures in serious development. GPUs, TPUs, LPUs, NPUs, DPUs, ASICs, wafer-scale engines, reconfigurable dataflow, neuromorphic, photonic, analog. Particularly, these architectures focus on compute for AI.
… This post aims to survey these varying approaches – their philosophy, architecture, scaling methods (scale-up and scale-out), and software stack (how you program the chip). — Read More
Three AI Pioneers Clash Over Jobs, Regulation And The Future Of AI
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
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