AI & Machine Learning

Visa is launching an AI Financial Assistant for banking apps: What cardholders should know

MARA's AI Pivot: The Most Undervalued 4.8GW Bet In Neocloud

AI has not triggered DeFi ‘hackpocalypse,’ Dragonfly partner says

Total value stolen and median hack size are declining compared to 2025, signaling that the AI hacking apocalypse was a false alarm, argued Dragonfly managing partner Haseeb Qureshi.

The US-China AI arms race has taken an unexpected turn

Powerful artificial intelligence models built by Chinese companies have gone from inducing widespread panic to being met with a shrug of the shoulders – what changed?

'It works unlimited hours': New AI super-assistant sits on your desk, has a 'mind and voice' – and handles everything from email to coding

Autonomous' Intern 2 promises to take on the work of a small creative team.

Reflection AI Secures Over $1 Billion in Nvidia Compute From Nebius

Reflection AI has signed a computing deal worth more than $1 billion with Nebius, securing Nvidia GPU access through 2029, just weeks after committing up to $6.3 billion to SpaceX for the same purpose. The open-source AI startup, founded by ex-DeepMind researchers, is locking in compute at a scale that outpaces its product releases so far.

STAT+: Drug metabolism AI competition results show that bigger may not always be better

The results of a recent AI competition show that better data trumps bigger models when it comes to predicting properties of drug candidates.

The Chatbot That Foretold Why People Share Secrets With ChatGPT

In the 1960s an MIT professor named Joseph Weizenbaum created a chatbot called ELIZA. The conversations people had with it set precedents for the chatbots to come.

Lower-skilled workers could earn more in an AI world, research indicates

For anyone worried about AI's effects on jobs, here's good news: New research by Stanford economist Lukas Althoff concludes that artificial intelligence is likely to reshape jobs rather than eliminate entire occupations and will ultimately increase wages for all workers—with the biggest gains benefiting lower-skilled workers.

From AI curiosity to operational transformation: Michael Vandi on how mortgage lending’s relationship with AI is evolving

Addy AI CEO Michael Vandi discusses mortgage AI adoption, enterprise AI mortgage strategies and why trust is shaping the future of lending.

12 Ways to Reduce LLM Latency and Inference Costs in Production

Scaling LLMs isn’t about adding GPUs. It’s about removing wasted work from every request.

DOGE Used AI for Housing Policy. The Government Won’t Say How

In response to a public records request, HUD has withheld documents about DOGE’s use of AI—in part by citing a privilege that doesn’t exist.

AI Inference Costs Are Quietly Eating SaaS Gross Margins

AI inference costs are the hidden line item turning great LLM features into shrinking SaaS gross margins, because unlike traditional software, every query costs money and scales linearly with usage instead of flattening out. This piece breaks down why the unit economics break and the concrete levers, caching, model routing, quantization, and batching, that founders can pull before a board asks why margin dropped.

Demis Hassabis proposes a US-based Standards Body for "Frontier-class" AI, modeled after FINRA; labs would share models for review up to 30 days before release (Demis Hassabis/@demishassabis)

Demis Hassabis / @demishassabis: Demis Hassabis proposes a US-based Standards Body for “Frontier-class” AI, modeled after FINRA; labs would share models for review up to 30 days before release — This is a pivotal moment in human history. Artificial General Intelligence (AGI), a system that exhibits …

AI Is Creating More Opportunities Than We Realize

The biggest fear around AI is simple: If two people with AI can do the work of ten people, what happens to the other eight? It is a valid concern. AI will automate tasks. Some teams will shrink. Some skills will lose value. But almost every discussion about AI replacing jobs makes one major assumption: The amount of software the world wants to build will remain constant. I don't think it will. When building becomes dramatically cheaper, we don't just build the same things with fewer people. We build more things. Imagine that building a software company previously required ten people. Today, AI coding agents, cloud platforms, automation, and AI-powered support might allow two or three people to build the same product. The obvious conclusion is: eight jobs disappeared. But consider another possibility. What if the founder who could previously afford to build one startup can now launch four products in parallel? Before: 1 startup × 10 people = 10 opportunities After: 4 startups × 2–3 people = 8–12 opportunities The individual companies became smaller, but the number of companies, products, and experiments increased. We are already seeing early signs of this. In 2026, the startup JustPaid reportedly created a team of seven AI coding agents using OpenClaw and Claude Code. In one month, those agents built 10 major features. Instead of eliminating every human role, the company redirected people toward higher-priority customer work and even hired a new developer who was trained largely by the AI agents. AI doesn't only reduce the number of people required to build something. It increases the number of things people can afford to build. Before cloud computing, companies needed people to buy and install servers, manage operating systems, configure networks, maintain storage and backups, provision infrastructure, and operate physical data centers. Cloud computing automated or eliminated many of these responsibilities. But the technology industry did not disappear. Instead, we

UiPath: The Odds Of Agentic AI Are Not Priced In