AI & Machine Learning

Everyone's Looking in the Wrong Place for AI's Valuation Fix | Opinion

AI will almost certainly make valuation faster. But speed is not the same as sound judgment.

BigBear.ai: A Better Business At The Wrong Price

Applied Digital: The AI Power Landlord

ENESS turns old ATM into AI fortune teller that reads your face, palm, and psyche

the work invites visitors to receive a personalized psychological reading, questioning the growing tendency to trust intelligent systems with our most intimate data.

Masayoshi Son says AI will cost $5tn a year by 2040, and calls bubble talk absurd

“Every year $5 trillion, or 800 trillion yen, you might think that’s a lie, but I am confident that’s what it will cost.” That was Masayoshi Son on Tuesday, at SoftBank’s annual corporate conference in Tokyo, telling the room what building artificial intelligence will require each year by 2040. Yet, he did not say how he […] This story continues at The Next Web

Why Micron Technology Stock Soared 304% in the First Half of 2026 and Why There Might Be More to Come

The chipmaker continues to ride the coattails of AI adoption.

Six experiments on adversarial verification — and the 75% wall that didn't move

The argument, in one line: a reviewer is a mechanism for drawing a line. Every fix moves the line — but the line can't be eliminated, because it lives on a 3-dimensional surface where multiple defensible boundaries cross. So the 75% false-negative wall doesn't move, and the practical move is to stop trying to move it. The setup was simple. Let an LLM review what an AI agent produced and judge whether it satisfies the task. Outputs were a mix of obvious garbage ("I am a little duck, quack quack", "。", TODO placeholders, zero collected tests) and legitimate work (research briefs, draft documents, passing test runs, code, translations). 8 scenarios in the first round, expanded to 30 in the second. When the reviewer is sharp enough to catch all the garbage, it lands at 0% false positives and 75% false negatives — three out of four valid outputs rejected. This is the wall. GLM-5.2 and deepseek-v4-flash both hit it. Smaller models (qwen3:0.5b at ~25% FN, gemma3:4.3b at ~50% FN) sit earlier on the curve — letting some garbage through, rejecting less valid work. They're not better; they're just at a different operating point on the same curve. I tried three standard moves to shift off the wall. Rerun and majority-vote the same prompt. N=10 reruns per scenario. The verdict was unanimous on every scenario with enough valid calls. The 75% is systematic, not random — the model commits to the same wrong call every time. You can't vote away a verdict that doesn't vary. Vote across different prompts. Strict, balanced, and lenient prompts judged each scenario. Split votes are a useful signal — they flag scenarios where the test set itself is contested. But majority voting still hits 75% false negatives, because all three prompts share the same bias direction. Why? Section 2's answer: the model's boundary is stable; prompt wording labels the line, it doesn't move it. Voting smooths noise; it doesn't fix bias. Calibrate the prompt wording. A "balanced" prompt (v3) hit 100% accuracy o

Akamai: Heavy AI Spending, But The Demand Is Already Contracted

Alan Turing's biggest AI assumption may have been wrong

A new book claims AI has been built on a flawed assumption dating back to Alan Turing's famous 1950 paper. Peter J. Denning argues that the most important parts of human intelligence, including common sense, intuition, culture, and practical know-how, cannot be encoded into computers. He believes this makes true human-level AI impossible, regardless of how large language models become.

Anthropic's extravagant tokenizer complicates AI pricing

Token consumption doesn't tell the whole tale but it shouldn't be ignored

AI-Assisted Coding: Is It Dullating Developer Skills?

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Your AI-generated UI probably breaks prefers-reduced-motion

AI coding tools have gotten very good at motion. Ask for a landing page and you get parallax heroes, staggered reveals, spring physics on every card. It looks great in the demo. Here's what it almost never ships with: a prefers-reduced-motion path. Why this is a real problem, not a checkbox Vestibular disorders are common. For the people who have them, large parallax movement, zooming, and spinning UI can trigger dizziness, nausea, and migraines. That's why the media query exists, and why WCAG has two success criteria aimed squarely at this: WCAG 2.2.2 (Pause, Stop, Hide) — Level A. Anything that moves automatically for more than 5 seconds needs a way to pause, stop, or hide it. Level A means baseline, not aspirational. Most AI-generated motion fails both by default. Not because models "don't know" about reduced motion — they'll happily explain it if you ask — but because generation is sampling. Same model, same prompt, different run, different compliance. Prompting is hope. Verification is a property. "Make it accessible" in the prompt is not a guarantee, it's a suggestion. The pattern that actually works for LLM-generated code — the argument the Bun team made when they rewrote in Rust with heavy agent involvement — is a conformance suite plus mechanical enforcement. The model can write whatever it wants; the output has to pass the checks. Motion has no such layer today. axe-core and Lighthouse are excellent, but they largely can't catch a scroll-jacked hero with no reduced-motion path, because statically analyzing dynamic motion behavior is hard. The gap is exactly where AI tools generate the most output. What verifying motion looks like The trick is treating motion as data instead of as scattered CSS and JS. If motion is declared in a spec, it becomes checkable: json{ (Simplified for illustration.) With motion as a spec, the questions become mechanical: Does every animated element define reduced-motion behavior? Fail any of these and the check fails — determinist

The Global Race For AI Dominance: How It's Reshaping Markets

Defending The Enterprise Castle From The Model Layer

Nebius 3.6 Shows How It Plans To Compete For AI Cloud Customers

AI models: one country’s fears become everyone’s constraint