1.
Google dismantles Deepmind and bets on a fresh start as Hassabis heads for the exit
Google is pulling DeepMind back into the core of Alphabet, stripping its independent status and shifting Gemini work to the Bay Area. Demis Hassabis is expected to leave in the next few months, while senior researcher Koray Kavukcuoglu will run day‑to‑day ops without a formal CEO title. Internally the move reflects deeper friction: teams are hitting snags training the newest frontier models, even though Google Cloud is still pulling in billions. The real question now is whether the company is betting on tighter infrastructure control or simply lagging behind the fast‑moving AI leaders.
2.
AI's energy appetite drives Nvidia and Amazon to pour billions into massive power infrastructure
The AI industry's hunger for power keeps growing. Nvidia is investing up to $3 billion in Lancium, a power infrastructure developer that already has four gigawatts under contract in Texas. Amazon, meanwhile, is building a gas-fired power plant in the state with a capacity of up to 7.65 gigawatts that could emit 33 million tons of CO₂ per year, making it the dirtiest in the country. The article AI's energy appetite drives Nvidia and Amazon to pour billions into massive power infrastructure appeared first on The Decoder.
3.
Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model
Instead of training a new model from scratch, Google DeepMind retrofitted Gemma 4 into a diffusion model using less than 10 percent of the original training budget. DiffusionGemma generates 256 tokens in parallel instead of one at a time, hitting about 1,500 tokens per second. Quality still trails the original autoregressive model in benchmarks, especially on reasoning tasks. The article Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model appeared first on The Decoder.
4.
AI is flooding Britain's employment courts with lawsuits
Britain's employment courts saw 39 percent more claims in the year through March 2026, many written with ChatGPT or Grok. The backlog jumped 55 percent to 64,000 unresolved cases, with AI-generated filings often running hundreds of pages and citing fabricated laws. The Economist calls it a "tragedy of the commons, AI edition," where workers with real grievances wait longer for justice. The article AI is flooding Britain's employment courts with lawsuits appeared first on The Decoder.
5.
Google Deepmind's WeatherNext predicts cyclone tracks and intensity at the same time
So I was looking into this new weather forecasting model from Google Deepmind, and what's interesting is that it can predict both the track and intensity of tropical cyclones at the same time. Normally, these are separate tasks, but this model can handle them simultaneously. What's even more impressive is that it can forecast these cyclones about a day further ahead than the current leading models. To put that into perspective, that's equivalent to about a decade of progress in traditional weather forecasting. And the best part is that the code and model are open-source, so others can build on this work.
6.
Scammers are enrolling fake students at US community colleges and using AI to collect financial aid
AI-powered cheating is spreading at US community colleges. According to The New Yorker, scammers enroll fake students in courses, use AI to complete their assignments, and pocket the financial aid. History professor David Roach asks, "Was it always the case that half of our students would cheat if it were easy enough?" The article Scammers are enrolling fake students at US community colleges and using AI to collect financial aid appeared first on The Decoder.
7.
How to Implement Structured Output with Local LLMs
They allow us to keep our sensitive data and reduce our dependency on cloud APIs. However, running the model locally is only the first step. In a practical application, the local LLM is usually part of a larger workflow. This means its responses often need to be consumed by another component. In those situations, free-form text can be very difficult to work with. We want the output to follow some predictable structures. That’s exactly what Structured Output is for. We can achieve that by first defining the expected shape, or schema, in advance.