OpenAI floated giving the Trump admin a 5% stake. Financial Times ran it citing two people familiar with the talks. OpenAI haven't confirmed or denied anything.

$852 billion valuation at last count, March 31. That 5% works out to $42.6 billion in paper equity nobody can touch yet.

The sequence is what sticks. Six weeks ago NOTUS had senior officials already talking AI equity stakes with major companies. Three weeks ago Commerce spent 18 days reviewing Anthropic's Fable 5 and Mythos 5 before lifting controls. OpenAI in early formal talks now.

I'm old enough to remember when tech got regulated by hearing about it on the evening news months later. Now the regulation happens in parallel, while the product is still being built.

The Alaska Permanent Fund comparison keeps surfacing — Americans getting a cut of AI returns the way Alaskans get oil dividends. Shows up in secondary reporting and OpenAI's own earlier policy docs on public wealth sharing. Altman may never have said those words in these talks. We don't know that for sure.

There were no governance channels for this six months ago. They're being built out of nowhere — equity stake, export controls, model reviews with fixed timelines. Everyone keeps asking whether Washington gets a seat at the table. Nobody asks what happens when they actually show up and talk money.

OpenAI engineers reportedly developed new optimizations that reduced the cost of running some existing AI models by more than 50%.

Inference is the computing required whenever ChatGPT or an API model generates an answer. Unlike model training, these costs continue growing with every user, prompt, and agent task.

OpenAI applied the optimizations to ChatGPT traffic from visitors who were not signed into free or paid accounts. At one point, the company reportedly needed only a couple hundred Nvidia GPUs to serve that portion of traffic.

The company has not revealed:

• Which models received the optimizations
• How many GPUs were previously required
• Whether speed or answer quality changed
• The exact technical method used
• Whether customers will receive lower prices

Possible techniques include using lower-precision calculations, reusing previous computations, grouping requests together, or sending easier prompts to smaller models. However, none of these methods has been confirmed.

The breakthrough is separate from Jalapeño, OpenAI’s new custom inference chip developed with Broadcom. Jalapeño is expected to begin deployment later in 2026, meaning OpenAI is attacking costs through both software and hardware.

Lower inference expenses could help OpenAI improve profit margins, increase ChatGPT usage limits, lower API prices, or run more demanding coding and research agents without equally large increases in computing capacity.

Source: https://www.theinformation.com/newsletters/ai-agenda/openai-discovers-new-way-cut-inference-costs-half?rc=mf8uqd

Meta introduced Brain2Qwerty v2, an AI system that converts brain activity into written sentences without requiring a surgical implant.

The system uses magnetoencephalography, or MEG, to measure the magnetic signals produced by the brain.

Researchers trained it using approximately 22,000 sentences from nine volunteers. Each participant spent around 10 hours inside an MEG scanner while memorizing and typing sentences.

Brain2Qwerty v2 achieved:

• 61% average word accuracy
• 78% accuracy for the best participant
• More than half of that participant’s sentences decoded with one word error or less

This is a major improvement over previous non-invasive methods, which Meta says achieved roughly 8% word accuracy.

The system combines several AI techniques. Deep learning processes the raw brain signals, while a language model uses sentence context to turn noisy predictions into more coherent text.

Meta also used AI coding agents to test and improve parts of the decoding pipeline, although human engineers selected the final training setup.

The long-term goal is to help people who lose the ability to speak or move because of brain injuries or neurological conditions communicate without having electrodes surgically implanted.

Meta is releasing the training code for Brain2Qwerty v1 and v2. Its research partner is also releasing the original dataset to support further neuroscience research.

There are still major limitations.

The system does not read random private thoughts. It mainly decodes brain activity connected to the physical process of typing.

It was tested on healthy volunteers rather than people with paralysis or serious brain injuries. It also requires many hours of personal training data and a large, expensive MEG machine that is unsuitable for everyday home use.

Source: https://ai.meta.com/blog/brain2qwerty-brain-ai-human-communication/

When Partners Start Eating Their Own Customers

An interesting thought after an interview with one of Nebius’ founding partners.

As I understand it, Nebius is moving beyond being just “another AI cloud.” They are entering the inference layer: helping users and companies run, optimize, and serve AI models faster, cheaper, and with less friction.

And this is where things get really interesting.

Today, companies like Nebius are infrastructure partners in the great AI race. They provide cloud capacity, GPUs, compute power, and the backbone that allows major AI players to scale. OpenAI, Anthropic, Meta, Microsoft and others consume compute like dragons consume gold.

But at some point, an infrastructure partner asks a very simple question:

“Why should we only be the power plant behind someone else’s models, if we can give customers ready-to-use inference, open-source models, APIs, optimization, and a better cost structure ourselves?”

And that is where the ouroboros begins.

First, AI labs feed cloud and infrastructure companies with their massive demand for compute.

Then infrastructure companies use that money to build stronger and stronger platforms.

Then they move up the stack: not just “here are your GPUs,” but “here is managed inference, model routing, optimization, API access, security, billing, and the enterprise layer.”

At that moment, yesterday’s infrastructure supplier becomes an economic competitor to its own partners.

Not necessarily by building “another ChatGPT.”

Something much more subtle.

The competition will not only be about the prettiest chat interface.

It will be about token cost, latency, privacy, customization, open-source models, enterprise control, and the ability to avoid dependency on a single closed API provider.

For businesses, this is a very strong signal.

Before, the choice was often simple: “Pay OpenAI or Anthropic and don’t think too much.”

Now a second scenario is emerging:

“Take an open or custom model, run it through a managed inference layer, optimize the cost, and stop being locked into one provider.”

This will not necessarily kill OpenAI or Anthropic. They still have frontier models, brand power, deep R&D, and excellent products.

But the margin on mass-market enterprise AI tasks may start leaking away.

Customer support, RAG systems, internal copilots, document intelligence, coding assistants, analytical agents — a huge part of these use cases does not always require the most expensive frontier model in the world.

Many companies do not need “the smartest model on Earth.”

They need a good enough model with speed, security, predictable pricing, and control.

And this is exactly where companies like Nebius, Baseten, Fireworks, Cerebras and other inference/infrastructure players may become the new power layer of the AI economy.

The AI market is maturing.

The first stage was: “Who has the strongest model?”

The second stage is: “Who can deliver inference cheaper and more reliably?”

The third stage will be: “Who controls model routing and task orchestration?”

And whoever controls routing, controls the money.

That is why Nebius is interesting not only as an infrastructure provider. It is interesting as a sign of the future AI market, where partners, customers, and competitors constantly change places.

Today, you sell shovels to gold miners.

Tomorrow, you open the gold exchange yourself.

And that may be the most beautiful — and dangerous — part of the AI economy.

The ouroboros is smiling.

If you are trying to break into AI, you’ve probably noticed a massive problem with most online courses: they teach you theoretical math, but they don't teach you how to deploy real models on real infrastructure.

Google just quietly solved this problem.

They have launched a unified learning hub at Google Skills, combining over 3,000 AI courses from Google Cloud, Google DeepMind, and Google Education. To sweeten the deal, they are offering 700+ hands-on labs, built-in Gemini Code Assist to debug your projects, and up to $500 in free cloud credits so you can build without breaking the bank.

According to global hiring data, recruiters are actively hunting for these specific skill sets, with Google Cloud credentials giving applicants up to an 82% hiring preference rate.

Here is exactly how the platform works and how you can maximise it to land a high-paying role in IT.

The 5-Step Roadmap to Get Started

You don't need a computer science degree to start. The ecosystem is designed to take you from a total beginner to an enterprise-ready developer:

1. Create Your Account: Head over to the official portal at skills. google.
2. Pick a Specialized Track: Select an AI path that matches your career goals (e.g., Generative AI Foundations, Machine Learning Architecture, or Vertex AI Development).
3. Dive into the Labs: Skip the text slides and jump into the 700+ live labs. You will write code inside actual Google Cloud consoles, not simulated environments.
4. Collect Skill Badges: As you complete milestones, you earn digital Google Skill Badges. These are verifiable links you can embed directly into your LinkedIn profile or resume.
5. Level Up to Professional Certifications: Use your knowledge to pass proctored Google Cloud Certifications, which routinely rank among the highest-paying credentials in the tech industry.

Three Features That Make This Platform Different

Most free bootcamps fail because they lack infrastructure. Google solved this by injecting three massive advantages into this platform:

• $500 in Free Cloud Credits: Training deep learning models is expensive. Google provides up to $500 in complimentary credits through their developer programs so you can host, test, and run your live AI models entirely on their dime.
• Built-in Gemini Code Assist: Stuck on a broken line of Python or a misconfigured cloud bucket? The platform has an integrated AI assistant. Gemini acts as a live, 24/7 coding mentor to fix your bugs, explain complex logic, and optimize your architecture in real time.
• Curated by DeepMind & Google Cloud: You aren’t learning from random internet tutorials. The curriculum is built directly by the researchers at DeepMind (the minds behind AlphaFold and Gemini) and the engineering teams scaling Google Cloud worldwide.

The Career Impact: Why This Matters to Recruiters

The tech job market is highly competitive right now, and generic "certificates of completion" don't carry the weight they used to. Recruiters want proof of hands-on capability.

Data shows that hands-on learning platforms yield a 133% increase in talent retention and skill mastery compared to passive video watching. Because Google's Skill Badges require you to successfully spin up cloud architecture and pass practical challenges to earn them, companies treat them as proof of actual on-the-job capability.

Whether you are a software engineer looking to upskill, a student trying to break into tech, or a non-technical manager needing to understand modern workflows, this is arguably the highest-value free resource available right now.

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Anthropic accused Alibaba’s Qwen team of running the largest known “distillation attack” against Claude.

Between April 22 and June 5, operators allegedly created nearly 25,000 fraudulent accounts and generated more than 28.8 million exchanges with Claude.

Anthropic claims the campaign targeted some of Claude’s most valuable abilities, including:

• Software engineering
• Agentic reasoning
• Completing complicated, long-running tasks

Distillation normally involves training a smaller AI model using the responses of a stronger one. The technique itself is widely used, but Anthropic argues that Alibaba violated its rules and access restrictions by hiding behind thousands of fake accounts.

Anthropic warned US lawmakers that these campaigns could allow Chinese AI companies to reproduce advanced American models faster and without paying the full research and computing costs.

The company urged Congress to close loopholes giving Chinese labs access to advanced US chips and consider penalties for companies involved in unauthorized distillation.

Alibaba had not publicly responded to the allegations when the report was published.

Source: https://www.reuters.com/world/china/anthropic-says-alibaba-illicitly-extracted-claude-ai-model-capabilities-2026-06-24/

Alibaba’s Qwen team released Qwen-AgentWorld, an AI model designed to simulate the environments used by autonomous agents.

Unlike a normal agent that decides what action to take, AgentWorld predicts what will happen after an action. For example, it can simulate the response to a terminal command, browser click, software change, or tool request without executing it on a real system.

It covers seven environments:

• Web browsers and search
• Linux terminals
• Software engineering
• Android devices
• Desktop operating systems
• MCP tools
• General web applications

Qwen trained the models on more than 10 million real-world interaction sequences.

Two versions were developed:

• 35B-A3B: 35 billion total parameters, with three billion active at a time
• 397B-A17B: 397 billion total parameters, with 17 billion active

On Qwen’s AgentWorldBench, the larger model scored 58.71, slightly above GPT-5.4 at 58.25 and Claude Opus 4.8 at 56.59.

The smaller model scored 56.39 and significantly outperformed the standard Qwen model of the same size.

Qwen also used AgentWorld to create simulated training environments. Agents trained in these environments improved across coding, search, terminal, MCP, and tool-use benchmarks.

In one experiment, agents practiced web research inside a completely fictional world containing invented people, organizations, and events. They later performed better on real search tasks, suggesting that they learned research strategies rather than memorizing answers.

Qwen released the smaller model with a 256,000-token context window, along with its benchmark and deployment code, under an Apache 2.0 license.

Source: https://qwen.ai/blog?id=qwen-agentworld