📊 The AI economy is scaling faster than almost anyone expected.

A new report from Exponential View estimates that the AI industry generated $110 billion in real revenue over the past 12 months, counting only end-customer spending and removing supply-chain double counting. That means if a dollar is spent on Claude and later flows to Amazon for cloud infrastructure, it’s only counted once.

Even more striking, AI is now running at a $175 billion annualized revenue rate, excluding China, internal productivity gains, advertising uplift, consulting, and systems integration.

Here are some of the biggest takeaways:

• AI revenue is growing roughly 3× faster than the internet or mobile revolutions at comparable stages.
• Revenue formation is accelerating dramatically. In 2023, it took about 180 days for the industry to add the next $1 billion in revenue. Today, it takes less than 2 days.
• Enterprise AI has moved beyond experimentation, but full company-wide deployment is still in its early innings.
• AI was mentioned in earnings calls by 31% of tracked S&P 500 companies, yet only 20% quantified its financial impact.
• Hyperscaler AI revenue currently appears sufficient to cover AI infrastructure depreciation, although those economics rely heavily on long hardware lifespans around 6 years for GPUs and 14 years for other infrastructure.
• Lower AI prices aren’t shrinking the market. Every 10% reduction in token prices drives 12–18% more token usage, suggesting demand is highly price elastic.
• The biggest bottlenecks are no longer user demand? they’re power availability and the cost of building new data centers.

@datasciencepulse 〰️

🤖 OpenAI just revealed what the first AI-agent workplace looks like and it’s wild.

A new paper shows that AI agents are no longer just helping employees write emails or code. They’re increasingly doing the work themselves.

The biggest surprise? Codex now generates 99.8% of OpenAI’s internal AI output, up from less than 10% just a year ago. And it’s no longer just engineers using it.

Legal, Finance, Recruiting, Customer Support, and other business teams are rapidly adopting AI agents to handle documents, approvals, policies, and the endless follow-up work that fills a typical office day.

The numbers show just how quickly this shift is happening. Since August 2025, non-developer usage has surged 137× among individual users and 189× across organizations, suggesting AI agents are spreading anywhere work follows repeatable processes.

People are also assigning much larger jobs to AI. More than 70% of users now delegate tasks that would take a person over an hour to complete, while one in four hand over work worth more than eight hours.

Instead of waiting for one task to finish, many users now run multiple agents at once. Nearly 29% manage five or more concurrent agents, and the heaviest users orchestrate the equivalent of 71 hours of AI work every day by running those agents in parallel.

@datasciencepulse 〰️

🧠 Demis Hassabis says AI is getting surprisingly good at reconstructing what people see in their minds and even their dreams.

According to the Google DeepMind CEO, neuroscientists are already combining brain scans with AI models to recreate images that people are imagining. A person thinks of an image inside an fMRI scanner, AI decodes the brain activity, reconstructs the visual, and asks if it matches what they had in mind.

It isn’t perfect mind reading but it’s getting remarkably close.

This isn’t just a futuristic idea either. In 2025, researchers at Fudan University introduced Neuropictor, a model that reconstructed snapshots from sleeping participants’ dreams using brain scans and then stitched them into video with AI.

Hassabis says work like this builds on decades of neuroscience research, including his own PhD, which found that memory and imagination rely on many of the same brain systems.

His prediction? Sci-fi-style brain interfaces that can visualize thoughts could arrive within the next few years.

@datasciencepulse 〰️

🤖 OpenAI’s GPT-5.6 Sol Beats Claude Mythos

OpenAI’s GPT-5.6 Sol edges out Claude Mythos 5 in agent coding with 88.8% vs. 88% on Terminal-Bench 2.1. The Sol Ultra variant scores 91.9%.

Sol matches Anthropic’s model in cybersecurity using three times fewer tokens. The US government restricts its release, which OpenAI opposes.

OpenAI plans wider API and Cerebras access in July. Officials previously blocked Anthropic’s Fable 5, showing ongoing regulatory hurdles.

@datasciencepulse 〰️

🤖 Claude controls robot dog 20 times faster

Anthropic tested Claude on a four-legged robot with tasks that human teams completed less than a year ago. Claude connected to the robot’s camera and sensors, wrote control code, and set up object detection in under 10 minutes.

Claude] was 37 times faster than humans without AI and nearly 19 times faster than humans with AI. It wrote ten times less code than the previous human+Claude team, with most working on the first try.

@datasciencepulse 〰️

🇪🇺 Europe wants Anthropic on its own turf

Europe is starting to treat frontier AI like critical infrastructure.

According to Reuters, Austria is urging the EU to convince Anthropic to establish part of its business inside Europe, with EU laws, customers, capital, and infrastructure to reduce the bloc’s dependence on US-controlled AI.

The problem? Moving servers to Europe doesn’t move control.

Anthropic is still an American company, and its ownership, model governance, key employees, and training infrastructure remain subject to US export controls. That means Washington could still restrict access to its most advanced models for foreign users.

Austria’s argument isn’t that this is easy, it’s that Europe shouldn’t rely entirely on AI systems that could become unavailable because of a US political decision.

Source.

@dztasciencepulse 〰️

🇺🇸 U.S. data center spending now surpasses most infrastructure projects

U.S. spending on data center construction has reached $50 BILLION, now exceeding the COMBINED spending on airports, ports, and mass transit, per Bloomberg.

The AI infrastructure boom continues to accelerate, with US data center construction spending up 357% since 2022 and now accounting for 2.3% of all U.S. construction spending.

@datasciencepulse 〰️

💸Oracle just crossed a major milestone in the AI era.

For the first time, an S&P 500 company has explicitly linked mass layoffs to AI in an SEC filing.

Oracle cut 21,000 jobs, about 13% of its workforce and took a $1.8 billion restructuring charge, saying AI adoption is driving the changes.

The productivity gains are staggering. Internal pilots reportedly shrank teams of 47 database administrators to just 3 senior architects supported by AI. The system catches 94% of issues before they become problems, while engineering tasks that once took 6 weeks now take just 6 hours.

But this isn’t simply about cutting costs.

Oracle is redirecting those savings into a $50 billion AI infrastructure expansion for fiscal 2026, pouring money into data centers, GPUs, and cloud capacity.

@datasciencepulse 〰️

🤖Grok 4.5 has been developed using xAI’s new V9 foundation model, which contains 1.5 trillion parameters and incorporates data from Cursor. This makes it roughly three times larger than its predecessor, the v8-small model, which had 0.5 trillion parameters.

The V9 foundation model is described as a strong, reliable system comparable to Opus, rather than bringing a sudden leap in performance.

Notably, the pace of advancements at SpaceXAI has accelerated, following a shift in focus by several leading engineers from Starlink and Starship projects to artificial intelligence development.

The previous v8 model, used for Grok 4.3, was completed in December with several significant limitations. Grok 4.5 is expected to represent a substantial improvement in capability.

@datasciencepulse 〰️

🔍 Gemini now searches and buys apps in Play Store

Google integrated Play Store into Gemini, letting users find apps by simply telling the AI what they need. For example, saying "map for travel abroad" prompts Gemini to locate and open the app page directly.

Gemini also enables buying Play gift cards and in-game items right in chat. This feature is limited to personal Google accounts, users 18+, and is rolling out gradually on Android.

@datasciencepulse 〰️

💧 A fact-check regarding artificial intelligence’s water consumption, with focus on the impact of data centers, has emerged.

Available data suggests that large AI models and the facilities housing them require notable amounts of water, particularly for cooling equipment during intensive computations. This operational need has led to increased scrutiny of the technology sector’s environmental footprint.

Sources underline that water usage figures can vary depending on the location, the type of cooling technology used, and the demand placed on data centers. Reliable quantification is challenging, as not all companies disclose detailed consumption data. Nonetheless, the topic continues to attract attention as AI development accelerates.

@datasciencepulse 〰️

🧠 Meta unveils Brain2Qwerty v2: AI can turn brain signals into text, no brain implant required

Imagine typing… without actually typing.

Meta has demonstrated Brain2Qwerty v2, an AI system that converts brain activity into text using a non-invasive magnetoencephalography (MEG) helmet instead of a surgically implanted brain chip.

Here’s how it works:

🪖 The user wears an MEG helmet that measures the tiny magnetic signals produced by the brain.
🧠 As the person thinks about typing, the AI filters through the noisy brain activity.
⌨️ The model reconstructs the intended keystrokes and generates coherent sentences.

The technology is still confined to research labs because MEG scanners are large, expensive, and require highly controlled environments. It’s nowhere near replacing a laptop or smartphone keyboard yet.

Still, it’s a major step for non-invasive brain-computer interfaces. While companies like Neuralink rely on implanted electrodes, Meta is exploring whether AI can decode thoughts without surgery.

@datasciencepulse 〰️