Good morning. It's Wednesday, June 24, and we're covering the economic ripple effects of AI data centers, China's latest push to close the AI gap with the U.S., and Meta's experiment with prediction markets.

Plus: yesterday's poll results, and a new test of your ability to separate reality from AI-generated content.

Anthropic is shifting Claude from a chat tool into a shared workplace collaborator that can remember context, take initiative, and work asynchronously inside team channels. The new Claude Tag launches in Slack, where users can assign tasks by tagging @Claude and let it access approved tools, data sources, and codebases. Anthropic says its internal version now generates 65% of product-team code and is increasingly used for analytics, support, and debugging work.

Meta's latest growth experiment avoids real-money gambling, even as it targets the booming prediction market trend popularized by Polymarket and Kalshi. CEO Mark Zuckerberg has tasked a team with building "Arena," a standalone app where users forecast outcomes using a points-based system. The project is considered a high priority despite its experimental status and could later expand into real-money wagering.

NVIDIA is teaching AI agents specialized biomedical expertise, reducing the time and computing costs required for complex scientific research. Its new BioNeMo Agent Toolkit works with agents such as Codex and Claude, providing built-in knowledge for tasks that general-purpose models struggle to perform. The toolkit supports drug discovery, genomic analysis, and medical imaging, while helping agents avoid wasting tokens on lengthy literature searches.

Groq is doubling down on AI inference rather than model training, arguing that running models will become the industry's largest infrastructure market. The company raised $650 million to expand its global inference cloud, following a licensing deal with NVIDIA and integration into NVIDIA's LPX platform. Groq already operates 13 data centers across four regions, serves more than five million developers, and processes trillions of AI tokens weekly.

Self-improving AI continuously benchmarks and optimizes app performance, moving page by page until all load times fall below 50ms.

Columnist Ann Davis Vaughan argues that many concerns about AI data centers overlook their growing economic impact on host communities. She points to examples including rural Louisiana, where tax revenue linked to a large Meta campus helped fund $50,000 teacher bonuses, and West Memphis, Arkansas, where local officials say Google's investment has boosted business recruitment. While acknowledging concerns over power, water, tax incentives, and housing, Vaughan cites studies projecting thousands of long-term jobs from data center projects in Wisconsin and Texas.

She also notes that regulators are increasingly requiring tech companies to cover infrastructure costs, while communities have gained leverage to negotiate investments in schools, roads, and public services. The piece contends that growing opposition to data centers may cause some communities to miss economic development opportunities, even as developers face rising pressure to prove they can deliver on promised local benefits. → Read the full article here. (Paywall)

Chinese AI startup Zhipu released its new open-source model, GLM 5.2, on June 13, claiming performance close to leading American systems at a fraction of the cost. Benchmark rankings place it among the strongest AI models available, with some evaluations showing it outperforming newer U.S. competitors on workplace-oriented tasks. However, researchers caution that Chinese models often score better on public benchmarks than private tests, where American firms such as OpenAI and Anthropic appear to maintain a lead of roughly eight to ten months.

The release comes as U.S. AI access restrictions and rising usage costs push some companies to explore alternatives, though lower advertised prices do not always translate into lower real-world costs because Chinese models typically consume far more computing resources. The development suggests China is closing the AI gap faster than many expected, even as American companies retain an edge in overall capability and efficiency. → Read the full article here. (Paywall)

MIT researchers have developed a system-on-a-chip called Gleanmer that allows small robots and other battery-powered devices to build detailed 3D maps in real time while consuming just 6 milliwatts of power—roughly the energy used by a single LED. Instead of relying on memory-intensive voxel-based mapping, the chip uses flexible Gaussian representations and a custom algorithm that processes depth images in a single pass, dramatically reducing memory and energy requirements.

The system consumes about 2.5% of the power required by the best existing mapping chips and can cut navigation energy use by roughly 80% by reusing map data during path planning. The technology could enable tiny drones to inspect infrastructure, improve lightweight augmented reality headsets, and expand the capabilities of autonomous devices operating with strict power constraints. → Read the full article here.

Google’s AI Era Cracks Show: ChatGPT, Bing, and DuckDuckGo are gaining users as Google's search traffic dips, exposing new cracks in its core business.

Meta Launches $299 AI Glasses: Meta launched its first in-house smart glasses, expanding beyond partnerships with Ray-Ban and Oakley.

Engram Raises $98M for AI Memory: The AI memory startup says its technology can cut token use by up to 100x, helping companies lower AI costs.

SK Hynix Overtakes Samsung: AI-driven demand for memory chips pushed SK Hynix past Samsung in market value after a historic stock rally.

Meta Pauses Employee AI Training: Meta halted an AI training project using employee keystrokes after a leak exposed internal conversations and data.

Here’s how you voted on yesterday’s poll: 38% believe AI’s pace is beyond our control, while 25% say the right approach depends on the domain.

🟨🟨🟨⬜️⬜️⬜️ Speed — the benefits outweigh the risks (22%)
🟨🟨⬜️⬜️⬜️⬜️ Caution — slow down, even if it costs us (14%)
🟨🟨🟨🟨⬜️⬜️ Depends on the domain (25%)
🟩🟩🟩🟩🟩🟩 We can't actually choose the pace either way (38%)

Thanks for reading. See you next time!

— Matthew Berman, Nick Wentz & the Forward Future Team