Good morning. It's Tuesday, June 23, and we're covering the rapid rise of Chinese AI models, NVIDIA’s effort to slash data center water consumption, and how SpaceX is turning AI compute demand into a major business.
Plus: a poll on AI acceleration, and a guest essay on why building the loop matters more than building the agent.
An open-source AI startup is set to spend up to $6.3 billion on computing infrastructure, underscoring how scarce AI chips have become. Reflection AI has signed a deal to access NVIDIA GB300 chips at SpaceX’s Colossus 2 data center, paying $150 million per month starting July 1. The agreement runs through 2029 and can be terminated with 90 days’ notice after the first three months. Reflection joins customers including Anthropic, Google, and Cursor as SpaceX expands its AI infrastructure business.
Intel and AMD are collaborating on a new specification that aims to make CPUs more capable of running AI workloads without relying on GPUs. The Advanced Compute Extensions (ACE) standard adds dedicated matrix-processing hardware to x86 processors while maintaining compatibility with existing AVX10-based software. ACE can perform up to 16 times more matrix operations per instruction and supports AI data formats including FP8, FP16, BF16, and INT8. The effort targets inference, edge computing, and latency-sensitive applications where GPU overhead can be a drawback.
Sakana AI claims its new Fugu Ultra system matches leading frontier models without relying on a single AI provider. The company has launched Fugu and Fugu Ultra, orchestration models that dynamically coordinate multiple AI agents through a single API rather than solving tasks with one monolithic model. Built on Sakana’s Trinity and Conductor research, the system handles model selection, delegation, and synthesis internally. Sakana positions the approach as a hedge against vendor lock-in and export-control disruptions affecting access to advanced AI models.
VIDEO
Building a DIY Bitzee With AI
A custom Bitzee powered by Raspberry Pi and Codex transforms a $40 virtual pet into a programmable touchscreen companion.
GUEST POST
Build the Loop, Not the Agent
Guest contributors: Ethan Wang is founding member of Google DeepMind Spark agent and founding member of Google DeepMind Mariner Agent. Namrata Ganatra is VP of Product & Engineering at Intuit, founder of AI-Native Builders, former AI startup founder, and ex-leader at Meta, Coinbase, Xero, and Microsoft.
Most teams treat agent modernization as a project with a finish line: re-architect around today's frontier model, ship, declare victory. But model capability advances faster than any single modernization effort can complete. By the time you finish re-architecting around today's frontier model, the next one has already shifted the ground beneath you.
Plan around a fixed end state and you will perpetually ship an architecture tuned to a model generation that is already obsolete. The better bet is simple: the team that can iterate fastest against the newest models will win. → Read the full article here.
AI RACE
Chinese AI Models Gain Ground as U.S. Firms Embrace Lower-Cost Alternatives
Chinese artificial intelligence developers are narrowing the performance gap with leading U.S. labs faster than many industry observers expected, driven by a wave of powerful open-source models from companies including DeepSeek, Moonshot AI, and Z.ai. Microsoft is reportedly considering offering DeepSeek’s V4 model through its Copilot ecosystem as a lower-cost option to models from OpenAI and Anthropic, while developers increasingly adopt Chinese models through platforms such as OpenRouter.
Recent releases including Moonshot’s Kimi and Z.ai’s GLM-5.2 have drawn praise from Silicon Valley executives, with some claiming performance comparable to top American systems. The trend highlights a growing tension between Washington’s efforts to limit China’s AI advances and the willingness of businesses to prioritize cost, accessibility, and performance when choosing AI tools. → Read the full article here. (Paywall)
SUSTAINABILITY
NVIDIA Unveils Data Center Cooling That Eliminates Water Consumption
NVIDIA says its new liquid-cooling architecture can reduce operational water use in AI data centers by up to 100% by recycling a closed-loop coolant mixture of water and propylene glycol. The system can operate at temperatures as high as 115°F, allowing many facilities to avoid the water-intensive cooling towers and chillers traditionally used to prevent AI chips from overheating.
The announcement comes as scrutiny grows over the environmental impact of AI infrastructure and as major tech companies seek ways to curb water consumption and lower operating costs. However, NVIDIA acknowledged the benefits vary by climate, with hotter regions still likely to require supplemental cooling during extreme temperatures. The design also addresses only one aspect of AI’s environmental footprint, leaving broader concerns around energy consumption, fossil fuel use, and data center expansion unresolved. → Read the full article here.
NEWS
What Else is Happening
Google Backs A24 With $75M (Paywall): Google is investing about $75 million in A24 and launching an AI research partnership with the studio behind recent hits, marking the tech giant's first ownership stake in a film company.
Groq Raises $650M Post-Nvidia Not-Acqui-Hire: Six months after Nvidia licensed its tech and poached its CEO, Groq raised $650M to pivot from chipmaker to inference-focused neocloud under co-founder Doug Wightman.
SpaceX Taps Bond Market After IPO: Days after its IPO, SpaceX launched its first bond offering to refinance short-term debt and fund AI infrastructure and Starship development while avoiding shareholder dilution.
Chevron Powers Microsoft AI Campus: Chevron signed a 20-year deal to supply natural-gas power to Microsoft's Texas data center campus, supporting up to 2.67 gigawatts as AI-driven electricity demand surges.
POLL
Can We Afford to Slow AI Down?
A UC Berkeley professor who stands to personally benefit from faster AI medical breakthroughs argues we should still slow AI down — she'd rather take the risk than rush. Where do you land on speed vs. caution? |
