Altman readies Manhattan Project–scale AI build-out

OpenAI is scaling on two axes: compute and hardware. On the infrastructure side, it’s committing an additional $100B to order server rentals, pushing total spend toward $350B by 2030. CFO Sarah Friar concedes compute shortages have already throttled feature rollouts; Altman insists the AI race is a contest of raw scale as much as algorithms. Forecasts peg annual server bills at $85B, nearly half of hyperscaler cloud revenue in 2024, putting enormous pressure on chipmakers, integrators, and utilities. These “standby” clusters won’t just idle, they’re designed for immediate spin-up of coaching runs or demand spikes, giving OpenAI an always-ready compute buffer.

At the identical time, Jony Ive’s io project is pulling Apple’s supply chain and design bench into AI-native hardware. Prototypes under discussion include a screenless smart speaker, glasses, recorder, even a wearable pin. Luxshare is already contracted; Goertek is in talks. Launch window: 2026–27. Greater than two dozen Apple veterans, including Tang Tan and Evans Hankey, have defected to OpenAI, citing fewer bureaucratic bottlenecks and the prospect to ship category-defining products.

OpenAI is making a Manhattan Project-scale wager. At $20B a yr on training alone, it’s betting that future AI leadership will hinge on brute-force compute combined with devices built from the bottom up for conversational, multimodal intelligence. Read more.

xAI’s Grok 4 Fast collapses the trade-off between scale and price. The model delivers GPT-5-class reasoning on AIME (92%) and HMMT (93.3%) while running 40% leaner, slashing task costs by as much as 98%. It fuses fast-answer and deep-reasoning modes right into a single prompt-driven system, cutting “considering tokens” and latency which is critical for real-time, compute-sensitive use cases.

With native tool use for web, code, and search, Grok 4 Fast even displaced OpenAI’s o3-search at the highest of LMArena. A 2M-token context and pricing from $0.05/M tokens position it squarely for mass deployment. Wharton’s Ethan Mollick called it one other reset of the AI cost curve, noting that benchmarks like GPQA Diamond may now be functionally maxed out.

Meanwhile, Neuralink heads into human trials this October with its thought-to-text implant, FDA-cleared for investigational use. The device decodes neural activity directly into text, with near-term applications for speech-impaired patients and longer-term ambitions to let healthy users query AI systems by thought alone. Read more.

Oracle’s push into AI cloud infrastructure is accelerating as the corporate reportedly nears a $20 billion multi-year cope with Meta to host its Llama models. The agreement, if finalized, would expand Meta’s compute capability across Facebook, Instagram, and WhatsApp while reducing its dependence on Microsoft Azure. It also builds on Oracle’s recent momentum: a record-breaking $300 billion contract with OpenAI and a partnership with xAI. Oracle is betting its cheaper, faster Cloud Infrastructure can undercut AWS and Google Cloud, a technique that has helped drive its top off 85% this yr.

However the AI boom is showing signs of strain. MIT research finds 95% of AI pilots fail to deliver ROI, echoing warnings from OpenAI’s Sam Altman and Meta’s Mark Zuckerberg. Chatting with Access podcast, Zuckerberg cautioned that “collapse is certainly a possibility.” Despite committing $600 billion through 2028 to AI data centers and infrastructure, he argues overbuilding is safer than missing the window for superintelligence.

Meta also faces reputational headwinds. Strike 3 Holdings has filed a $350 million lawsuit alleging Meta pirated 2,396 pornographic videos via BitTorrent to complement training data, including explicit titles suggesting underage actors. Legal scholars warn such practices could contaminate AI outputs and trigger public backlash. Meta denies the costs, noting its V-JEPA 2 model trained on “1 million hours of web video,” though critics say the dataset stays suspiciously vague. Read more.

Google is popping the Home app right into a true AI command center. Version 3.41.50.3 embeds Gemini, letting you query your smart home via the brand new “Ask Home” bar, by voice or text, and get contextual insights, not only device toggles. Gemini taps into device states, sensor histories, and activity logs, synthesizing data to anticipate needs, track trends, or summarize past events.

UI changes streamline control. The old Favorites tab is now Home, and Devices/Settings live behind a grid icon. You’ll be able to pin live environmental metrics like outdoor air quality and temperature, while latest video and thermometer icons hint at next-gen Nest hardware. Read more.