Andrew Feldman

Andrew Feldman × Robin Rombach

Cerebras reports a $25 billion backlog as faster inference turns token capacity and data-center execution into scarce product advantages, while open models take routine and sovereign workloads.Feldman expects its architecture to improve well over 2x in 18 months; staged releases, red-teaming and recursive model improvement remain catalysts—and risks—as multimodal AI reaches film and robotics.

Andrew Feldman × Will Marshall × Brad Gerstner × Chamath Palihapitiya × Jason Calacanis × David Sacks × David Friedberg

The IPO pendulum is shifting toward companies listing at $1 billion to $5 billion, with Planet creating roughly 90% of its post-SPAC value in years three and four.Cerebras’s $18.50 IPO followed a difficult 9.5 years, while its chip targets 15–18-times faster OpenAI workloads; orbital data centers offer a potential two-to-three-year cost catalyst, but distributed clustering remains unresolved.

Harry Stebbings × Andrew Feldman

AI demand is running ahead of infrastructure, with Cerebras carrying a $25B backlog after models became genuinely useful somewhere in 2025.Structural HBM shortages could persist for years as new fabs require $40B and five years, while Cerebras’s SRAM architecture avoids HBM and CoWoS constraints.Cerebras reports Kimi K2.6 running 6.7x faster than the next-fastest GPU cloud, while its $20B+ OpenAI deal keeps customer concentration in focus.

Peter Diamandis × Salim Ismail × Dave Blundin × Dr. Alexander Wissner-Gross × Andrew Feldman

Google’s AI comeback is a distribution-and-infrastructure thesis: Gemini reached 900 million monthly users, AI Mode exceeded 1 billion, and projected CapEx is $180–190 billion as Spark and Search embed agents in defaults and commerce.Cerebras’ 68% IPO surge to a stated $95 billion market cap validates wafer-scale inference, but the more-than-$20 billion OpenAI deal and decades-long domestic-fab constraints leave demand and supply to monitor.

Elad Gil × Sarah Guo × Andrew Feldman

Cerebras says useful AI in 2025 made inference latency a daily constraint, with systems running 15, 18, 20x faster than GPUs.G42’s $1 billion order enabled cluster testing, while an OpenAI agreement Feldman says exceeds $20 billion and an AWS deployment raise the stakes for a 10x manufacturing increase this year.Delivery and governance now matter as much as contrarian architecture.

Brian Armstrong × Andrew Feldman × Jake Loosararian

Coinbase says U.S. crypto has shifted from regulatory defense to institutional deployment, with five of the top 20 global banks using its infrastructure.Under the GENIUS Act, regulated stablecoins hold 100% reserves in short-term Treasuries, while Coinbase can pass customers “about 100% of the economics” through rewards.B2B cross-border settlement is already driving strong Coinbase Business demand, while trade groups Armstrong believes are trying to revisit the law remain a risk to monitor.

Harry Stebbings × Andrew Feldman

Cerebras raised $1bn at a record valuation because it had margins while rivals had negative ones, despite still intending to go public; UAE orders may have represented 75-80% of revenue and consumed manufacturing capacity.Feldman argues two-year chip depreciation is empirically wrong and Nvidia’s pre-announcements and OpenAI investment signal defensive demand capture, while power sits in the wrong places; investors should watch utilization, customer concentration, permitting, and whether AI adoption delivers productivity.

Harry Stebbings × Jason Lemkin × Bhavin Shah × Andrew Feldman

AI is creating a golden age of software and extraordinary revenue growth, but thin wrappers, dilution, and uncertain recurring revenue are exposing the limits of the old scale-equals-durability model.Moveworks’ $2.85B sale shows the value of enterprise distribution and workflow integration, while CoreWeave’s IPO, a possible $2B put obligation, and constrained M&A make execution and liquidity the next catalysts.

Harry Stebbings × Andrew Feldman

Cerebras argues that GPU off-chip HBM is a fundamental inference bottleneck, while wafer-scale SRAM could challenge NVIDIA’s near-total market share.Feldman sees inference demand exceeding 100 times today’s market within five years, with chip providers potentially worth more than model companies, though G42’s 87% revenue concentration remains a key risk.