AI Leaders Reveal the Next Wave of AI Breakthroughs (At FII Miami 2025) | EP #150

Summary

• Stability AI is shifting Stable Diffusion’s distribution lead—launched in August 2022, with 270 million downloads versus 9 million for the next model—into specialized production tools for film, TV, gaming and advertising. Prem Akkaraju said roughly two dozen of a planned 50–60 “ultra-narrow AI” models address workflows such as rig removal, paint and rotoscoping, and camera match-plate construction. He estimated convincing on-demand video within six to 12 months and argued that Hollywood is “confusing headwind with tailwind”; he pointed to tools appearing in Avatar 3, 4 and 5.
• Liquid AI’s bet is that private, on-device intelligence can expand the market beyond GPU-equipped clouds. Ramin Hasani described a non-transformer architecture derived from liquid neural networks that can deliver ChatGPT-like experiences locally on phones and laptops, while also powering cars, satellites and jets. Once deployed on a device, he said, hosting costs “$0”; future robots could have local AI brains rather than rely on the cloud. Peter said Liquid AI had gone from zero to a $2 billion valuation in about two years, and noted a quarter-billion-dollar round led in part by G42.
• SandboxAQ is targeting quantitative industries where language models cannot perform the underlying physics. Jack Hidary said the company has raised $850 million and argued that drug and materials discovery needs models “trained on molecules and atoms,” with quantum equations translated into GPU-compatible matrix algebra. Quantum computers may join GPUs in a hybrid GPU/QPU cloud in five to seven years, but useful large quantitative models, or LQMs, run on GPUs today; Aramco is its newest announced customer.
• Tenstorrent is attacking AI’s compute cost and lock-in with native tensor processors and an open software stack. Peter noted its recent $700 million Series D. Jim Keller’s target is systems 5–10 times cheaper than current systems, spanning small television-chip configurations through large-model training machines. His thesis is that AI need not be “unbelievably expensive, unbelievably big, [or] unbelievably proprietary,” and that open infrastructure will broaden adoption and innovation.
• Enterprise AI remains more pilot theater than production, according to figures Sukharevsky cited. He said only 11% of use cases reached production over the past five years, with generative AI at “maybe 7% at the best,” because companies insert technology into broken processes instead of redesigning them. QuantumBlack, he said, has 5,000 people in 50 countries, five R&D centers and roughly 43 products deployed globally. Senior sponsorship, data, architecture and organizational politics are central constraints.
• The panel offered two execution prescriptions: focused teams that start with concrete gains, and institution-wide commitment. Hidary cited an 11-person team solving GPS-denied navigation with AI and quantum sensors and urged responsible, faster adoption to tackle diseases and battery storage. Keller asked his software organization to double productivity and first produce code with fewer bugs. Sukharevsky agreed with Peter’s warning that companies failing to use AI may be out of business by decade’s end, but said transformation requires top-level commitment: “you cannot go small—you need to go big to succeed.”

Deep dive

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