The AI industry has moved past the era of 'sweatshop data labeling.' Frontier models now require a 'strategic research accelerator' to generate highly complex, expert-level data for advanced reasoning, coding, and STEM capabilities, which is Turing's core focus.
The speaker defines ASI as the automation of 90% of knowledge work and outlines four key pillars to achieve it: multimodality, reasoning, tool use, and coding. He argues for a future of steady, continuous progress rather than a sudden 'rapid takeoff' scenario.
The claim is made that internet data for pre-training was exhausted three years ago. To continue improving models according to scaling laws, labs must now rely on vast amounts of newly generated expert human data and synthetic data.
Turing operates a dual-sided model, working with 'Formula One' frontier labs to build superintelligence while also helping enterprise 'car companies' adopt this technology. Insights from real-world enterprise use cases inform the data generation process for the labs, creating a virtuous cycle.
Keep pulling the thread on Jonathan Siddharth.