What Is the Best Way to Move AI Forward?

Jonathan Siddharth says the best way to move AI forward is to close the research and deployment loop. Turing works with labs to improve models for coding and knowledge work, finds what different models and agents are good at, and sees the jagged intelligence they have.

The work has made Turing a unique partner for enterprises deploying end-to-end agentic systems. Turing deploys them, watches where they break in the real world, and uses the capability gaps to improve the models and take on bigger enterprise problems. The cycle runs again: deploy, see the breaks, improve the models. The loop is what makes Turing a trusted partner between research and deployment. He thinks the loop is also the path to safer systems, because a system has to see reality to master real work.

He thinks labs and enterprises will increasingly focus on closing the loop between research and deployment, and he thinks the phase of building superintelligence will last at least 10 years. Some people believe all the jobs will be gone and no one will need to work after two years. He does not believe that. The singularity will hit, and he thinks recursive self-improvement is definitely real. People use AI models to speed up pre-training and post-training. Pre-training is a verifiable domain because the aim is to minimize perplexity, or pre-training loss.