Last week, we had a chance to host @jonsid, founder of @turingcom for @sv_icons. When you talk to Jonathan, it feels like he processes everything through a purely factual lens of causes and outcomes. Most of us draw takeaways through the filter of our own experiences. What Jonathan does differently is strip away the bias and analyze events almost from a machine-learning perspective. One of the most fascinating and insightful conversations we've had at Icons. Here are a few takeaways: • What was refreshing to hear is that Jonathan isn't the stereotypical Zuckerberg-style founder who succeeded on the first try. His first startup didn't work out the way he intended. Right after Stanford CS, Jonathan started a company in Silicon Valley and spent seven years building it before reflecting on what went wrong. The answer wasn't Jonathan. It was the market. He was attached to an idea that simply didn't have a large enough market. He was stubborn. He believed it could be huge. But that's not what the market demanded. • In situations like that, Jonathan suggests being less stubborn. Give yourself the freedom to think differently. Go talk to 100 ICPs and verify whether they actually care about the problem you're trying to solve. If the answer is yes, go solve it. If the answer is no, pivot away. Not just pivot slightly, but jump away from what you had before - teleport. All your existing collateral can become a curse when you're trying to find a truly great startup idea. • But what about insights? Didn't we learn at Stanford that we should stick with an "insight," following Andy Rachleff's Product-Market Fit framework? Jonathan's view is: challenge your insight. Most insights only exist within a specific time horizon. Imagine having a brilliant insight around automation before 2023. Then ChatGPT arrives. Do you still hold on to that insight? Probably not. Humble yourself. Your insight may no longer be true. Don't become attached to the dream. • Okay, you've pivoted and your old insight is no longer valid. What's next? Go all in. Jump into the new thing that excites you most. Don't underestimate your ability to develop new insights. If you're smart and curious, you'll go deep and find them again, but this time inside a market that's actually growing fast enough to matter. • When Jonathan started Turing, OpenAI called and asked how many people he could dedicate to expert-skill labeling. He wanted to say an even bigger number because the demand was so overwhelming. The market signal was impossible to ignore. In just a few years, Turing grew to multi-hundred million ARR. Today, it serves many of the leading AI labs and also helps enterprises adopt AI by connecting them with the best solutions available. • Is the opportunity around data labeling limited? Eventually, yes. But not anytime soon. Jonathan's view is that we're still decades away from fully automating the process. At the same time, Turing has built a second business that leverages the latest AI models and innovations to help enterprises deploy AI directly into their operations. • How would Jonathan screen for startup ideas? He would look for highly fragmented markets with mostly analog competitors. Real estate is one example: fragmented, less technology-driven, and deeply connected to the physical world. • Another way to think about opportunities is to become an input to AI companies. What will they need to reach the next level? It could be data. It could be infrastructure. It could be something entirely different. • Jonathan believes founders need to stay several years ahead of competitors. How do you get ahead? Reading books isn't enough. You need high-variance learning so you don't get trapped in a local minimum. That means constantly meeting new people, exposing yourself to new ideas, and learning from what others have built, especially in Silicon Valley, where the density of ambitious and talented people remains incredibly high. Thanks Jonathan Siddharth for phenomenal evening. Appreciate @AlmaImmigration @Aizada, @UofBeta and Signal for supporting Icons.

Imagine replacing 90% of your employees with a team of geniuses who have no idea how your company operates. Total chaos. Nothing works. That’s what AI feels like today. The missing piece is extracting all the domain knowledge from people’s heads and providing that as structured context to the models.

MMLU is saturated. HLE is getting there. We built Multimodal STEM HLE : for what comes next, and the top frontier labs publishing SOTA models are already using it. 1,100 PhD-level multimodal STEM problems that break Opus 4.6. Around 20% pass@1 on SOTA. Hard enough to expose reasoning failures. Solvable enough to generate real RL signal. Every problem requires joint reasoning over images and text, has a deterministic ground-truth answer, and was authored by a PhD-level domain specialist. 50-task public sample on @HuggingFace. Full pack available now. Links below.

Imagine replacing 90% of your employees with a team of geniuses who have no idea how your company operates. Total chaos. Nothing works. That’s what AI feels like today. The missing piece is extracting all the domain knowledge from people’s heads and providing that as structured context to the models.

Turing is hiring Strategic Project Leads at gigantic scale with a focus on coding and enterprise. This is the role for people obsessed with running a tight ship while building at the frontier of superintelligence. The job: own the human data programs that train every frontier model worth training. Work with all the frontier AI labs and neo labs. Turing is the only company in this space building both ends of the research and enterprise deployment loop. This is a founder-mode company. We want operators with the same posture. Ex-founders, consultants, investment bankers, finance operators, technical PMs, engineers who've run a program end to end. The bar: exceptional ability and entrepreneurial DNA. The best SPLs don't fit in the box. They break it and shape a new one. Comment if you're interested. Tag someone who should be. DM me or email jonathan.s@turing.com. I'll read every application personally.

The “it’s not AGI because machine intelligence is jagged” is dumb cope. It’s obviously AGI. If you had a friend who had a 130 IQ, could write production code flawlessly, could write academic papers of a high research caliber, pass any exam in any field with flying colors, create a sophisticate LBO model, draw technical diagrams perfectly, compose poetry in any language, and could find solutions to significant unsolved mathematical problems, you would call that person a world historical genius. Certainly, no single human has ever had intelligence that “general” before. Now you think it’s “not AGI” because it sometimes slips up and makes mistakes - so does any human that you would consider “extraordinarily intelligent.” The professor might forget a colleagues name that he has known for a decade. He is still considered intelligent. The math genius might be a little autistic and shy, unable to maintain polite conversation. Still intelligent. You might stare at the fridge for 30 seconds unable to find the butter, despite 5 million years of evolution perfecting your visual intelligence. We give intelligent humans a pass when they have jagged intelligence. So why the double standard? The qualities people list as “necessary for AGI” are important traits to have, but no longer pertain to intelligence. People will say things like “true AGI requires agency, long term goal setting, embodiment, self-direct action”. But none of those things are intelligence. Those are “things that humans have that AI lacks”. Raw intelligence, AI has it in spades. That other stuff - important yet, but broader than and different from intelligence. The unwillingness of people to acknowledge that AGI obviously exists and has existed for a while is due to a kind of anthropic chauvinism - a psychological need to believe that humans are superior in every respect, that we possess soft skills that no machine could replicate. Yes humans are different from machines, but if we are limiting the discussion solely to general intelligence, AI has it already. That battle is over. If you want to reframe the discussion to matters of human dignity and personhood, fine, but that’s not an AGI question. That’s something else. Just take the loss on AGI already. It’s over.

Introducing the Open MM-RL Dataset. A PhD-level multimodal STEM benchmark built for verifiable reasoning across physics, chemistry, biology, and math. Four STEM domains, one dataset -Physics: Quantum and Particle Physics, Condensed Matter and Materials, Electromagnetism, Photonics, and Plasma Systems, Astrophysics and Space Physics -Mathematics: Algebra and Structure, Discrete Mathematics, Analysis and Continuous Mathematics, Probability and Geometry -Biology: Evolutionary Systems, Molecular Mechanisms, Cellular Processes and Neural Biology -Chemistry: Chemical Structure, Reaction Mechanisms, Synthesis, Spectroscopy and Properties We're raising the bar.