CEO @koop_ai and https://xitter.cf/t.co/4uqGTjayAB – Certify and insure AI operating in the physical world IN BOTS WE TRUST 2026 Report: https://xitter.cf/t.co/CEEdVBB8TM

New Jersey, USA
Joined August 2019
I was invited to recreate the iconic 1995 "Old Glory Insurance" skit from @nbcsnl. Correction: I invited myself. Because a 1995 robot insurance commercial desperately needed a 2026 update. Back then, the joke was that robots were coming for us and nobody would insure against it. Thirty years later, @koop_ai has helped pioneer the robot insurance category, risk-controlling and insuring roughly a quarter of the U.S. robotics market. That gave us a front-row seat to something nobody had quantified before: what actually makes a robot risky, and what makes it trustworthy? Today we're putting the data behind that question out into the world. IN BOTS WE TRUST 2026 is the culmination of five years of underwriting robots in the real world, and our attempt to give the robotics industry a shared, quantifiable way to build and prove trust. 392 companies. Five years of quotes, policies and claims. Hundreds of thousands of data points. One question: can we trust the robots? The answer might surprise you. P.S. More importantly, how did we do with the skit?
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Sergey Litvinenko retweeted
Digging out after 10 days in China Will write more / share more later Top of mind: 1 - energy is everything 2 - manufacturing is not won through cheaper humans but through excellence at manufacturing processes and built up industrial clusters around them 3 - robotics is not lost yet / needs real data at scale / this game is still open 4 - robotics hardware will be like ai models - all the money will be made in the applications and unique data sets on top of mostly commodity hardware assemblies
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Sergey Litvinenko retweeted
Your SPRS score is a legal statement. Recent False Claims Act settlements show why defense contractors must back every compliance claim with evidence.
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Robot safety compliance and insurance are critical to unlocking commercial deals. Source: forbes.com/sites/johnkoetsie…
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Consider the sheer dimensionality of physical world data: spatial geometry, motion, vision, audio, touch, force, temperature… and how all these signals interact and evolve over time. Now multiply that by every possible object, environment, task, physical interaction, and edge case. The resulting space of possible experiences is almost incomprehensibly vast. World data also captures the consequences of actions: what happens when an agent actually interacts with its environment. Robotics data collection is a practically bottomless well.
The winning robotics data business has a much higher terminal value than investors are underwriting today. Robotics will be a long race with many twists and turns, but when the ChatGPT moment hits, the winning data player will be in a very strong strategic position to capture token value. They will have large revenue, a strong balance sheet, talent density, deployments experience, and hardware supply chain knowledge. When it comes time to blitzscale they will be amongst the players that can capture the most value. The reason this is misunderstood today is because most people are using traditional LLM data businesses as a prior for robotics data businesses and what their terminal value could be. I posit this is incorrect framing. Because the model and harness are integrated in LLMs, there is very little room for data players to play meaningfully in the token value flow in the long term. In robotics this is exactly the opposite, the model and harness (hardware) are separate, the pretraining era is just beginning, there are many more dependencies on distributors/deployers, and there is a large surface area for other players than just models to capture value. Open source will also play a larger role earlier in the robotics race, as all prior LLM learnings will be incorporated as the race is run. This also creates opportunities for long term infrastructure players. There is a massive amount of terminal value in winning robotics data and participating in the path of the industry from a position of strength. From first principles: The endgame of robotics will take place over the next decade. Empirically, today, no player has enough data, enough model intelligence, or sufficient hardware to facilitate a fast revenue takeoff in the field. No player has reasoned through the right sales motion either, which can take years to develop pilots and move to production. Serious players are correctly focused on scaling up pretraining/deep learning vs. who has the best model, deployment, or hardware today. The robotics data business takes participants on a global hunt to build data, supply chains, and hardware. Along the way it also teaches deployment and deep knowledge of SOTA model development. Winning data participants also get the first crack at the still nascent robotics RL market, which will likely be the next large revenue stream after pretraining. As they compete to win data, data players must develop incredible talent density across both Asia and the US. The winning data business will have both Asian and North American DNA. Canada 🇨🇦 encompasses an interesting cultural mixture of both of these parts of the world. Bullish Canadian Robotics Mafias, more specifically Waterloo/Toronto. So: What would constitute a strong strategic position to capture value once the ChatGPT moment of robotics hits? You need a large revenue base, a strong balance sheet, and talent density + experience across data, deployments, hardware, supply chain, and RL. The hyperscalers, the frontier labs, and the winning data player all qualify. Anyone under time pressure in what will be a long race is in a weaker strategic position in my view. Given the costs of integrated hardware+software+deployment, this would actually be most robotics-native players. They will depend on VC interest, which can be fickle.
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Sergey Litvinenko retweeted
Building next-gen weapons, robots, or defense software? Security training is what keeps your other controls working. Protect the mission. Train the people.
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Sergey Litvinenko retweeted
What does a dollar of robot revenue pay for liability insurance? It depends on what the robot touches. The "IN BOTS WE TRUST 2026" report ranks robot categories by rate on revenue, with the autonomous mobile robot at 100: ➡️ Drones = 50 ➡️ Inspection = 86 ➡️ AMRs = 100 ➡️ Robot arms = 172 ➡️ Material handling = 258 ➡️ Earth moving and moving = 6-7x the AMR Price ranks exposure, not technology. Robots working near people, goods and buildings pay the most, because that's where property damage concentrates. Read your rate against this ladder as the market's view of your exposure.
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Sergey Litvinenko retweeted
87% of robotics companies report a safe-stop system. 6% publish a safety page. Controls already cut liability rates 25–65%, but terms won't follow until the evidence is certified. From IN BOTS WE TRUST 2026 → koop.ai/reports/in-bots-we-t…
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Thanks @RoboStrategy for the shoutout A billion bots will require financial protection – @koop_ai is the answer
Underwriters and claim files tell two different stories about robot risk. Underwriters worry most about property damage and robots hitting people while the claims associated to this category are relatively low. Whereas Cyber barely registers, at 8% of reviews but caused 34% dollars paid in claims. Insurance is an interesting lens as insurers price risk with their own capital, and claim files record what actually went wrong.
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Sergey Litvinenko retweeted
I was invited to recreate the iconic 1995 "Old Glory Insurance" skit from @nbcsnl. Correction: I invited myself. Because a 1995 robot insurance commercial desperately needed a 2026 update. Back then, the joke was that robots were coming for us and nobody would insure against it. Thirty years later, @koop_ai has helped pioneer the robot insurance category, risk-controlling and insuring roughly a quarter of the U.S. robotics market. That gave us a front-row seat to something nobody had quantified before: what actually makes a robot risky, and what makes it trustworthy? Today we're putting the data behind that question out into the world. IN BOTS WE TRUST 2026 is the culmination of five years of underwriting robots in the real world, and our attempt to give the robotics industry a shared, quantifiable way to build and prove trust. 392 companies. Five years of quotes, policies and claims. Hundreds of thousands of data points. One question: can we trust the robots? The answer might surprise you. P.S. More importantly, how did we do with the skit?
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how far is AI from human use? chatbot ➡️ browser use ➡️ computer use ➡️ robot use (soon) ➡️ human use (when?) ➡️ company use ➡️ city use ➡️ country use ➡️ earth use ➡️ solar system use
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Sergey Litvinenko retweeted
Fuck the Doomers. Fuck the Decels. Fuck the Commies. Access to AI is a right. Owning compute is a right. Come and take it. Over my dead body. Join us in the fight, anon.
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Sergey Litvinenko retweeted
We’re thrilled to welcome @glennkoop as Koop's Chief Technology Officer 🚀 Glenn previously led engineering at Invisible AI, building AI systems deployed at manufacturers including Ford, Nissan, and Toyota to improve safety, efficiency, and production performance. At Koop, he’ll lead technology strategy and engineering to advance our AI-powered compliance and insurance platform, helping companies across AI, robotics, defense, and other frontier industries secure and insure their mission-critical operations. Welcome aboard, Glenn!
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The best marketing is an incredible product
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IN BOTS WE TRUST
Trust is going to be the biggest barrier (and driver) of personal agent adoption.
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Physical AI will be AT LEAST 10x digital AI
BREAKING: Nvidia declares physical AI could become “10x larger than digital AI” with Jensen Huang predicting every industrial company will become a robotics company.
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Sergey Litvinenko retweeted
Never on budget, never on time. @NASA can be a powerful customer, but its procurement and funding cycles are slow and unpredictable. This paper explains how startups can navigate that system, especially through SBIR/STTR programs, which can provide faster contracting, follow-on funding, and a clearer path into NASA programs. Link in comments 👇
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The whole economy (or the world for that matter) will be powered or touched by AI Unbelievable results WATTBA
Absolutely insane. Nvidia, $NVDA, just guided $108 BILLION in revenue for Q3 alone. And, this guidance assumes ZERO data center compute revenue from China. This builds on the record $96.2 billion in revenue posted for last quarter for a projected total of $204.2 billion in 6 months. We now have a $5+ trillion company is seeing +106% year-over-year revenue growth. There has never been anything like what we are seeing right now with AI. This is arguably the biggest technological revolution of all time.
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