"Not Even Wrong" Podcast
Investing in fundamentally new concepts and engineering practices with large impact.
Tesla’s FSD is being stalled by regulators on behalf of the legacy auto industry. Movie Longitude shows how watchmaker in the 18th century solved the longitude problem for British Navy. It took decades for the technology to be adopted and the movie shows why. The establishment was stalling by continuously asking for detailed explanations and overwhelming the watchmaker with scientific pedantries. Similar situation with Tesla FSD. Regulators are stalling to gain time for their constituency, legacy OEMs, not the public. FSD would save lives and give disabled, older and younger people more mobility if the regulators were more honest about the technology.
Knowledge and Truth and the job of the future. Inspired by Jessica Moss talk about knowing what is what. Discrimination as driver of knowledge, the ability to distinguish this from that. But how do we avoid a Wittgensteinian word salad arms race? Explanation. Epistemic uncertainty and aleatoric uncertainty. Concepts converge. By reducing aleatoric uncertainty (increase knowledge domain) we increase epistemic uncertainty (there are many more problems to be solved, and solvable with more data and compute. The job of the future is to reduce epistemic uncertainty in specific domains (aerospace engineering will work on AI models to increase knowledge about how to solve problems). Foundation models must follow truth seeking explanations. Most value. Demand for intelligence is infinite.
Quantum for AI and AI for quantum. Two presentations that both tackle quantum physics but from different angles. Researcher 1: Jiaqi Leng uses quantum computation to accelerate finding optimal point with lots of local optima. Interference, superposition and hamiltonian evolution offer a faster look at the landscape and accelerate optimal solutions. His PhD thesis is about setting the foundation for quantum computation to solve highly non-convex problems with lots of local optima. Researcher 2: Liang Fu. Use a neural net and transformer architecture to guess and approximate many electron interaction. A “first-principles AI” framework is introduced in which neural networks as universal and systematically improvable variational wavefunctions (guessing Hamiltonians of fermions when interacting) for many-electron quantum states. Crucially, these neural wavefunctions are optimized entirely by energy minimization, without training data or input physics knowledge. Input Hamiltonian and optimize with physics constraints.
AI will reduce bigotry because returns of collaboration go up with higher productivity. Complex societies with advanced economies have high return of collaboration. People choose diversity because collaboration is profitable and the opportunity cost of bigotry higher (focusing on kinship, ethnicity) The big question is, what will happen if AI reduced returns on collaboration (Coasean Singularity) and everybody becomes the CEO of a one man company? AI will actually increase returns of collaboration because higher productivity drives returns of collaboration. The more productive people are (they make more with same input of time, energy), the more they benefit from collaboration.
Discussing our essay ‘CRISPR: Much Ado About Very Little’. Gene editing started the same year as AI. Today AI is a trillion dollar industry while Crispr is still not much more than a science project. Why? Because the government has turned life sciences into a trust fund. Science must serve the public and not the other way around. Two things we want to see to invest in gene editing again; 1/2 Image net style data for life sciences to capture complex relationships (similar to LLMs and language) and 2/2 Real entrepreneurs enter the field, not academics with side jobs.
Book discussion “Buckeye” by Patrick Ryan. Coming of age novel about US between 1940 and 1980. Showing critical social and political developments through stories of human characters. Powerful. 1/3 The best history books are novels. Transcend time and location with human narratives. 2/3 Role of women evolves. Some things don’t change. Responsibility, accountability, trust and love. Women post WWII went through transition from tradition to liberation. Challenging. 3/3 Betrayal and lies. Even if you are lied to, forgiveness is best way forward. Actions in the book are not as crucial. It’s the way characters feel about what’s happening. Interesting literary technique.
Humanoid supply chain waiting for Tesla to deliver form factor and intelligence. Forcing function for scalable technology requires large, credible player to commit to size. In the case of humanoids it’s new supply chain. Component supplies must wait for the right mix of components, chips, networking solution and intelligence. Intelligence dictates sensor suite and sensor suite determines intelligence. Tesla best positioned because they can iterate around use cases like factory, space AI data centers and Cybercab maintenance.
Waymo is competing with Tesla the same way Yahoo competed with Google. Driverless taxi on digital rails versus Tesla’s general intelligence. Short-term gain, longterm pain. Tesla scaling will dwarf competitors. Waymo and Uber are competing with price in markets where Tesla is present. Waymo advertising legroom and other perks. Predictable competitive reaction towards Tesla's lower cost and higher value offering. Tesla is disrupting transportation form both sides, lower cost and higher comfort. Incumbents like Waymo and Uber will suffer psychological breakdown since they can’t react. Only way to compete is to replicate supply chain.Chinese OEMs are real competitors because they are replicating Tesla supply chain. We expect 130 Billion Dollar revenue by 2030 for Tesla.
We live in a tech bubble and the money is being made elsewhere. That’s exhausting. Lots of wealth has been generated in the past nine months. And it’s not in tech. AI is driving everything but the money is being made elsewhere. Copper mining stocks have tripled in the past nine months. Memory makers, steel makers, turbine blades makers and many other basic industries are thriving while we are talking about Space AI and Cybercabs. It’s exhausting. What do we do? Stick with our mission. Use Elon’s way; stay on path and aggressively error correct.
The purpose of science is technological deterrence and survival of species. In war and in morals. We are the sorcerer and we must control the brooms. Give AI good goals so it can do science for us. Three questions. 1/3 What is the purpose of science? 2/3 How can we make sure AI follows human states purpose of science? 3/3 How do we actually implement that? What is the best way to organize society in light of this massive industrial revolution? Organic. Error correct. The purpose of science is to enable strong technological deterrence and morals. Survival of society. Protect against external and internal enemies. Moral infrastructure to handle technological power. Goal driven AI is the most promising architecture. ‘Era of experience’. Preserve fragile equilibrium of liberty and stability. Ownership of science important. Responsibility to pursue overarching goals. What is a good outcome? What is success in science? Today, science goals are not well defined. Leads to inefficiency and corruption. Good goals (technological deterrence). Bad goals (climate change). Goals must have measurable verification and accounatibclty. Open models vs. closed models. There is no such thing as independent, open model. All AI is biased. Pursue truth and error correct.
Space AI will lower cost of intelligence by 100x or more. Today AI is power constrained. Not just power, but also political. Nimby and geopolitics are making terrestrial energy production ever more expensive. Space AI will shift focus from token/Watt to token/kg. New industry will arise and lower cost of intelligence by more than 100x. Demand for intelligence is infinite and with massively lower prices it will grow exponentially. Space X is space AI company. Tesla is chip and terrestrial intelligence company. Engineering problems to be solved; Starship reusability and lower cost per kg to orbit. Lower weight chips and optimizing for compute/kg. Lower weight solar cells with higher efficiency. Low cost and low weight radiation. Optical networks for compute and communication.