As a Roman Catholic, it’s a headline that I would have never expected reading even two years ago

There’s two emotions that I really don’t like driving young American culture: anxiety and grievance. You can see it everywhere: left, center, and right. Yes, there are problems. But it’s not like Gen Z are poorer than previous generations economically when they were our age. The anxiety part baffles me. 60 years ago, lots of young people from the western country went on the hippie trail. And the world was a way more dangerous place back then. We lived with looming nuclear warheads, and for people in much of the world, people were much poorer. In the rich world, people lived with smog, leaded oil, and asbestos. Sure, maybe we learned we needed to be cautious of people. But being fearful of people has robbed us of so much joy that makes life meaningful.

The American healthcare system has a very ignoble reality: we are not achieving the optimal set of outcomes given the technological constraints and amount of resources we allocate. While the current technologies might not bring immortality, with better processes and resource allocation, people can live a lot longer and healthier lives. And the reason is because medicine is fundamentally a problem-solving discipline. I am not saying the American healthcare system is terrible at all. It is actually probably the best in the world in treating a demographically very unhealthy population, driven by lifestyle factors outside of medicine (i.e. diet, sedentary movement, violence). The vast amount of money in the field and a lighter amount of governmental intervention in compared to other countries in healthcare also incentivizes continuous innovation. But, the healthcare system can absolutely prevent millions of premature deaths without that much effort, such as better prevention systems, and also by getting more data through better diagnostics for sick patients. And the reason we haven’t been doing that, is that the field doesn’t have enough good problem-solving people and attention spans. The problem-solving nature of medicine originates from the 18-19th centuries when medicine becomes a formalized discipline. Our understanding of health and medicine, on the basis of the scientific method, is that it is a collection of biochemical and systematic cause-and-effect relationships. Today, the American healthcare system is highly income-unequal: wealthier patients see better doctors and get much better outcomes. The wealthier breast cancer patients do not have access to super radically different therapies as patient on Medicare. Rather, the wealthier patient is paying for the doctor’s problem solving ability, having a better understanding of the standard of care, and that the doctor can reason through the patient’s case and find a viable path to treating the disease. And it’s not just the primary disease that requires extensive problem solving. Healthcare problem solving also is about managing secondary factors, such as comorbidities and side effects. I very much hope that AI can better meet the need for healthcare problem-solving, especially for poor or rural patients around the country, and help close this income inequity in problem-solving.

Even as calculators have existed for a long time, and I myself use it to crunch numbers, I have always insisted on developing my mental math capabilities. This is very much because it is not every situation where I will have access to a calculator. It has become extremely useful: if someone pitches some ideas to me, and it smells like someone is full of BS, I can ask for some numbers, crunch them, and walk away. The death of mental math is without a doubt already visible in its impacts. If Americans weren’t so terrible at mental math, Americans would be much more capable at managing their finances. I am not looking forward on how machine intelligence erodes thinking. I am excited about the pursuit of knowledge it creates, that it can rigorously search through problem spaces, but I am not excited that it will create Swiss cheese holes in how humans understand their crafts. And this is a tool that precisely needs people to understand their crafts, because machine intelligence is not deterministic (and even if they are, poorly observable).

Since this quoted post had a nontrivial amount of traction, I am going to tell another story from this chapter of my life in college. While the post I quote tweeted can seem like a linear life path, I am open about the fact is not all sunshine and roses; there was plenty of struggle. And I empathize with people who have struggled. My “liberal arts” development was probably one of the most unusual of American college students. When I was 18 years old, in 2022, I took what I thought at the time was a huge gamble: to go to Switzerland, on exchange at EPFL as a computer science student in my last year of undergrad, as a fresh young adult. I basically was forgoing recruiting and gunning for Big Tech internships because I wanted to explore the world. I learned immensely during this time. I visited 13 countries, and was hopping between hostels, train stations, churches, museums, and town squares. I learned so much just by being in unfamiliar places, seeing people who lived very different lives as I did. While on exchange, I met students from at least 50 countries. Another more anti-establishment part of my heart was also developing, knowing how much American universities charge for liberal arts courses but not rigorously educating its students on style, rhetoric, or interpersonal skills. I have never been to Switzerland before then. The only thing I knew about the country was that it spoke three languages, German, French and Italian, and that it is really, really expensive. My best way of putting it is, I was very glad I went, it was an extremely formative chapter of my life, however I definitely would have enjoyed it a lot more and been a lot happier if I went at 20, 21, or 22 on exchange. I was intellectually and socially mature enough to do fine over there. But spiritually and emotionally I had some to catch up. For much of my time there, I had a language barrier. People in Lausanne spoke French. It was only towards the end of the exchange, after December, especially during the January exam period, that I finally learned and practiced enough French to be able to speak it conversationally compared to the meager public school French I studied. At the time, I also applied to CS PhD programs at around a dozen of universities. I treated PhD applications like undergrad apps and I was NOT knowing what I was getting into. My statements of purposes (SoPs) were unfocused and aimless. And I eventually was rejected by all of them. To this day, I do not regret applying, because I found out it was not the right path in life for me. The social environment was also very different. EPFL was much more academically “gloomy intense” than UIUC. My fellow exchange students were all at least 21-22, often 24-26, while I struggled with relating to the younger undergrad students speaking mostly in French. I was away from my happy, easygoing friends at UIUC. So, my least favorite part of going on exchange, easily, was the loneliness. And it says a lot, because I remembered being super broke at the time, I had to get creative to save money, but it never bothered me as much as being lonely. However, it was not a horrible time, otherwise I could not have remembered it so fondly. I made friends with a few exchange students and Swiss classmates. And I also saw many places that left deep impressions on me. I learned what it meant to just trust people. People who were kind and honest made my time there a lot happier, and below I’ll show some pictures of places I visited where I really trusted people. 📍Slovakia 📍Kraków, Poland 📍Ticino, Switzerland 📍Zürich Christmas night market

If you work at a startup, feeling slightly overwhelmed (though, not being burnt out) and being pulled in many different directions is not a bad thing. It means there are enough necessary work for you to optimize your workflows and become more efficient.

How does FelonyBench fit into this

I think the strongest argument against AGI right now is that current AI (especially LLMs) do not have a strong sense of time at all. Memory solves part of the time management problem, but not all of it. And I have not yet seen how world models properly address this; they have massive mathematical and reasoning power, but it is hard to see yet how they can manage the most critical resource in the universe. If anything, we can make the argument here that self-driving systems are closer to AGI.

When I saw Terence Tao writing his blog post worrying about AI’s impact on math research, I feel sad that a lot of really smart people are losing their self-worth as AI gets better. When I was in middle school, I thought I wanted to be a mathematician too! Then I did math competitions. I didn’t make Canadian Math Olympiad when I was in high school in Canada in 10th grade; and I barely missed the USAMO cutoff in both grades 11 and 12 after moving to Pittsburgh. Was I disappointed? Incredibly. I spent years on this craft. But I realized that the space of problems worth pushing the human frontier for is way bigger than just theoretical math. So, I decided to major in computer science. I went to UIUC CS, took many of the most useful undergrad and grad classes, and now I am building software infra and systems for chip design with @architectlabs . It’s not an easy problem! But it is an interesting problem. There’s always more work to do, more engineering processes to rigorously build and evaluate. And wonderfully, I get to work with smart, interesting colleagues. There’s plenty of interesting, meaningful problems to solve. For smart young people, you have both the neuroplasticity and also the lack of life + work experience commitments to choose any path you want to take. You can mold yourself down any path you find interesting. If it’s not theoretical math and its relatives (theoretical physics and theoretical computer science), there are so many problems in applied spaces, like hard tech, that desperately need smart people. Applied spaces demand creativity and cleverness just as much as the pure theoretical spaces. And you can get paid handsomely for your creativity; after all, clever applied processes will either directly save money or make more money on the same amount of resource inputs. Even if you already have money or don’t care about money, you can impact the world really positively with your work. If you find a way to do tumor mutation testing for cheaper or manufacture CAR-T cells for cheaper, you will help a lot of people. If you are good at designing systems, you’re in a lot of luck! Real world systems are messy. If you are smart, you can design rigorous systems and know when to make tradeoffs between doing things quick and dirty and when to systematize your processes. The world needs smart people to reason through trade-offs and allocate limited resources efficiently. There’s no shortage of meaningful problems to work through. For promising talented people, I encourage you guys to discern through what fields excite you, what fields give you meaning in life, and be adaptable. Life is long, fear and anxiety won’t get us anywhere. But optimism and curiosity will get us a long way.

Counterpoint: AI has made the education that I received in grad school far more valuable. Granted, I only spent a year doing a professional course-based masters at UIUC. The basic computer science principles that I learned in undergrad, I don’t think would have been as helpful in getting me a job especially compared to before AI. But I think grad school actually taught me how to think, because all of the classes were applicable to industry, after taking classes like: - computer security - distributed systems - advanced algorithms Agents can write code. But it takes humans and academic knowledge to discern what code to write, and whether the code is right for the problems you’re solving. And it is very difficult to get this fundamental understanding in industry, outside of classrooms.

High trust societies are a consequence of competent governance. For places to be governed competently, however, you need people who are willing to take on risks. Not everything will be done perfectly; but enough people will take risks that you feel progress around you.

One thing that the solved Millennium problems (and now the alleged race to solve whether P=NP) tell us is that even after a landmark problem is solved, there is plenty of mathematical knowledge to research. There are plenty of cases and things we don’t know, many of them in useful domains, especially considering many of these breakthrough solutions have been counterexamples. We still don’t know that much about the “under which conditions, whether something is true” cases, which will be quite interesting to explore.

We're a frontier lab attacking hardware development from all angles, and formal is another one of our directions. Excited to sponsor this hackathon as well!

Economics is so interesting because for the same issue you can have two extremely smart people who completely disagree with each other, both for valid reasons

I’m very curious how AI in biology would shape circulatory diseases. It seems like in oncology, there is a more defined playbook today: find mutations, try tune the microenvironment, use targeted inhibitors on immunotherapy, and voilà, you have new drugs and therapies. Circulatory diseases, such as atherosclerosis, feels like it is much more of a physics problem and less of an evolutionary behavior problem. Will be interested to see where this leads us.

This is a formulation of another relatively well-known problem in theoretical computer science, called maximum flow. However, even though max flow algorithms run in polynomial time, they generally are quite inefficient to compute. Heuristics can probably get us most of the way there without incurring huge computational costs and poor asymptotic time complexity.

Not gonna be as fast as ours :)

Introducing Project Redwood 🚀🚀 @architectlabs is a frontier AI lab bringing together talent from Anthropic, xAI, Google DeepMind, and seasoned leaders across the hardware industry. We've raised a $24M seed round to build AI systems for chip design. 我們 Architect Labs 是一個 frontier AI lab ，由各家 Anthropic, xAI, Google DeepMind 還有各種硬體專家們組成，我們在做的是 ai system for chip design，目前募資 seed round $24M - Today, every major hardware company has its own chip design workflow—but these organizations and processes have become so large and entrenched that truly revolutionary change is difficult. We’re starting from first principles to create an AI-native chip design workflow—one that enables chip development to finally move at the speed of AI. 現在每家大硬體巨頭都有自己的晶片設計流程，但很多都大到不能做革命性的流程改動；我們在做的是從零思考，創造 ai native 的晶片設計流程，讓晶片設計真正跟上 AI 的速度。 - Project Redwood - from a single specification, our AI system designed, verified, and deployed a chip in under two weeks—delivering 3.4× better performance per watt than NVIDIA Jetson on billion-parameter models including Llama, Qwen, and Kimi. It autonomously generated the RTL, verification, firmware, drivers, and kernels, co-designing the model, software, and silicon in one optimization loop. We acknowledge that silicon is the ultimate ground-truth. We’re taking our approach all the way to GDS. We intend to tape-out multiple improved families of Redwood co-designed for various use-cases, on TSMC. Project Redwood - 一份規格書，我們的 AI 系統在兩週內完成晶片的設計、驗證與部署。在 Llama、Qwen 和 Kimi 等模型，其 performance per watt 比 NVIDIA Jetson 高出 3.4 倍。從 RTL、驗證、韌體、驅動到核心 kernels，全部由 AI 自己寫，並在同一個 loop 中自主設計模型、軟體與晶片。當然，流片才是最終的驗證標準。因此，我們會將這套方法一路推進至 GDS，並計畫採用台積電製程，針對不同應用場景協同設計多個持續改良的 Redwood 晶片系列並完成流片。 - Full report on Redwood architecture and its autonomous design. Follow @architectlabs on X 我們有公開 Redwood 架構及其自主設計流程，歡迎去看論文～