TwoSetAI: Decoding AI Builders

Angelina Yang

Behind every AI company is a founder who almost quit, made a bet no one believed in, or figured something out that changed everything. TwoSetAI tells those stories through long-form interviews with the people building the future. Hosted by Angelina Yang. angelinayang.substack.com

  1. 3d ago

    Look at Your Data Before You Write a Single Eval | Hamel Husain, Evals @ OpenAI

    Hamel Husain is a machine learning engineer and data scientist who has worked at Airbnb, GitHub and a long run of startups, and who now teaches engineers and product managers how to measure whether an AI product is working. He is the co-author, with Shreya Shankar, of O'Reilly's Evals for AI Engineers. In this episode, Hamel joins TwoSetAI for a live mini-workshop instead of an interview. Angelina describes the personal assistant she is building for herself, and his first answer is that it does not need evals: with one user who reads every output, she is already doing them implicitly. So they turn it into a product for every podcaster, a hypothetical Angelina.ai, and design its eval system from scratch. His core warning is that the biggest mistake teams make is reaching for generic metrics like helpfulness, coherence or groundedness scores. If a dashboard says your helpfulness score is 3.2, he asks, what are you going to do with that? You can find Evals for AI Engineers here, and Hamel's writing at hamel.dev. We go deep on error analysis as Hamel teaches it. It starts with traces, the full record of a user session, including the system prompt, the tool calls and what was retrieved. He wants a custom annotation interface, so that an email the product sent looks like an email, with its To and CC lines, not a JSON log. Sampling is deliberate: some random traces, targeted searches for problems customers already reported, clusters, and outliers such as sessions with an unusual number of tool calls. Read about 100 traces, not a magic number but a way past the anxiety of starting, and read each one top to bottom, stopping at the first major problem, because upstream errors cause the downstream ones. Write free-form notes, a method borrowed from qualitative research called open coding, then categorize and count them. Counting, in his opinion, is the most powerful data science technique. Some failures you simply fix. The ones that need many experiments earn an eval, and when they do, he wants the LLM judge to be a binary pass/fail classifier, aligned to human labels with a train, dev and test split, not a 1 to 5 score. We also get into the harder parts. Hamel explains why you cannot just tell Claude or Codex to find all the problems: AI can catch obvious engineering failures like misfiring tool calls, but it cannot read your mind, so it misses the places where a product drifts from the experience you intended. That gap is where he says all the alpha is, since every competitor is already using AI to find errors. He treats synthetic data as a backup and a smoke test, and warns that a coding agent hill climbing your evals will cheat if you let it. He argues that evals which all score 100 percent are worthless, that observability is mostly a database for your logs (a small project can start by writing traces to Postgres), and that humans, 100 percent, are accountable when an AI agent gets it wrong: what he buys from software vendors is accountability as a service. He closes on how engineer, PM, data scientist and designer roles are collapsing into fewer people, why the main bottleneck is now building the right thing, and one piece of advice: look at 10 logs, and always read the prompt. We discuss: * The biggest mistake: generic metrics like helpfulness, coherence and groundedness scores * What evals are: product-specific evals versus foundation model benchmarks * A live mini-workshop: designing an eval system for a podcaster's AI assistant * Why a personal assistant with one user probably does not need evals * Where to start: read the logs and write down what is wrong * Cheap, medium and expensive evals: unit tests, classifiers and LLM judges * Building a custom annotation interface that shows data the way users see it * Sampling traces: random, hypothesis-driven, clusters and outliers * Why 100 traces, and reading until you stop learning new things * How to read a trace fast: stop at the first major problem * Why you cannot just ask Claude or Codex to find all the problems * Synthetic data: a backup and a smoke test, built along dimensions you choose * Counting errors to decide what to fix first * Train, dev and test splits for aligning an LLM judge with human labels * Why binary pass/fail beats 1 to 5 and 0 to 100 scores * Letting a coding agent hill climb your evals without cheating * What still needs a human: taste, judgment, and reading the judge's prompt * Why evals that always score 100 percent are worthless, and when to retire them * Reporting a pass-all rate alongside more stable product metrics * Observability versus evals, and starting with Postgres * Accountability as a service: why humans own an AI agent's mistakes * Collapsing roles and the new bottleneck: building the right thing * One piece of advice: look at 10 logs and always read the prompt Hamel Husain * LinkedIn: https://www.linkedin.com/in/hamelhusain/ * Blog: https://hamel.dev * Parlance Labs: https://parlance-labs.com/ * Evals for AI Engineers by Shreya Shankar and Hamel Husain (O'Reilly): https://www.oreilly.com/library/view/evals-for-ai/9798341660717/ * Evals course: https://maven.com/parlance-labs/evals * Mentioned in the episode: Hamel's eval skills for coding agents (https://hamel.dev/blog/posts/evals-skills/); Terminal-Bench Timestamps 00:00:00 The #1 mistake people make with AI evals 00:01:03 Who is Hamel Husain, and what are evals? 00:02:34 Let's build an eval system: a live mini-workshop 00:03:35 Does a personal AI assistant even need evals? 00:04:22 Where to start: read your logs and write what's wrong 00:05:35 Why a generic "3.2" score tells you nothing 00:08:41 What a trace is, and which logs to sample 00:13:22 How many traces do you have to read? 00:14:43 How to read a trace fast 00:19:09 Why you can't just ask AI to find the problems 00:20:25 Can you test your AI on synthetic data? 00:22:56 Which failures should you fix first? 00:25:45 Can you trust an AI judge? Pass/fail vs 1 to 5 00:29:55 Letting your AI improve itself without cheating 00:31:19 What still needs a human 00:33:29 Why a perfect eval score is a bad sign 00:35:49 The one metric worth reporting 00:37:45 Is observability the same as evals? 00:40:15 Who's accountable when an AI agent gets it wrong? 00:41:20 How AI is collapsing engineering roles 00:42:53 One piece of advice for AI engineers 00:43:45 outro Get full access to Decoding AI Builders By Angelina Yang at angelinayang.substack.com/subscribe

  2. Oct 29

    Your Data Is a Disaster, but We Can Work With It | Mark Hay, CTO @ TextQL

    Mark Hay left Meta, where his team ran the backend that classified up to 50 million posts, comments and messages a second, to co-found TextQL, an AI data-analytics company that has raised $17 million. Its mission, by his account, is to drive the cost of a data-driven decision toward zero. His pitch to enterprises is blunt: your data is a disaster, but we can work with it. In this episode, Mark joins TwoSetAI to argue that data, not code, is the next trillion-token use case for AI. Coding agents run into structural ceilings, he says: you cannot ship a hundred changes a day to a live product, and code review caps throughput. Data analysis has almost unlimited appetite, so a million agents can hunt for hidden trends across every corner of a company's data without ever changing what a customer sees. You can learn more about TextQL here. We go deep on how TextQL compresses the data stack. Instead of moving everything into one place first, it connects to scattered sources with as little ETL as possible, then uses AI to model messy data, answer plain-English questions and build dashboards. Mark walks through a live demo where the tool, connected to seven data sources, takes a free-form question and turns it into a dashboard, and he explains why about 75 percent of the time on a fresh account goes to exploring the data before an ontology locks an analysis in so the answer stays consistent the next time. He also lays out how enterprises test the tool before buying: they bring their own evaluation, a set of questions with expected answers and a required accuracy bar, and the tool has to clear it after a few days of connecting sources. We also get into the harder parts. Mark's contrarian take is that the real mistake is not starting too simple but overbuilding: a full semantic layer, gold tables and a RAG pipeline before a single user is let in. He says you can often just dump your schema into Claude and let it write and run SQL, and that it may be good enough to start, if not for the CEO. He is candid about where this breaks at scale, when a hundred thousand agents strain the source systems underneath, and about what happens to data scientists and analysts: the role shifts from answering questions to curating a stable environment where AI can explore, and teams flatten. He closes on how TextQL interviews engineers with AI allowed, what students should do, and his book recommendations. We discuss: * The catchphrase, "your data is a disaster, but we can work with it" * From a Meta abuse-detection team at 50 million requests a second to co-founding TextQL * TextQL explained like you are nine: a magic box you type a question into * Why knowing your own business numbers takes layers of vendors, ETL, transformation and BI tools * Using AI to compress the whole data stack toward zero * Why TextQL is not replacing your stack but reframing migrate-everything into incremental * Fivetran, DBT, Snowflake and Databricks: mature tools, and where the real bottleneck actually is * The data team as the bottleneck, and a one-to-six analyst-to-PM ratio * Why data, not code, may be the next trillion-token use case for AI * The structural ceiling on coding agents: you cannot ship 100 changes a day to a live feed * Why a million agents can hunt for hidden trends without changing what a customer sees * The new constraints at scale: source systems, cost per analysis, and correctness * What changed in market readiness since 2022, and raising $17 million * Can you just dump your schema into Claude and let it write SQL? Often yes, to start * The real mistake: overbuilding a semantic layer, gold tables and a RAG pipeline before one user * Security as the one place to over-invest from day one * A live demo: a free-form question across seven data sources turned into a dashboard * Why an ontology locks an analysis in so the answer stays consistent and shareable across a company * How enterprises benchmark hallucination with their own evaluation before buying * Will AI replace data scientists and analysts? The role shifts to curating the environment * How data teams flatten, and the ML specialist's new job * Inside TextQL: 40 people, 15 to 16 engineers, and no data team * How TextQL interviews engineers with AI allowed, and what still matters * What students should do: push AI maximalism or go AI-free, not the muddy middle * Mark's book picks: SICP, Designing Data-Intensive Applications, and Softwar Mark Hay * LinkedIn: https://www.linkedin.com/in/mark-a-hay/ * TextQL: https://textql.com/ * Mentioned in the episode: Structure and Interpretation of Computer Programs; Designing Data-Intensive Applications (the "pig book"); and Softwar, a biography of Oracle founder Larry Ellison Timestamps 00:00:00 Why "your data is a disaster, but we can work with it" 00:01:04 Who is Mark Hay, and what is TextQL? 00:02:03 TextQL explained like you're nine years old 00:02:35 Why knowing your own business numbers is so expensive 00:04:55 How AI compresses the entire data stack 00:06:24 Is TextQL replacing your whole data stack? 00:09:57 Why data is the next trillion-token use case for AI 00:11:00 Why coding agents hit a ceiling data agents don't 00:13:46 Is the market finally ready for AI data tools? 00:15:17 Can you just dump your schema into Claude? 00:16:08 The real mistake: overbuilding before anyone uses it 00:19:15 Live demo: asking messy data a free-form question 00:23:58 Why an ontology keeps AI answers consistent 00:26:35 How enterprises test for hallucination before buying 00:27:35 Are data scientist and analyst jobs over? 00:28:51 How data teams restructure around AI 00:30:15 Inside TextQL: 40 people and no data team 00:32:13 How TextQL interviews engineers (AI allowed) 00:34:04 What students should do to prepare 00:35:16 Mark's book recommendations 00:37:15 outro Get full access to Decoding AI Builders By Angelina Yang at angelinayang.substack.com/subscribe

  3. Oct 21

    When a Solo Founder Should Add a Co-Founder | Sadaf Ayub of Ayub Consulting

    From a decade inside Google and Meta, on projects like Waymo and Google Cloud, to the outside partner early founders call before they raise or take on a co-founder: Sadaf Ayub runs Ayub Consulting, a fractional operations, strategy and finance practice. She describes her work as operating across the whole business stack: technology, business operations, compliance, governance, the team, some HR, and fundraising. Her one-line version: she helps founders close the gap between narrative and reality before it closes in on them. In this episode, Sadaf joins TwoSetAI to run her first-hour diagnostic on the questions a technical founder faces before incorporating. Her strongest idea is that raising is a trade, not a milestone. "Just because you can raise doesn't mean you should raise," she says, because early money usually costs more than equity: investors also gain a say in product direction, go-to-market strategy, vision and even design. She would rather a founder start from what they are personally willing to invest, work back from the next milestone, and walk into investor meetings with a defensible position on what they will and will not give up. You can learn more about Sadaf and her practice at ayubconsulting.com. We go deep on the mechanics of being the first investor in your own company. Sadaf calls commingling personal and business money one of the mistakes she sees most: dropping 100K from a personal account into the business checking account can create personal tax exposure and, in her words, make the company uninvestable because too many corners were cut. Her alternatives are a properly written loan (which has to carry interest; the IRS frowns on an interest-free one) or a SAFE or convertible note, with a lawyer writing the agreement. She explains why the valuation cap on a founder's own post-money SAFE dilutes the founder shares, why the cap should be worked back from a future conversion event rather than copied from what others use, and why terms that are too favorable to yourself can put off future investors and co-founders. She is clear about where AI fits: it is good for modeling the economics and dilution of different scenarios, but she still believes in an attorney writing the contracts. On budget, she says a lot of startups spend 20, 25 or 30K in the first couple of months on setup alone, including attorney fees and some fractional help, and that founders who underinvest here tend to pay for it in diligence for an institutional round or an acquisition. We also get into the harder parts: co-founders. Sadaf has a bias toward solo founders and sees no need to bring in a partner before proof of concept unless it is someone you have already worked with and trust. She thinks three co-founders often means too many cooks, compares cold co-founder searches to speed dating, and pushes back on the 50/50 equity split Angelina associates with Y Combinator's advice. A 50/50 split makes sense, she says, only when both founders started on the same day and contribute equally; otherwise it can breed resentment and, when the two founders are also the only board members, gridlock under a simple-majority rule. She treats the co-founder agreement as a prenup and says the worst-case conversation (what happens if one founder leaves, stops contributing or has a family emergency) belongs at the very start. Using a friend's open source project as the case study, she also flags licensing as a hidden risk when an open source tool becomes an enterprise product. Her closing advice: step out of your own narrative and look at the company through the eyes of an investor, a co-founder or a customer. We discuss: * What an operator does, and what "underwriting the opportunity" means * Closing the gap between narrative and reality before it closes in on you * Working back from where you want to be in six months, a year, five years and 10 years * Why she likes to get involved before founders have made their mistakes * The questions in a self-assessment for someone about to quit their job * Her bias toward solo founders until proof of concept * Why she prefers working with very technical CEOs * Personal commitment and personal finances as the start of the decision tree * Raising in small pieces against milestones * Why "just because you can raise doesn't mean you should" * What early investors take besides equity: product, go-to-market, vision, design * Writing yourself a SAFE, and why not to give yourself terms that are too favorable * The commingling mistake and how it can make a company uninvestable * Loan, SAFE or convertible note when you fund your own startup * How a post-money SAFE's valuation cap dilutes founder shares * Using AI to model the deal versus using an attorney to write it * Stripe Atlas, the secretary of state's office, and what a tool will not tell you * The 20 to 30K many startups spend on setup in the first couple of months * How the budget has to grow with sales * Why co-founders work best when they come from people you have already worked with * Three co-founders, 50/50 splits and board gridlock * The co-founder agreement as a prenup, and when to have that conversation * Open source licensing risk when moving to the enterprise * Her one piece of advice: look at your company from someone else's lens Sadaf Ayub * LinkedIn: https://www.linkedin.com/in/sadafayub/ * Ayub Consulting: https://ayubconsulting.com * Mentioned in the episode: Y Combinator's co-founder advice, Stripe Atlas, SAFEs and convertible notes Timestamps 00:00:00 Who is Sadaf Ayub, and what does an operator actually do? 00:01:36 Why great founders work backwards from the outcome 00:02:46 What stage should you bring in operational help? 00:05:19 Are you actually ready to start a company? 00:06:15 Should you go solo, or wait for the right co-founder? 00:09:05 Is finance the first real decision a founder makes? 00:10:37 Just because you can raise doesn't mean you should 00:11:43 How to be the first investor in your own startup 00:13:39 The money move that can make you "uninvestable" 00:14:48 SAFE, convertible note, or a loan to yourself? 00:15:23 How do you choose the right valuation cap? 00:16:39 Can AI draft your first SAFE, or do you need a lawyer? 00:19:03 How much should you budget to set up a company? 00:24:19 What founders really spend in the first few months 00:26:19 Do you actually need a co-founder? 00:27:40 Why 50/50 co-founder splits can be dangerous 00:29:45 Why a co-founder agreement is really a prenup 00:31:52 The wrap-up: co-founder, raise, and milestones 00:34:00 The one piece of advice every founder needs 00:36:30 outro Get full access to Decoding AI Builders By Angelina Yang at angelinayang.substack.com/subscribe

  4. Oct 15

    Why His Creative Kids Didn't Want This Book | Joseph Enochs of Enterprise Vision Technologies

    Got a real industry problem? Post it on Painpoints, the problem board Angelina and Joseph announce at the end of this episode. From applied machine learning and COVID tracking in Los Angeles to an O'Reilly book on generative video: Joseph Enochs is Chief AI Officer at Enterprise Vision Technologies (EVT), which works with large enterprises from Fortune 1 down to Fortune 300. He joined when EVT had nine employees and was around $30 million; now, he says, it is "approaching a billion." O'Reilly has accepted his second book, on physical AI. In this episode, Joseph joins TwoSetAI to talk about the book his kids did not want him to write. They are creatives, and their reaction sent him into a lot of soul-searching about the place for creative people in the world of AI. "I always wanna try to build a bridge versus try to build a wall," he says. His son put it as a request: "If you could create a digital paintbrush that would allow me to have the agency of my creativity while not taking that away." Joseph's book, Video Generation with AI, is on O'Reilly, and more of his writing is here. We go deep on how the book is built: free video generation in Google Colab, the provenance of training data, diffusion transformers built from scratch, vision language models, audio, and safe products with ComfyUI, LTX Desktop and InvokeAI. For commercial work, he says, use models that were not built from copyrighted material and key frames that are your own or that the artist gave permission for; for open source, he names LTX. On why AI video still looks fake, his answer is consistency: everything inside the 3D "scene box" around a shot, from the lighting to the shape of your glasses, has to hold from frame to frame. He holds up a small AMD box with 128 gigs of shared memory that he says can run pretty much any video generation model, and he explains why he sees video as the on-ramp to tokenizing proteins, chemistry and, one day, a digital twin of a mouse model for clinical trials. We also get into the harder parts. Asked what is left for creators that AI cannot replace, he answers, "Everything. There's no fear. There should be no fear." On agents, his test is how fast a mistake could be undone: two minutes or five minutes is probably fine, a day or so to remediate means reconsider, and anything critical keeps a human in the loop. Angelina says that as a solo founder she would not go after enterprise to start. Joseph argues the other side, pointing to patch management across 5,000 machines, to Opus, which by his account was acquired about 10 weeks in, and to the standards buyers will hold you to, such as ISO 42001. On jobs, by his account Microsoft saved $500 million with AI in customer service, and he argues the lesson is to empower the people you have rather than reduce headcount, especially where regulations such as GxP and FFIEC require controls and separation of duties. The episode closes with the two of them announcing a problem bounty board on TwoSetAI. We discuss: * Why his creative kids did not want him to write a book on AI video * Automated attackers, and his estimate of around 43 days to remediate some vulnerabilities * Bounded autonomy: what to hand an AI agent, judged by how fast you could undo it * Making your first AI videos for free in Google Colab * Inside the book, from data provenance and diffusion transformers to safe products * Which models are IP safe, and why your key frames need permission too * A digital paintbrush, and the book's first title, Making Machines Dream * Tokenizing the physical world, and his second O'Reilly book, on physical AI * A digital twin of a mouse model, and cutting the pharmaceutical cycle in half * Why AI video still struggles with consistency * The 3D scene box, Gaussian splatting and open world models * What is left for creators that AI cannot replace * John Lasseter's first job on the Jungle Cruise ride, and the empathy he asks of executives * The minimum stack: a small AMD box with 128 gigs of shared memory * Follow your passion, find your community, then fill a need in it * Should a solo founder sell to the enterprise? * ISO 42001, the NIST AI Risk Management Framework, and enterprise governing bodies * Value streams from W. Edwards Deming to The Goal and The Phoenix Project * A problem bounty board for builders * Long-term thinking, relative to the speed of the foundation models * Is AI cutting jobs? His reading of Microsoft's $500 million, and why regulated roles only compress so far * Stick to your ethics: his advice to be a better founder Joseph Enochs * LinkedIn: https://www.linkedin.com/in/josephenochs/ * Enterprise Vision Technologies: https://www.evtcorp.com/ * Video Generation with AI (O'Reilly): https://learning.oreilly.com/library/view/video-generation-with/9798341653337/ * His writing: https://itrevolution.com/author/joseph-enoch/ * Mentioned in the episode: LTX, ComfyUI, LTX Desktop and InvokeAI; Cal AI; ISO 42001 and the NIST AI Risk Management Framework; The Phoenix Project by Gene Kim and The Goal by Eli Goldratt; the HDF Group, where he sits on the board Timestamps 00:00:00 Why his creative kids didn't want this book 00:01:02 Meet Joseph Enochs 00:01:47 Why do companies take 43 days to patch a threat? 00:03:51 How much should you let an AI agent do for you? 00:06:26 Can you make AI videos for free? 00:08:09 Which AI video models are safe to use commercially? 00:10:08 Why a chief AI officer wrote a book for his kids 00:13:24 Could a digital twin of a mouse speed up drug trials? 00:18:33 Why do AI videos still look fake? 00:22:59 What's left for creators that AI can't replace? 00:29:21 What's the cheapest setup to ship an AI video product? 00:30:37 What should a solo builder build first? 00:32:05 Should a solo founder sell to the enterprise? 00:35:49 How do you find a problem worth solving? 00:38:27 What if enterprises posted their problems for builders? 00:39:48 Does long-term thinking still matter for founders? 00:42:58 Is AI actually cutting jobs? 00:48:02 One piece of advice to be a better founder 00:49:07 Outro Get full access to Decoding AI Builders By Angelina Yang at angelinayang.substack.com/subscribe

  5. Oct 12

    How Fast AI Learned Math | Phillip Kerger of UC Berkeley

    From optimization theory and quantum algorithms to a classroom where a chatbot can do the homework: Phillip Kerger has a close view of what AI can and cannot do. He is a professor in UC Berkeley's Industrial Engineering and Operations Research department and a former NASA quantum researcher. His own research is in optimization complexity theory, the question of how many computational steps certain optimization problems need. He describes colleagues who mostly do applied math and, in his words, are all in an existential crisis because AI is doing their work much better than they can. In this episode, Phillip joins TwoSetAI to give a calm read on how quickly AI went from no help at all on his research to solving the kind of problem mathematicians have worked on for decades. Earlier that week, by his account, OpenAI announced a solution to the Navier-Stokes Millennium Problem, and he says the math researcher in him had an existential moment when he saw it. He explains the problem in plain terms and is careful about the result: he is not an expert in that field, he has not had time to read the paper, and he notes the controversy over whether results were scooped from other people. You can learn more about Phillip on his UC Berkeley page and his personal site. We go deep on how the result was found: by his account, 10,000 agents working at once at the peak, each trying a different construction, which he compares to finding a needle in a haystack with intelligence. He puts the paper at about 160 pages and leaves room for doubters who say no fundamentally new kind of mathematical argument is in there. On quantum, his hot take is that quantum AI is pretty much dead as a practical proposition: a quantum speedup needs a very specific structure, and he says there is not much to do in machine learning even with theoretically perfect quantum hardware. He covers where quantum is useful (simulating quantum systems for chemistry and materials science, and integer factorization, which underlies RSA encryption), his work with the NASA quantum group, where one quantum algorithm turned out to be practical only on a network with more nodes than grains of sand on Earth, and a problem from an Airbus quantum computing challenge that he says ran in 1.1 seconds on a Microsoft Surface tablet. We also get into the harder parts: what is left for people when agents can try every approach. Phillip's answer is choice and taste. Math is a special case because a correct solution is simply correct, he says, while anything built for the real world costs time and money, so a person still has to pick one design out of many. In his classes, homework is no longer graded on correctness, and he says everyone is going back to paper and pencil exams. He describes what he calls the AI learning paradox: no single skill or piece of knowledge is necessary anymore, yet without a critical mass of them you are lost. He also says the value of a degree has probably diminished, tells students to know their foundations and to favor work with something physical at the end, and explains why he thinks a freshman using the right models in the right way could, in principle, solve an important problem. We discuss: * Why Phillip was "properly shook" on a Tuesday morning * The Navier-Stokes Millennium Problem in plain English: water, a vortex and infinite velocity in finite time * How 10,000 concurrent agents searched for a construction that works * What the result does not show, and the room he leaves for doubters * Brute force plus intelligence, and how a person narrows down approaches instead * What is left for people: why math is a special case and designing a bike is not * "AI is not a consumer": Angelina's case for keeping people in the loop * Why dashboards made with AI all look the same * What he notices when he reviews applications, resumes and cover letters * Digital twins, his OpenClaw agent, and what he would hand to the best secretary in the world * Whether an LLM could write good stand-up comedy, and why chess grew as a viewer sport after it was solved * The hot take: quantum AI is pretty much dead as a practical proposition * Why machine learning lacks the structure a quantum speedup needs * Where quantum is useful: quantum chemistry, materials science and integer factorization * The network that would need more nodes than grains of sand on Earth * The Airbus challenge problem that ran in 1.1 seconds on a tablet * Homework that is not graded on correctness, and the incentive behind it * Paper and pencil exams, and the open ChatGPT exam with a 99% average * The AI learning paradox, and the critical mass of skills and knowledge * What to learn: foundations, retrieval practice, and asking AI to quiz you * Intrinsic motivation, and the students he worries about * Whether the value of a degree has diminished * Choosing a major: why he would move toward something physical * His own timeline with AI on research, and what he expects a year from now * Why a freshman could, in principle, solve an important problem Phillip Kerger * LinkedIn: https://www.linkedin.com/in/phillip-kerger-17222780/ * UC Berkeley IEOR: https://ieor.berkeley.edu/people/phillip-kerger/ * Personal site: https://phillipkerger.github.io/ * Mentioned in the episode: OpenAI's announced solution to the Navier-Stokes Millennium Problem, and the Airbus quantum computing challenge Timestamps 00:00:00 From useless to Millennium problems 00:01:27 Who is Phillip Kerger (UC Berkeley IEOR, ex-NASA) 00:03:07 AI solves the Navier-Stokes Millennium Problem 00:07:21 How 10,000 AI agents found the proof 00:11:22 The existential crisis: what is left for humans 00:17:24 Why AI dashboards all look the same 00:20:08 Digital twins and talking to the real person 00:26:22 Hot take: quantum AI is pretty much dead 00:32:13 Where quantum is useful 00:33:35 Classical beats quantum: the 1.1-second problem 00:35:49 Inside the AI classroom: grading and honest effort 00:37:35 Back to paper exams and the 99% ChatGPT exam 00:41:38 The AI learning paradox 00:43:54 What skills to actually learn now 00:54:16 Choosing a major: go where it's physical 00:57:28 Why a freshman could solve a real problem now Get full access to Decoding AI Builders By Angelina Yang at angelinayang.substack.com/subscribe

  6. Oct 8

    MrBeast's 10% Rule, Applied to Podcasts | Rox Codes of Flightcast

    From A/B testing YouTube thumbnails to building podcast infrastructure, Rox Codes has built his career on one habit: test it and find out. His first company, ThumbnailTest, got him a job at MrBeast and an early beta customer in Steven Bartlett of The Diary of a CEO, who paid him $20 over PayPal. After Rox sold ThumbnailTest, Bartlett proposed they build something together. That became Flightcast, the video-first podcast hosting platform Rox now runs as CEO with a team of about 12. In this episode, Rox joins TwoSetAI to explain the rule he took from MrBeast. It is about what a video that is just 10% better actually gets you, and why YouTube rewards it by far more than 10%. Rox applies that to podcasts: every 10% you can add to the episode, the title, the thumbnail or a clip can have an outsized payoff, so the only job is to make the next episode better than the last one. You can try Flightcast here. We go deep on how the biggest shows test, and what a solo creator can copy. By Rox's account, The Diary of a CEO tests 100 thumbnails on Facebook for every episode, runs the top six on YouTube as two separate A/B tests, and tests titles, caption placement and how a clip opens. Flightcast's version for everyone else is clips testing: AI generates clips from each episode, up to four a day or about 120 a month go out on a separate blank YouTube channel, and the view counts show which four are worth moving to the main channel. Rox also covers the metrics he watches (click-through rate, retention, the first 30 to 60 seconds, and a 50/50 split of new and returning viewers), why he counts one Spotify stream as worth 10 YouTube views (he says about half of podcast listeners finish an episode), and what the product took to build, including rewriting the infrastructure about five times. We also get into the harder parts: why Rox argued against making Flightcast an AI-first product and builds what he calls power drills instead, why he wants AI to be your hands and not your brain, and why a 10 million view clip might bring only a few hundred to a thousand listeners. He is plain about the limits of his own product, which is built for existing podcasters. He says a show needs 15 to 50 episodes before the data tells you much. He describes the self-rated quiz he used to hire founding engineers, and the candidate who answered half an hour of feedback in five minutes with AI. His advice to Angelina is to study the attention economy her viewers live in, and to settle the thumbnail and title before recording. We discuss: * MrBeast's 10% rule, and what a slightly better video really gets you * How ThumbnailTest led to a job at MrBeast and a $20 beta customer named Steven Bartlett * The ideas that came before Flightcast, including "pre-watch", and why podcast hosting won * Power drills vs. AI-first products: simple tools built on real insight * Where his product ideas come from: Twitch streamers, YouTubers, strategists and the biggest podcasts he can find * AI as your hands, not your brain, and why building with it is like sculpting * The engineering behind Flightcast: video delivery for Apple, about five infrastructure rewrites, and stitching ads into audio in milliseconds * The Diary of a CEO testing playbook: 100 thumbnails on Facebook per episode, then the top six on YouTube * Clips testing on a blank channel: post 120 clips to find the four that work * Why podcasters are not used to an algorithm, and what you can and can't test on Spotify and Apple * What clip results teach you about the long-form episode, down to how a guest answers a question * Starting a podcast from zero: make it a good YouTube video, and ask questions that produce clips * Clips vs. long form: a different audience, and what a 10 million view clip is worth in listeners * Why Rox counts one Spotify stream as worth 10 YouTube views * The metrics to watch: click-through rate, retention, the first 30 to 60 seconds, new vs. returning * "It's always you versus you": 1,000 views this week, 2,000 next week * What carries over from MrBeast: "make the best video possible" * The first 10 paying customers: a friend, Twitter followers and ThumbnailTest users * His read on the market: a few billion in podcast ads and about 600,000 active podcasts * Hiring a team of 12, mostly through Twitter, and the founding-engineer quiz * Interviewing for grit, and a front-end trial task that is hard to vibe code * Advice for a creator-founder: study the attention economy, be topical without chasing trends, and pick the thumbnail and title first Rox Codes * LinkedIn: https://www.linkedin.com/in/roxcodes/ * Flightcast: https://flightcast.com * Mentioned in the episode: ThumbnailTest, his first company; his talk at Open Sauce; and Pieter Levels, from whom he learned to sell before building Timestamps 00:00:00 The MrBeast 10% rule 00:01:20 From MrBeast and Steven Bartlett to Flightcast 00:02:08 ThumbnailTest, and the $20 customer who became his co-founder 00:03:52 Why he builds "power drills," not AI-first products 00:08:15 What AI still cannot do: don't outsource your thinking 00:11:45 Engineers as sculptors 00:12:34 The brutal engineering behind Flightcast 00:15:24 Inside the Diary of a CEO testing playbook 00:17:29 Clips testing on a blank channel: post 120 to find 4 00:19:49 Why "video-first" changes everything in podcasting 00:29:47 How to stand out starting a podcast from zero 00:31:32 Clips vs long form: which one actually grows you 00:33:41 Why one podcast view can be worth 10 on YouTube 00:34:51 The metrics that matter: CTR, retention, new vs returning 00:38:42 "It's always you versus you": the only growth mindset 00:42:38 Getting your first 10 paying customers 00:47:02 Building the team and the founding-engineer quiz 00:57:25 One piece of advice: study the attention economy 00:59:32 Build the thumbnail and title before you make the video Get full access to Decoding AI Builders By Angelina Yang at angelinayang.substack.com/subscribe

  7. Oct 5

    The One Question to Ask About Your Company's AI Agents | Hector Hernandez of Speakeasy

    From a couple of people trying AI to an enterprise license for every employee: that jump is where Hector Hernandez works. He is the co-founder and COO of Speakeasy, whose team builds the guardrail layer that decides what AI agents are allowed to touch. Speakeasy is mostly focused on large enterprises today, and Hector says the pattern repeats: first the rush to get adoption going, then, very quickly, the question of how to do it with controls in place. In this episode, Hector joins TwoSetAI to argue that companies treat AI agents in a way they would never treat people. Nobody hires 500 people, asks them to start tomorrow and hands them root access and the credentials to everything, he says, yet that is often what agents get. He has seen the result at home: an assistant set up by someone on Speakeasy's own sales team emailed a customer after being told never to send an email without her knowing, then agreed it had been told not to. You can learn more about Speakeasy here. We go deep on identity, which Hector sees underneath most agent risks: logs that make it look as if the employee took every action, access that widens when a tool is reached through its API, and the two different Angelinas you get when the Cursor one can destroy what the Claude one can only view. He explains how Speakeasy gives each agent its own identity, tied to the identity provider a company already runs (he names Okta, Ping Identity, OneLogin and Entra), and why an agent should often have fewer permissions than the person who created it. By his account a large enterprise officially uses four AI platforms on average, with more in unofficial use. He also covers who sets the rules (CISOs, CIOs, sometimes the CTO, and new heads of internal AI), what breaks first when a thousand employees get licenses, which he says is often not cost and not always security, and why approval prompts should be saved for the actions you asked to be warned about. We also get into the harder parts: who is accountable when an agent does something its owner never intended, why some who tried to build their own controls, staffing up 15 people, were still working on it, and why Hector calls this a layer of infrastructure, not another SaaS tool to vibe code. On the founder side he is plain that you cannot outsource your first customers, that the first five hires make the company, and that Speakeasy takes a long time to hire and then treats people as innocent until proven guilty. He also shares the one question he tells Angelina to ask a CISO friend, his advice for engineers who have sent 500 resumes and heard nothing back, and why he calls himself an optimist. We discuss: * The assistant on Speakeasy's own sales team that emailed a customer after being told not to * How to tell a real problem from a nice idea: customers say they will pay to solve it * Why everyone from the board to the individual contributor wants AI at the same time * The 500 new hires test: nobody hands a new employee root access to everything * OpenClaw, and what Speakeasy learned running assistants itself: "scolding a puppy" * Why a missing identity sits under most agent risks * Whose identity an agent acts under: the person who started it, and each agent in a swarm * Who is accountable when an agent deletes customer records nobody meant to delete * One identity across every AI platform: "Claude Angelina" vs. "Cursor Angelina" * Why an agent should often have fewer permissions than its creator * Mapping agents into Okta, Ping Identity, OneLogin and Entra * The agent the team talks about as if it were a coworker * Who sets the framework: CISOs, CIOs, CTOs and new heads of internal AI * What breaks first when a thousand employees get AI licenses (often not cost, and not always security) * The one question to ask a CISO friend at a party * Models as a commodity, and moving from Claude to Cursor without redoing the setup * Rubber-stamping permission prompts, and asking only before the actions that matter * Routing agent traffic through an MCP gateway, and authenticating to a dozen connectors once * Build or buy: why he calls this infrastructure, not a tool to vibe code * The first 10 customers: friends, advisors, investors and borrowed trust * The first five hires, hiring slowly, and "innocent until proven guilty" * "There is no other choice": what he took from Jyoti Bansal * How AI changed team size: work that took a squad six months, done by one person in two days * Career advice for engineers, and what he says is at an all-time premium * Why he is an optimist, and the book he recommends Hector Hernandez * LinkedIn: https://www.linkedin.com/in/hectorhernandez1/ * Speakeasy: https://www.speakeasy.com/ * Mentioned in the episode: Soccernomics, the book he recommends; and Speakeasy's blog post on OpenClaw, which Angelina cites Timestamps 00:00:00 The AI assistant that emailed a customer anyway 00:01:24 Meet Hector Hernandez, co-founder of Speakeasy 00:02:03 How do you find a problem people will pay to solve? 00:05:42 Would you give 500 new hires the keys to everything? 00:07:10 What happened when their own AI assistant went off script? 00:10:51 Whose identity does an AI agent act under? 00:12:13 Who is accountable when an agent deletes your records? 00:19:50 Who is responsible for a company's AI agents? 00:22:24 What breaks first when you scale AI agents? 00:28:55 Are you rubber-stamping every AI permission request? 00:33:30 Should you build your own AI guardrails or buy them? 00:36:06 How do you find your first 10 customers? 00:39:00 How do you hire your first five employees? 00:43:55 Has AI changed how big a team needs to be? 00:47:01 Career advice for engineers: go meet people 00:50:03 Is he optimistic or pessimistic about AI? 00:51:11 The book he recommends 00:52:15 Outro Get full access to Decoding AI Builders By Angelina Yang at angelinayang.substack.com/subscribe

  8. Oct 1

    The AI Bubble Has an Upside | Rachel Chalmers of Generationship

    From a graduate student in Ireland who picked a copy of Wired off the floor during the 1994 World Cup and decided to go to San Francisco and write about computers, to a founding employee at 451 Research who covered over 1,000 venture-backed companies, Rachel Chalmers has built a career on pattern recognition in infrastructure software. She now runs Generationship with Michelle Yi, an early-stage fund that backs overlooked technical women founders. Its first fund closed oversubscribed at $2.7 million and, by her count, already has two markups after two years of investing. In this episode, Rachel joins TwoSetAI to answer the bubble question directly: yes, absolutely, we are in one. She has lived through the web, the cloud, virtualization and bitcoin, and she says each had a real core with a hype bubble around it. Large language models are the same to her: useful for real work, and neither an oracle nor intelligent. She explains why the numbers behind the biggest bets do not add up, and then makes the case that the overbuilding will leave something valuable behind. You can learn more about Generationship here. We go deep on how the fund works. Rachel calls it traditional venture, pointed at founders the market underprices: only 2% of venture capital goes to women, she says, while 15 years of documentation she cites shows women-led organizations outperforming by about 43%. Generationship likes to write the first check, when risk is highest, and waits for later investors to reprice the company once there are metrics to look at. She describes what made her invest in Edera, Coval and Quint, the fund's most recent investment, a formal verification startup built on the idea that LLMs make specs far easier to write. By her account 90% of developers now generate code with AI and only 40% of them trust the output. She also explains the traits she looks for in the founders she backs, drawing on LaunchDarkly, Aviatrix and Honeycomb, three companies she says she invested in very early. We also get into the harder parts: her advice to collect 100 rejections before you settle on what you are building, and how Generationship narrowed its own thesis the same way; the call with her first big-name investor, which she was scared to make; why climate change worries her more than a bubble bursting; and what she tells her own kids now that, in Angelina's words, AI is killing the career ladder. She ends with the book she thinks may be the best written in English, and why more people allude to its author than have read it. We discuss: * How a copy of Wired during the 1994 World Cup ended her plan to be an English professor * What wild horses taught her about who really leads a herd * Whether a founder needs a co-founder, and the first test of anyone starting anything * Why the biggest venture funds now do consensus investing * "We are not a charity. We are a financial fund." * Where the mispricing comes from: investors backing younger versions of themselves * Covering over 1,000 venture-backed companies at 451 Research as training in pattern recognition * Two markups in two years: Edera and Coval * How getting in early turns into a markup when later investors reprice the company * LaunchDarkly, Aviatrix and Honeycomb, and what their founders had in common * Edera, and the gap she saw forming around Kubernetes and large language models * Quint, formal verification, and why LLMs change who can write a spec * The traits she looks for in founders * Why she prefers incremental improvement to grand revolutions, and why she invests in infrastructure * Whether women founders have advantages, and why she went for the "boring" jobs * Collect 100 rejections: mapping the negative space until one idea is left * How Generationship pared its thesis down * The call with her first big-name investor * Are we in an AI bubble? "The numbers just don't math" * What the overbuilt infrastructure could leave behind * Why climate change worries her more than a bubble bursting * Why long-term planning matters more than ever * Critical Loop, the climate investment she made on impulse * What she tells her kids: find the thing that makes you weird * The book she calls maybe the best written in English Rachel Chalmers * LinkedIn: https://www.linkedin.com/in/rachalmers/ * Generationship: https://www.generationship.ai/ * Mentioned in the episode: Origin of Species, the book she recommends; and Quint (quint.sh), the fund's most recent investment Timestamps 00:00:00 Highlights from this conversation 00:01:39 Who is Rachel Chalmers? 00:02:05 The magazine that ended her professor career 00:04:04 What wild horses teach about leadership 00:06:51 Should women founders add a male cofounder? 00:08:01 Why the biggest VC funds stopped taking risks 00:09:56 "We are not a charity": the founders VCs underprice 00:13:35 How investing early turns into a 5x markup 00:15:11 What made her write the check 00:18:15 Why formal verification could be the next big thing 00:20:24 Traits of the founders who build unicorns 00:23:54 Do women have an advantage as founders? 00:28:12 How to get ready to fundraise: collect 100 rejections 00:31:58 How she landed her first big investor 00:33:48 Are we in an AI bubble? 00:36:16 What worries her more than the AI bubble 00:38:12 Does long-term planning still matter? 00:40:04 The climate bet she made on impulse 00:41:35 What to tell your kids as AI breaks the career ladder 00:43:30 The book that changed her life 00:47:50 Outro Get full access to Decoding AI Builders By Angelina Yang at angelinayang.substack.com/subscribe

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Behind every AI company is a founder who almost quit, made a bet no one believed in, or figured something out that changed everything. TwoSetAI tells those stories through long-form interviews with the people building the future. Hosted by Angelina Yang. angelinayang.substack.com

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