Anthropic recently published a 35-page "Founder's Playbook" for building an A.I.-native startup. It doubles as marketing for their products, but the guidance is disciplined, specific and useful:
THE PREMISE
• A.I. has removed the three bottlenecks that historically gated company-building: capital, headcount and technical skill.
• The founder's role shifts from individual contributor to "orchestrator of agents": Your scarce attention goes to deciding what to build and why; A.I. handles much of the execution.
• Each of the 4 stages of the playbook boils down to one principle: Keep your sense-making ahead of your building, especially when building feels effortless.
STAGE 1: IDEA
• The #1 trap is "mistaking building for validating". 42% of startups already failed by building something nobody wanted; expect that rate to climb now that prototypes take hours, not months.
• Sharpen your problem statement into a testable hypothesis: exactly who has the problem, how often, how severely and what they currently do about it.
• Use A.I. as a structured devil's advocate. Ask it to argue *against* your idea and find disconfirming evidence... A.I. tools have given confirmation bias a serious power-up.
• In customer interviews, ask about the specific past ("tell me about the last time..."), not the hypothetical future ("would you use...?").
STAGE 2: MVP
• Beware "agentic technical debt": Without written specs and architectural constraints, each AI coding session re-derives decisions from scratch and your codebase drifts.
• Fix: Document your architecture BEFORE you build, and log key decisions after each session. Five minutes of documentation is cheap insurance.
• Write a scope document stating what the MVP deliberately does NOT do; frictionless building makes scope creep nearly free.
• Define your retention and activation benchmarks before launch so early buzz doesn't masquerade as product-market fit.
STAGE 3: LAUNCH
At Launch, *you* become the bottleneck. Audit everything you handle: What can be automated, what needs a human (not necessarily you) and what merits founder judgment.
STAGE 4: SCALE
At Scale, the question is defensibility: If a well-funded incumbent copied you today, would users stay? Moats come from encoded domain expertise, compounding user data and workflow lock-in.
Thanks to my friend and A.I.-native founder Jeff Tompkins for pointing this guide out to me! Very helpful indeed :)
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Filtering by Tag: #ai
How AI Is Quietly Saving Lives, with Steve Mock
Negative A.I. buzz makes most of the headlines but there are lots of ways that A.I. has made big (even life-changing!) positive impacts on people. In today's episode, Steve Mock, shares many such inspiring stories.
More on Steve:
• Investor at the venture capital firm Blumberg Capital.
• Entrepreneur involved in growing five successful software businesses.
• Creator and developer (without writing any code!) of a website called AISavedMe.org that has a wide range of inspiring examples from healthcare to education to more trivial engineering stories.
In today's episode, we discuss:
• AISavedMe.org and the stories users have posted on the site.
• How he built the website without having a technical background.
• Lots of market insights from his entrepreneur-investor brain.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in June 2026
It is mighty hot in New York rn... but not nearly as spicy as the interviews on my podcast in June! ICYMI, here are the best bits of my on-air convos last month:
1. Two-time mega-bestselling O'Reilly author Chip Huyen on what's left for humans to do when the cost of building software is headed to $0.
2. Andrey Kurenkov, co-host of my favorite podcast ("Last Week in A.I.") and Founding A.I. Lead at Astrocade, on effective vibe-coding.
3. Lightning AI's VP of Infrastructure Frank Basso on what it's actually like inside an A.I. data center.
4. Gilbert Eijkelenboom on why 85% of data scientists can't communicate their work effectively... and the framework for fixing this.
5. In a role-reversal for landmark Episode #1001, the founder and original host of the SuperDataScience Podcast, Kirill Eremenko, interviewed me. In this clip, we discussed whether AGI would require something like consciousness to be realized.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Recursive Self-Improvement
Recursive Self-Improvement (RSI) is suddenly a term that's everywhere. What is RSI? How concerned should be about it? And how soon can we expect it? Here's the skinny:
WHAT IS RSI?
• The idea: An A.I. gets good enough at A.I. research to build a more capable successor, which builds an even better one, in a loop that compounds every turn.
• What we have today is *not* RSI but "A.I.-assisted coding", in which humans still set the goals and judge the results (actual RSI takes the human out of the loop, as shown in the diagram).
• RSI isn't a new concept; it's been around since at least 1965 when mathematician I.J. Good described an "intelligence explosion".
WHAT'S THE CONCERN?
RSI could unleash Artificial Superintelligence (ASI) and "the singularity", a point beyond which there could be radical abundance and radically positive outcomes for humanity... but we have no idea what will happen beyond the singularity and that's also a cause for concern (e.g., human extinction risk, Terminator-style "SkyNet", etc.).
HOW CLOSE ARE WE TO RSI?
• Anthropic reports that, as of May 2026, over 80% of code merged into its production codebase was written by Claude — up from low single digits before early 2025.
• On the hardest open-ended problems, its models' success rate jumped from under 20% in late 2025 to 76% by May.
• Think-tank METR finds the length of tasks A.I. can handle solo is now doubling roughly every four months, up from the "doubling every seven months" trend of the past few years.
• Anthropic co-founder Jack Clark puts a 60% chance on an A.I. creating its own successor, with no human involved, by the end of 2028.
REASONS TO BE SKEPTIC
• Skeptics flag two bottlenecks: compute (chips are scarce) and data (success is hard to verify outside code and math, risking "recursive drift").
• Others note the gap between today's coding agents and real RSI is wider than the hype suggests.
BOTTOM LINE
The productivity gains from coding assistants are real, accelerating rapidly and already in your hand. The closer we get to systems that improve themselves, the more it pays to keep human checkpoints, monitoring and oversight firmly in place.
Listen to the most recent episode of my podcast (Episode #1004) to hear more on all of the above, including what you can do personally to mitigate the risks of RSI if that's a way you might like to make an impact!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Building an AI Data Center End to End, with Lightning AI’s Frank Basso
We've done over 1,000 episodes of this show on every layer of the A.I. stack... except the one that physically runs all of it: the A.I. data center. Today we fix that in a fascinating episode with Lightning AI's Frank Basso.
Frank is VP of Infrastructure at Lightning AI, a New York-based company that has over 35,000 modern GPUs, over $500m in ARR, and that makes it easy to go from A.I. idea to product, "lightning fast" (I hold a fellowship at Lightning so am not an unbiased source on the business, btw). Frank himself is based in Los Angeles and, prior to Lightning, he spent decades directing the development of data centers in California.
In this exceptionally informative episode, Frank explains:
• How Lightning provisions its 35,000+ GPUs through hyperscale co-location.
• Why everything new is liquid-to-chip cooled.
• How GPUs talk to each other over ultra-fast east-west networks.
• What it’s actually like to stand inside a 110-decibel A.I. data hall.
• The most persistent myths about data-center water and electricity use.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How AI Erased My Career Moat, an Episode #1001 Special: Jon Krohn interviewed by Kirill Eremenko
To mark cresting over 1000 episodes, today’s features a role reversal: Kirill Eremenko (who founded the podcast a decade ago) returns to host and welcomes *me* as the guest. Kirill's still got it, enjoy!
Kirill hosted the first 431 episodes of the SuperDataScience Podcast before handing me the reins five years ago. In today's role-reversal episode, we discuss:
• A.I. rapidly usurping our technical skills
• Whether we’re in an A.I. bubble
• The one key reason why I’ve seen A.I. projects fail
• Relationships between A.I. and biological neuroscience.
... so, as usual, lots of A.I. in this episode, but unusually, I’m the one answering the questions instead of asking them!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Ten Years of the Super Data Science Podcast, with Jon, Kirill and Special Guests
Today, we published Episode #1000 of the SuperDataScience Podcast! To celebrate, the show's original host Kirill Eremenko joined me and dozens of regular listeners on air to predict what the next 10 years of A.I. will bring.
In a bit more detail:
• We publish 104 episodes per year so Episode #1000 coincides with the show being about ten years old.
• The show was founded by Kirill Eremenko in 2016, who hosted over 400 episodes before handing me the reins in 2021.
• In a first for the show, Episode #1000 was streamed live online with our audience invited to join on air.
• Most folks interacted via chat functionality but a number of surprise guests came right onto the recording including Natalie Ziajski and Mario Pombo from the podcast team, rockstar A.I. entrepreneur Jepson Taylor, my 96-year-old grandmother and my very own pa, William Krohn.
• Kirill and I looked back on a decade of the podcast and fielded listener questions on topics such as A.I.’s biggest opportunities, the build-versus-buy dilemma, how to break into the field today, and how to stay grounded amid the relentless pace of A.I.
Thank you for support and listenership over all these years — we make this show for you and couldn't do it without you! We're excited to see what the next decade brings :)
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
CBS PrimeTime News: OpenAI IPO & Public Ownership of AI
Last night, I was on CBS PrimeTime News to discuss OpenAI's upcoming IPO, as well as Senator Bernie Sanders' plans for an A.I. sovereign wealth fund... an idea I thought could be "awkward".
What’s Left to Build When Software Is Free, with Chip Huyen
For today's landmark episode (#999!), I asked rockstar Chip Huyen to be my guest and she said "yes"! We discuss her book "A.I. Engineering" (the most popular O'Reilly book in 2025) and how the A.I. job landscape is shifting.
In case you haven't heard of her, more on Chip:
• Her most recent book is "AI Engineering", which was the most popular book in the O'Reilly platform last year.
• Previously wrote “Designing Machine Learning Systems”, which was also an O'Reilly mega-bestseller and was based on the Stanford University course she created and taught on the same topic.
• Is currently building a new stealth startup.
• Previously worked as VP of AI at Voltron Data, co-founder of Claypot AI, ML Engineer at Snorkel AI and Sr Deep Learning Engineer at NVIDIA.
• Holds a Master's in Computer Science from Stanford.
• Her invaluable posts have earned her over 300k followers on LinkedIn.
In this episode, Chip breaks down:
• What separates AI engineering from machine learning engineering.
• The case for a "start simple" workflow.
• The real costs of running LLMs in production.
• Physical AI.
• Robotics.
• World models.
• Why the durable problems worth solving are increasingly human ones.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in May 2026
Well, I certainly learned a lot from the outstanding guests we had on my podcast in May. ICYMI, today's episode features the best parts of my conversations with them:
1. Rubrik's Anneka Gupta and Cal Al-Dhubaib on how, in the Mythos era, the old cybersecurity playbook of prevention and detection is no longer enough, and how A.I. agents themselves are becoming a new source of data exposure inside organizations.
2. marimo's Dr. Trevor Manz on why code notebooks have become the natural working memory for A.I. coding agents. Trevor walks me through the Marimo Pair skill, which lets you drive a notebook from your agent, collaborating with Claude Code or Codex in real time as you load, explore, and visualize your data.
3. Jazmia Henry of collide. walks me through her work as a "full-stack" foundation model builder. We cover all four stages of the process: the often unglamorous slog of data curation, building bespoke tokenizers and embeddings, model training and reinforcement learning, and the inference layer that serves it all to end users.
4. Jacob Miller and Jeremy Mumford of Pattern (and authors of the great, brand-new book "Architected Intelligence") argue that the most expensive AI mistake an organization can make is failing slowly and sticking with prototypes long past their sell-by date because the traditional software mindset says you have to. We, of course, also discuss a solution.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How This Text-to-Video-Game AI Startup Hit 20M Users
Imagine being able to vibe-code full-blown video games... for free! My returning guest, Dr. Andrey Kurenkov, helped engineer Astrocade to do just that... and already 20 million people have played games through their platform.
More on Andrey:
• Founding A.I. Lead at Astrocade, a Bay Area-based startup that has raised $68m in venture capital to create the TikTok of video games, where creators create games for free and you play them for free.
• Co-host (alongside Jeremie Harris) of my favorite podcast, "Last Week in A.I.".
• Holds a PhD from Stanford University, where his research focused on machine vision and robotics.
In this episode, we discuss:
• The fascinating Astrocade journey, of course.
• The surprising pace of humanoid robotics.
• Why he's a skeptic on Artificial Super Intelligence.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Episode #1000: Join Us Live for the First-Ever Interactive SDS Podcast!
Ten years ago, Kirill Eremenko founded The SuperDataScience Podcast. To celebrate the upcoming Episode #1000, we are inviting you to join us both in a format we've never tried before:
It will be the first-ever interactive episode where you can join in online as we record, and ask your questions... or I suppose just make comments! You'll be able to ask share your thoughts in the chat or come right onto the show via video.
Kirill (founder, original host) and I (current host) will both be there, so you can ask us anything, e.g.:
• Did Kirill think the show would last ten years and 1000 episodes?
• How has data science transformed over the past decade?
• Did Jon have hair on his head ten years ago?
Date: Next Thursday, June 4th
Time: 5pm Eastern Time / 2pm Pacific Time
To get a calendar invite that includes the URL to join us live, check out the Luma link below ⬇️
luma.com/7vl7mdos
The "Super Data Science Podcast with Jon Krohn" is available on all major podcasting platforms and a video version is on YouTube. Whether you join us or not for the interactive recording, Episode #1000 will be published on Friday June 12th!
End-to-End Foundation Models for the Energy Industry, with Jazmia Henry
What does it take to build foundation LLMs from scratch today? Deeply impressive Jazmia Henry breaks down the four stages in today's episode, enjoy!
Jazmia:
• Holds degrees from Tulane University and Columbia University... and is partway through a PhD at the University of Oxford.
• Held a technical fellowship at Stanford University.
• Previously worked as a data strategist at Morgan Stanley, head of ML at The Motley Fool and a Lead Applied AI engineer at Microsoft.
• Published a top paper at NeurIPS, the world's most prestigious academic AI conference.
• Currently works as "Member of Technical Staff for AI/ML" at collide., a Texas-based startup that’s building AI infrastructure (including all aspects of specialized foundation models) for the energy industry.
Key topics covered in this episode include:
• What foundation models are.
• Her "full-stack" foundation-model building's four distinct stages.
• How reinforcement learning (RL) models are "bursty" because they idle the GPU during reward calculation and then dump enormous loads on it all at once.
• Reward hacking by RL models.
Thanks to Mark Freeman II for recommending Jazmia as a guest.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI’s Putting Recent Grads Out of Work; Here’s How to Get Hired Anyway!
Computer science/engineering grads had an employment advantage (see chart) that, since ChatGPT's release, has disappeared. Is A.I. to blame? Here's what the data say and what new grads (or anyone!) can do about it:
THE EMPLOYMENT LANDSCAPE
• NY Fed: unemployment for recent computer-science grads (22-27) sits at 7.0%, and computer engineering at 7.8% (roughly on par with fine arts and anthropology grads!)
• Compare that to ~5.8% for recent grads overall and ~4% for the whole US workforce.
• Eighteen-year-olds are voting with their feet: US undergrad CS enrolment fell 11% in 2025; computer programming fell a stunning 26%.
• Demand is shrinking too: Handshake postings are down ~50% from their 2022 peak, and Revelio Labs data suggest entry-level software and data-analysis postings have dropped as much as 67%.
IS A.I. TO BLAME?
• "Yes" camp: A 2025 Stanford University study found employment for 22-25-year-olds in A.I.-exposed jobs dropped 13% since 2022, while older workers held steady. The Dallas Fed replicated it... and the decline comes from juniors never being hired, not layoffs.
• "Not so fast" camp: Google economists found posting declines were just as steep for senior workers and predate ChatGPT. A Fed study of 1M+ firms found "null effects." Their take: high interest rates and a post-pandemic hangover, with A.I. as a convenient scapegoat.
WHAT YOU CAN DO:
1. Stop competing on raw code. The human edge is now system design, architecture and deciding what to build in the first place.
2. Pick a domain. "A.I. engineer" is a common résumé; "A.I. engineer who worked alongside a hospital team for two summer internships" is a short list.
3. Build a public portfolio. Substantive GitHub repos and a Kaggle project beat CVs sent into the void.
4. Get fluent with agentic tooling, e.g., RAG, model evaluation, multi-agent orchestration. PwC found A.I.-skilled workers earn a 56% wage premium (!!!)
5. Lean on your network. Referrals and warm intros are crushing mass (often GenAI-produced) applications in this market.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Web Summit Vancouver 2026
"Collision" has grown and re-branded as "Web Summit Vancouver". I'm looking forward to experiencing the new brand for the first time next week! See you there? Here's where you can catch me:
• Tue May 12 at 11am: Mentor Hours on "scaling your startup"
• Wed May 13 at 1:30pm: Delivering my agentic A.I. talk ("Something Big is Happening") on the "A.I. Summit" stage.
• Wed May 13 at 1:50pm: Emceeing the "A.I. Summit" stage all afternoon.
More on Web Summit Vancouver:
• Taking place May 11–14 at the Vancouver Convention Centre.
• It's the second year in a row the conference, under this new brand, has taken place (the previous "Collision"-branded event was held annually in Toronto and the photo in this post is from a talk I gave there in 2024).
• Connects over 35,000 startup founders, investors and industry leaders to discuss A.I., entrepreneurship and tech trends.
Security for Mythos-Era Agentic Risks, with Rubrik’s Anneka Gupta and Cal Al-Dhubaib
Mythos finds security vulnerabilities at ~100X the rate of publicly available models, and comparable open-weight models are ~6 months away. Scary? Thankfully my guests today, Anneka and Cal, have solutions!
Anneka:
• Chief Product Officer at Rubrik.
• Lecturer in Product Management at Stanford University.
• Climbed the ladder from software engineer to President (!!) during an 11-year tenure at LiveRamp.
• Holds a degree in math and computational sciences from Stanford.
Cal:
• Principal Technologist at Rubrik.
• Formerly founder and CEO of Pandata, which was acquired by Further.
• Highly sought-after keynote speaker.
• Holds a degree in data science from Case Western Reserve University.
This is an exceptional episode with two brilliant, entertaining and highly knowledgeable guests. It can be enjoyed by anyone! In it, they cover:
• How Anthropic's Mythos model can be pointed at a code repository and autonomously surface every vulnerability inside it, and how Anthropic itself estimates Mythos-class capabilities will reach other labs within six to eighteen months, with open-weight versions likely to follow.
• How code-gen models make it easy for attackers by scaling up their capabilities... and by vibe-coders not being aware of vulnerabilities they have!
• How Rubrik's Agent Cloud delivers three pillars of resilience: visibility into every agent in your environment, governance and runtime control through the SAGE small language model, and remediation through Agent Rewind.
• Why the next wave of knowledge work is inherently cross-functional, with A.I. attorneys, security pros, and data scientists all needing shared literacy in A.I. risk.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI Infrastructure, Ray, and Why Nonlinear Careers Win, with Linda Haviv
For folks in A.I., software, data science, things are moving so fast, it's easy to be overwhelmed. Luckily, A.I. engineer Linda Haviv makes it a joy to stay up to date! Today, we discuss career tips as well as open-source A.I. tech like Ray.
More on Linda:
• Until recently, was Staff Developer Advocate at Anyscale, makers of Ray, an open-source framework for managing, executing and optimizing A.I. compute.
• Previously was A.I. Developer Advocate at Amazon Web Services (AWS).
• Before that, was a software developer at Fox Corporation.
• Was a professional singer in New York up until her second (of three!) children was born.
• Holds a degree in philosophy from Baruch College.
In this episode, Linda ebulliently covers:
• How "A.I. infrastructure" refers to the compute stack, tooling and frameworks purpose-built for A.I. and ML workloads.
• Ray is a Python-native open-source distributed computing framework that lets engineers distribute training, data processing and model serving across GPUs without needing to become distributed systems experts.
• How building in public, creating content and contributing to open source are not just career insurance... they're how you find your community, attract unexpected opportunities and learn faster through teaching.
• And much more!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI in the Classroom: How a Top Elementary School Is Doing It Right, with Principal Traci Walker Griffith
Long overdue episode today on how A.I. can support children's education. Hard to imagine a better guest than Traci Walker Griffith, principal of a K-8 school that has used innovations like A.I. to become Boston's #1 school.
In this episode, we discuss:
How Traci transformed The Eliot School from an underperforming school on the closure list into the highest-performing school in Boston.
How kids as young as four at the Elliott work with robots and coding tools like Kibo and Scratch Junior, learning that the quality of their input determines the quality of their output ("garbage in, garbage out").
How, for younger students in kindergarten through fourth grade, teachers use A.I. behind the scenes.
How students in grades five through eight interact with A.I. directly, enabling them to build metacognition and critical-thinking skills.
Her concrete guidance for schools (or parents!) considering incorporating A.I. into pedagogy.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.