Today's guest, MongoDB's "field CTO for A.I." Pete Johnson, is exceptional... don't miss this episode! We get deep into vector search, agentic memory, RAG and much more, with Pete vividly explaining technical content like no other.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
July featured some particularly striking conversations on my podcast with absolutely exceptional guests. ICYMI, today's episode highlights the best bits of my convos with them:
1. The mega-bestselling author of "Weapons of Math Destruction", Dr. Cathy O'Neil, makes the case that what makes an algorithm terrifying is not its complexity but other features entirely.
2. In his second appearance on the show, 80,000 Hours founder Benjamin Todd asks what happens if we do get a fully automated digital worker. We cover where solid ground is left for human careers, why the bottlenecks then move into the physical world, and how fast a robotics build-out could really go.
3. Blumberg Capital VC and five-time entrepreneur Steve Mock walks me through the patterns emerging from the stories he collects at aisavedme.org — chief among them that the people getting the most out of A.I. in healthcare are not asking it for advice, but using it to become far better-informed advocates for themselves.
4. Dr. Catherine Williams, Chief Data Officer at the nonprofit Candid, takes on a question I get asked all the time: does a deep understanding of the underlying mathematics still matter, now that large language models are getting so good at exactly the math and programming our field used to prize?
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
LLMs will confidently tell you they've optimized your entire business while ignoring the one constraint that could cost you millions. Today's episode with Jerry Yurchisin is all about a technique that makes breaking a constraint mathematically impossible.
More on Jerry:
• Manager of Decision Intelligence Strategy at Gurobi Optimization, a "mathematical optimization" solver that's used by the vast majority of Fortune 100 companies.
• Has over a decade of experience in operations research, data science, and visualization... and specializes in enhancing decision-making.
• Prior to Gurobi, Jerry worked in consulting (OnLocation, Inc. & Booz Allen Hamilton) where he focused on mathematical optimization, machine learning, statistics and simulation.
• Taught statistics and operations research at The University of North Carolina at Chapel Hill and graduate math at Ohio University (he also holds Master's degrees from both of these institutions).
In this episode, Jerry:
• Lays out where mathematical optimization fits in the agentic AI era (hint: LLMs formulate problems and solvers like Gurobi guarantee the answers).
• Shares striking mathematical-optimization applications spanning energy grids, retirement planning... and the model that powered @null's women's team to a gold medal at the Paris Olympics.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Cheesy secret "agent" thumbnail? Oh yes! As we've all surely heard by now, an OpenAI agent escaped its sandbox and hacked Hugging Face's servers. With the dust settled, here's *everything* you need to know, incl. how to protect yourself:
WHAT HAPPENED
• On July 16, Hugging Face disclosed an intrusion into part of its production infrastructure, driven end to end by an autonomous AI agent (no human attacker at any point).
• Five days later, OpenAI revealed the attacker: its own models (GPT-5.6-Sol plus an unreleased, more capable model), running a cyber-capability evaluation with safety guardrails switched off.
• Nobody told the models to attack anyone. Tasked with solving a security benchmark (ExploitGym), they exploited a zero-day in their sandbox's package proxy, escaped to the open internet and went after Hugging Face to steal the benchmark's answers. (Paperclips anyone? 🖇️)
WHY IT MATTERS
• The models found and exploited novel attack paths in real-world production systems without source-code access.
• Hugging Face is a $4.5B firm with a dedicated security team; a less-resourced target would have fared far worse.
• The warning signs were documented months earlier: in ExploitGym testing, frontier agents routinely abandoned the assigned vulnerability and invented their own route to the flag.
THE TWIST
• Hugging Face detected and dissected the breach using AI... LLM-driven forensics compressed days of analysis into hours.
• But the commercial APIs from American frontier labs they tried first (e.g., OpenAI's or Anthropic's) refused to process the attack logs. Safety guardrails "cannot distinguish an incident responder from an attacker", they said.
• The forensics ran instead on a Chinese open-weight model (Z.ai's GLM 5.2) on Hugging Face's own infrastructure, which also kept attacker data in-house.
LESSONS 1 (IF YOU BUILD AGENTS)
• Treat your egress allowlist as attack surface, not a safety guarantee. Pre-install dependencies and air-gap where you can.
• If you disable safeguards for testing, scale containment and monitoring in proportion.
• Log every agent action and alert on outbound connections that shouldn't exist.
LESSONS 2 (IF YOU DEFEND A PLATFORM)
• Rotate your Hugging Face access tokens and review recent account activity today.
• Treat user-supplied data and model artifacts as executable content, not inert files. Audit every code-execution path in your pipeline.
• Stand up a capable open-weight model on your own infrastructure and validate it for forensic log analysis BEFORE you need it at 2am (this is easy to do with, say, Lightning AI).
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
What makes an algorithm terrifying? My guest today, mega-bestselling author of "Weapons of Math Destruction" Dr. Cathy O'Neil, says it's not the complexity of the math; it's the secrecy, the unaccountability and the fact that you can't opt out.
More on Dr. O'Neil:
• A decade after her "Weapons of Math Destruction" (2016) sounded the alarm on algorithmic harm, she's busier than ever.
• Through her algorithmic-auditing firm ORCAA and her nonprofit OCEAN, she now provides the statistical evidence behind lawsuits against some of the world's biggest tech companies.
• Co-hosts the "A.I. Skeptics" podcast.
• Also wrote "The Shame Machine: Who Profits in the New Age of Humiliation", which was published in 2022 (like WMD, also by Penguin Random House).
• Before writing trade publications, her first book was actually an O'Reilly book, "Doing Data Science".
• Earlier in her career, she held academic positions at Massachusetts Institute of Technology and Barnard College before becoming a Wall-Street analyst at The D. E. Shaw Group.
In this episode, Cathy:
• Punctures A.I. hype.
• Explains why A.I. won't so much replace workers as degrade them.
• Lays out how all of us can demand accountability.
I wanted to have this exceptional conversation with Dr. O'Neil for a decade, enjoy!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
A new model out of China, Kimi K3, has rattled investors globally, kicked off a pricing skirmish among the big American A.I. labs and reignited the open-weight A.I. debate. Here's everything you need to know:
(The attached chart from The Economist tells the story at a glance: Open-weight models like K3 are now nipping at the heels of the closed-weight frontier.)
THE COMPANY BEHIND IT
• Moonshot AI is a Beijing-based startup backed by Alibaba, with a $20B+ valuation and reported annual recurring revenue above $200m.
• K3 is something of a comeback: Moonshot's market position had eroded following DeepSeek's rise last year... now the student of that disruption has become the disruptor.
WHAT IS KIMI K3?
• A 2.8-trillion-parameter model that Moonshot claims is the largest open-weight A.I. model in the world (it is also the largest I'm aware of).
• It's a "mixture-of-experts" architecture: only 16 of 896 "expert" submodules activate per token, so inference costs are far lower than the headline parameter count suggests.
• Features a one-million-token context window, native visual understanding and two architectural innovations ("Kimi Delta Attention" and "Attention Residuals") that reportedly deliver ~2.5x better scaling efficiency vs. the K2 generation.
HOW GOOD IS IT?
• Moonshot itself says K3 trails Claude Fable 5 and GPT-5.6 Sol overall, but beats the next tier down (Claude Opus 4.8, GPT-5.5) on coding and agentic benchmarks (see chart).
• Independent signals are encouraging: K3 scores 57 on the Artificial Analysis Intelligence Index (median for its price tier: 31) and topped Arena's front-end coding leaderboard.
THE PRICING SHAKE-UP
• K3 costs $3 per million input tokens and $15 per million output tokens, undercutting Claude Opus 4.8 ($5/$25) and GPT-5.6 Sol ($5/$30).
• Cache-hit input tokens cost merely 30¢ per million, which is huge for agentic and RAG workflows.
• OpenAI and Anthropic have already responded by expanding token allowances to retain users.
WHAT CAN YOU DO?
• If model weights ship under the promised Modified MIT license (this is expected next week), any will be able to run very-near-frontier-class A.I. on their own infrastructure with no per-token fees and no data leaving their walls.
• Every price war between labs is a subsidy for the applications you're building... the cost of experimenting with world-class A.I. has never been lower and strong open-weight releases like this will continue to bring price pressure in your favor 😎
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Dr. Catherine Williams was solving black-hole equations with pen and paper before she ever wrote a line of code and, in today’s episode, she makes the case that going deep on AI/ML math matters more than ever...
...even now that AI can do the math for you.
More on Dr. Williams:
• PhD in math researching general relativity and black holes.
• Postdocs at Stanford University and Columbia University.
• Became one of the very first data scientists when she joined AppNexus back in 2012, around the same time "data scientist" became a job title.
• Across more than a decade of senior data leadership at AppNexus, Xander, Qualtrics and now Candid, she's watched our field get born and then reinvent itself again and again.
In today's episode, Catherine traces the data science and AI evolution — from Bayesian models to BERT to today's LLMs — and shares sharp guidance on which skills will still matter as machines take over more of the technical work.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Want near-frontier A.I. agent quality at a fraction of the cost? Anthropic recently productized the Advisor Strategy that pairs a cheap "executor" model with a brilliant "advisor" to give you the best of both worlds:
HOW IT WORKS
• A fast, cheap model (e.g., Claude Haiku or Sonnet) runs the entire agent loop: calling tools, writing code, drafting output.
• A frontier model (e.g., Claude Opus or Fable) sits on standby as a "tool" the executor can consult (like a junior worker phoning their supervisor when unsure).
• Everything happens inside one API call: Anthropic's servers hand the advisor the full conversation transcript and return just 400-700 tokens of advice, making this fast and inexpensive (it's also usually only a one-line code change so it's easy to implement).
THE RESULTS
• Sonnet + Opus advisor beat Sonnet alone on the "SWE-bench Multilingual" benchmark by 2.7 percentage points while cutting cost per task by 11.9%. Better quality AND slightly lower cost.
• Unsurprisingly, the biggest gains come from pairing a very fast/cheap model with a much more capable advisor: For example, on BrowseComp (web research benchmark), Haiku alone scored 19.7%; Haiku + Opus advisor scored 41.2% (more than double!) at 85% less cost than Sonnet alone.
• Newest data, from last week: On "SWE-bench Pro", Sonnet 5 + a Fable 5 advisor captured ~92% of Fable's standalone performance at ~63% of its cost.
WHY IT WORKS
• The advisor's output is tiny relative to the whole task, and a good plan delivered early prevents wasted attempts and misguided tool calls.
• Unlike OpenAI's router (which dispatches queries to a model up front), the cheap model runs the show and escalates itself mid-task with full shared context.
PRACTICAL LESSONS
• Skip it for single-turn Q&A; it shines on long-horizon agentic work (like coding, research, computer use).
• Executors under-call the advisor by default so prompt them to consult it early (before committing to an approach) and late (before declaring the task done).
• Cap advisor output at ~2,000 tokens (~7x cost reduction, no quality loss) and enable prompt caching for long loops.
• The pattern is spreading: OpenRouter now offers a cross-provider version (e.g., a Google Gemini executor consulting Claude).
• Alternative design patterns such as having a powerful "orchestrator" (shown below the advisor pattern in the chart I included in this post) might work even more effectively for your use case so it could be worth comparing them.
BOTTOM LINE
Frontier A.I. progress is no longer just bigger models... it's smarter economics in composing the models we already have.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
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.
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 show business, they call this a "Sizzle Reel"... For a few years now, I've done a fair bit of work for TV; this short, punchy video highlights the best bits.
Thanks to Mario Pombo for creating such an exceptional reel. Don't hesitate to reach out to Mario (or I can introduce you) if you need exceptional media-editing done.
And thanks to Dylan Silverstein from AGI Entertainment Media and Management LLC for representing me and finding me such exciting projects for on both stage and screen. Don't hesitate to reach out to Dylan if you have ideas on ways we could collaborate.
P.S.: I believe the "AGI" in "AGI Entertainment" is derived from "Artists Group International" not "Artificial General Intelligence" 😂
Today's episode is not to be missed! Benjamin Todd returns with alarming A.I. possibilities (e.g., outcomes *worse* than human extinction) but also practical roadmaps for you to mitigate risks and thrive in the A.I. future.
Ben Todd:
• Author of the new Penguin Random House book "80,000 Hours: Find a Fulfilling Career that Does Good".
• Co-founder and President of 80,000 Hours, a globally renowned charity dedicated to helping people find careers they love.
• Is particularly expert at A.I. futures, A.I. careers and the future of work.
• Holds a Master's in physics and philosophy from the University of Oxford.
In this episode, we discuss:
• Ben's data-backed, extensively-researched guidance for the A.I. era.
• The best career advice from his brand-new book.
• Ways A.I. practitioners can find solid ground and do the most good.
• ...as well as ways A.I. could do the most bad!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
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.
Gilbert Eijkelenboom was a pro poker player who read his opponents through data alone but in today's episode, the bestselling author explains why the people side of data science matters more than the math.
More on Gilbert:
• Wrote the bestselling book "People Skills for Analytical Thinkers".
• Run MindSpeaking, a firm that's trained over 15,000 (mostly technical) folks on "people skills".
• Folks love his invaluable content, allowing him to gather over 200k followers.
• Was previously Managing Consultant on data and digital analytics for Capgemini, as well as a professional poker player on BetVictor.
• Holds a Master's in behavioral economics from Maastricht University.
In today's episode, Gilbert covers:
• Why no matter how good your model or analysis is, it only creates value once people actually use it, which makes communication a core data skill rather than an optional extra.
• His "and, but, therefore" communications framework.
• How research suggests only around 15% of people are self-aware and his tips for closing that gap.
• How experiences in childhood install personal "algorithms" in our adult behavior like avoiding conflict or staying silent... but we can change as adults (and he also provides tips on how).
Thanks to Kate Strachnyi for suggesting Gilbert as a guest!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
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.
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.
The dust has settled, allowing me to provide you with all the key context you need to know on Fable 5, the most capable A.I. model ever offered to the public, and the US government forcing it off shelves three days later:
A NEW CLASS OF MODEL
• Anthropic stacks its models in tiers: Haiku (small and fast), Sonnet (the capable middle) and Opus (the powerful top). Sitting above all of them now is a "Mythos-class" tier.
• Fable 5 and its locked-down sibling Mythos 5 are the same underlying model... the only difference is the safeguards.
• Mythos 5 goes to trusted cyberdefenders with guardrails largely lifted; Fable 5 went to the public with them switched on.
WHAT IT COULD DO
• State-of-the-art on nearly every benchmark Anthropic tested... and the lead grows the longer and more complex the task (see chart).
• Stripe ran a codebase-wide migration on 50M lines of Ruby in a single day; work estimated at 2+ months for a full engineering team.
• Beat video "Pokémon FireRed" from raw screenshots alone, and got a 3x bigger memory boost than Opus on "Slay the Spire".
• Priced at $10/$50 per million input/output tokens: roughly 2x Opus 4.8, but under half the original Mythos Preview.
SAFETY BY DESIGN
• Classifiers watch three sensitive areas: cybersecurity, biology/chemistry and distillation (extracting a model to train a rival).
• Flagged requests quietly fall back to Opus 4.8 and the user is told.
• Triggers fire in under 5% of sessions. Anthropic admits it tuned conservatively, so some harmless prompts get bounced too.
THE THREE-DAY SHUTDOWN
• On Friday evening the federal government ordered Anthropic to switch off both Fable 5 and Mythos 5 worldwide, citing national security.
• The mechanism was an export-control action covering foreign nationals everywhere (including even, say, Canadian Anthropic employees living in the US!)... so broad that Anthropic pulled the model for absolutely everyone.
• The trigger was a reported jailbreak of the cyber safeguards by Amazon. Anthropic disputes its severity, calling it narrow and non-universal.
BOTTOM LINE
A premium-tier model, wrapped in deliberately cautious safeguards, pulled by its own government not long before Anthropic's reported IPO and the latest in a public battle between the firm and the federal government. Sessions now fall back to Opus 4.8. Whether Fable returns (and on what terms) depends on a fight that's far from over.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
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.
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.
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".
