Why?? Well, for a long time, I didn't want to "go here" but for a mix of practical, personal, and philosophical reasons... it's time for a change.

The tl;dr is that a past tense verb (crafted) is a poor way to describe a show that (cue exciting music!) is really about the future. More specifically, how we, builders, can steer the future in the right direction.

So, on Tuesday January 20th, you’ll see a new name, website, art, trailer, and email platform. I’m really excited to share this all with you very soon!

I’m not going to reveal that name today, but I do want to share a bit of why I’m changing the name of a show that’s been honored three years straight by the Webby Awards — and what is not changing.

If you prefer: watch the video below or listen to this update as short (2m) podcast on Spotify | Apple | etc…

There are three reasons for the name change:

1. Practical: “Crafted” is really hard to find in search. I’ve literally stood next to people who are looking to subscribe and they can’t find the show. I swear this wasn’t the case when we launched three years ago, but today there are several shows that are either called crafted or something close to it.

2. Personal: The second reason is more personal: the show is mine now — and that wasn’t always the case. You may recall the show launched when I was with a high craft software consultancy doing product and client work. (Launching a podcast was not on my bingo card!) When I left and got full ownership of the show I didn’t want to change too many things all at once. Also, I like the name crafted, but, and this leads to the *real* reason I’m changing, it no longer fits the show.

3. Philosophical / the real reason: CRAFTED. is a past tense verb. and it perfectly described the original incarnation of this show, where founders, makers, and innovators would look *back* on things they’d built and we’d do a sort of case study that would help other builders learn from their mistakes and understand how that great product or company they built got so great... So here’s the thing... I’m not really doing that sort of case study thing anymore. And I haven’t for a while. Creating explicitly educational content is not favorite thing. I’m not exactly a “here is a framework” kind of guy.

So I’ve been following my interests...

For a while now, this show has been much less concerned with teaching case studies and much interested in what comes *next.*

• What are the implications of new tech?

• How will AI change how we live, work, play, teach our kids...?

• Should we get ready to live with humanoid robots?

• How are stablecoins changing the world of money?

• And what about quantum computers?

And what do builders need to know about these things so that we can build a future we actually want???

That’s the part is not changing: the show is still for builders. And you can take that literally, as in people who make software, or a bit more broadly, as in people who putting in the work to build a better future.

Sorry if that’s a bit cheesy, but it’s true. Because while I’m optimistic that we will build an amazing future, there is... uh... a lot going on right now in tech and in the world. And I believe that, together, we have power to steer the future in the right direction.

This show will still feature the world’s top technologists. And we’re going to get into all of these future-forward things. Of course, we’ll talk about things they’ve done in the past, because if we don’t learn from history... well, you know how that expression goes.

So, get ready to see some new art and a new name -- I’ll give you a hint, it’ll have the word future in it -- on Tuesday.

And I would love your help spreading the word. When the new trailer and website drop, please share them with all your builder friends.

So stay tuned... and please get ready to share with friends and help this endeavor — and our future — grow!

Onward!

Dan

On Tuesday, you should get an email from me with the new name and it should come from a new email provider (Beehiiv, not Substack), so let’s hope that all goes smoothly. If anything weird comes up or you don’t hear from me at all, please drop me a line: dan@modernproductminds.com

Enjoy on: Apple | Spotify | YouTube | Everywhere

So will AI dethrone or entrench the big players in travel? Rafat Ali says for the first time in a while smaller players have a chance to disrupt the travel oligopoly. But it could go either way...

On the latest CRAFTED., the OG blogger and founder of Skift (which bills itself as the travel industry’s homepage) and I discuss:

• Why every AI launch seems to promise the same thing: “Tell me where you want to go, and I’ll plan everything.”

• Whether these agents are really all that good — and where they’re likely to reside (e.g. on OpenAI or Expedia or some startup)

• Why, in an age of data, it baffles him that airlines don’t know what you’ll do when you land — and how this is changing

• The enshittification of travel (esp. in America, esp. in Las Vegas) with maximum extraction and “luxury holding up”

• Why no consumer AI travel apps have really impressed him (whereas there’s lots of interesting stuff underway in B2B)

• Plus Rafat’s greatest dad hacks, including why he flies long-haul business class with his three young kids, and what how he frees himself from caring about the glances he gets from fellow travelers (”don’t give a sh*t; you’ll never see them again.”)

Enjoy this one. I did!

Subscribe now

Enjoy on: Apple | Spotify | YouTube | Everywhere

This week I’m turning the mic over to podcast friends Mike Masnick and Ben Whitelaw, hosts of Ctrl-Alt-Speech, a show about what happens when we talk on the internet, the messy world of content moderation, trust & safety, and the laws trying (and often failing) to keep up.

In their first episode of the new year, they build a 2026 bingo card of things that might happen across AI, regulation, and online speech. Not predictions exactly — more a way to follow along and yell “BINGO” as we stumble into another year of deepfakes, age verification fights, and calls to repeal Section 230.

Enjoy on: Apple | Spotify | Everywhere

Meanwhile, we’re just ten days away from me renaming CRAFTED. and announcing a few other exciting things, so stay tuned…

Enjoy on: Apple | Spotify | YouTube | Everywhere

Happy New Year! This is the time of year when people make big changes. So, I’m bringing back my conversation with the co-author of Tomorrowmind. It’s a fascinating book and especially relevant at this time of the year.

Dr. Gabriella Rosen Kellerman writes that that career trajectories used to be like steamships (full steam ahead), and then they became more like sailboats (lots of tacking), but now we’re swirling in whitewater. So how can we stay afloat? How can we flourish?

“When you’re kayaking in the whitewater. It’s hard to get a sense of what could be around the bend, but if you know if what’s coming up is a sudden cascade or versus another, you know, set of gentle bumps, or maybe it’s a calmer space in the river, it can give you a great advantage.”

On this episode of CRAFTED., we focus on PRISM, the five key skill groups that Gabriella says can help you be more successful: Prospection, Resilience, Innovation and creativity, Social support by way of rapid rapport, and Mattering and meaning.

Gabriella was until recently the Chief Product Officer at BetterUp, a platform that helps organizations and people level up through a mixture of human and AI coaching. She originally appeared on the show in a two-part episode. Part one is includes more on the tomorrowmind skills and her career path; in part two, she describes how BetterUp builds products and innovated under her leadership.

And stay tuned as we employ our own tomorrowminds here at CRAFTED... there are some big changes to the show, including a new name, coming this month!

Featured voices:

• Dr. Gabriella Rosen Kellerman, Partner at BCG, former CPO of BetterUp, and co-author, with Martin Seligman, of Tomorrowmind

• Me (Dan Blumberg) — I’m the host of CRAFTED. and the founder of Modern Product Minds. HMU if you want to build something great! I love building from zero to one.

And if you please…

• Share with a friend! Word of mouth is by far the most powerful way for podcasts to grow

• Share your feedback! I’m experimenting with new episode formats and would love your honest feedback on this and other episodes. Email me: dan@modernproductminds.com or DM me on LinkedIn

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Drop me a line and let’s talk.

• Get psyched!… There are some big updates to this show coming soon!

Enjoy on: Apple | Spotify | YouTube | Everywhere

Enjoy on: Apple | Spotify | Everywhere

The big rebrand and relaunch is less than one month away! So get psyched!

In the meantime… something completely different…

I’ve been enjoying presenting episodes from some of podcast friends. So, here’s an episode of Famous & Gravy. On each episode, host Michael Osborne and guests look at the life of a famous dead celebrity and ask themselves if it’s a life they would’ve wanted. The show gets into all sorts of things you will not in that person’s official obituary or biography. I’m a fan. Here’s how they describe today’s episode:

This person died 2011, age of 56. He dropped out of Reed College in 1972 and once said that taking LSD was among the most important things he ever did. In the early years of his career, his obsession with detail drove colleagues crazy, but later he inspired extraordinary loyalty. In the 1990s he bought a small computer graphics spinoff from George Lucas and built it into Pixar. He told the world he would step down as Apple’s CEO if he could no longer meet expectations — and then he did. Today’s dead celebrity is Steve Jobs.

Subscribe to Famous & Gravy in all your favorite podcast apps and at famousandgravy.com

Enjoy on: Apple | Spotify | Everywhere

And if you please…

• Share with a friend! Word of mouth is by far the most powerful way for podcasts to grow

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Drop me a line and let’s talk.

• Get psyched!… There are some big updates to this show coming in January

Enjoy on: Apple | Spotify | Everywhere

Here’s something different…

This week I’m the guest and my friends at Whiskey Web and Whatnot are the hosts. And they’re great hosts, because they send their guests a bottle of whiskey before talking web and whatnot.

As we head into the holidays I hope you’ll raise a glass with us and enjoy this very laid back episode. Chuck and Robbie hosted me a year ago and I love that they got me on tape when they did, because it was just as I was starting to consider making some big changes to my show... Changes that I will announce in late January... so get excited for that!

Here’s how they described the episode:

Robbie and Chuck talk with Dan Blumberg about his journey from radio producer to product manager and podcaster. They explore the art of building great software, podcasting essentials, and the changing landscape of podcast platforms. Plus, Dan shares his kayaking adventures and insights on balancing authenticity and growth.

Yes, this is podcasters talking podcasting (and web and whatnot!), but also you’ll enjoy the conversation. Put it on while washing dishes or something. We’re good company :)

And if you please…

• Share with a friend! Word of mouth is by far the most powerful way for podcasts to grow

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Drop me a line and let’s talk.

• Get psyched!… There are some big updates to this show coming in January

For more on Whiskey Web and Whatnot...

• Check out https://whiskey.fm

• Connect with Robbie Wagner: https://x.com/RobbieTheWagner

• Connect with Chuck Carpenter: https://x.com/CharlesWthe3rd

In this episode:
- (00:00) - Intro
- (03:26) - Whiskey review and rating: Woodinville Straight Bourbon
- (09:23) - Apple Podcasts vs Spotify
- (11:20) - Spotify video vs YouTube
- (13:02) - Podcasting audio vs video
- (15:24) - Advice on starting a podcast
- (19:24) - Equipment requirements for guests on podcasts
- (22:15) - Having a pre-interview interview
- (26:06) - Social media and podcasting challenges
- (27:37) - How to grow your audience
- (33:18) - How to make money as a podcaster
- (37:28) - Being yourself vs having a persona
- (38:42) - Monetizing your podcast
- (42:11) - What’s missing from RSS
- (43:38) - Dan’s non-tech career ideas
- (45:40) - Podcast recommendations
- (49:12) - Dan’s plugs

Links
- Woodinville Straight Bourbon: https://woodinvillewhiskeyco.com/
- Crafted: https://crafted.fm
- WNYC: https://www.wnyc.org/
- NYT: https://www.nytimes.com/
- Apple Podcasts: https://podcasts.apple.com/
- Spotify: https://www.spotify.com/
- Pocket Casts: https://pocketcasts.com/
- IAB: https://www.iab.com/
- National Geographic: https://www.nationalgeographic.com/
- Shure SM7B: https://www.shure.com/en-US/products/microphones/sm7b
- Focusrite: https://focusrite.com/
- Shure MV7: https://www.shure.com/en-US/products/microphones/mv7
- Elgato: https://www.elgato.com/
- AirPods: https://www.apple.com/airpods/
- Audio Technica: https://www.audio-technica.com/en-us/
- Morning Edition: https://www.wnyc.org/shows/me
- Chicago Public Radio: https://www.wbez.org/
- Riverside: https://riverside.fm/
- TikTok: https://www.tiktok.com/
- Mr. Beast: https://youtube.com/@mrbeast
- Docker: https://www.docker.com/
- Artium: https://www.thisisartium.com/
- Jay Clouse: https://creatorscience.com/
- Hark: https://harkaudio.com/
- Syntax: https://syntax.fm/
- Hard Fork: https://www.nytimes.com/column/hard-fork
- Big Technology with Alex Kantrowitz: https://www.bigtechnology.com/
- Decoder with Nilay Patel: https://www.theverge.com/decoder
- How I Built This: https://www.npr.org/series/490248027/how-i-built-this
- Acquired: https://www.acquired.fm/
- Smartless: https://smartless.com/
- Wondery: https://wondery.com/
- Sacha Baron Cohen: https://en.wikipedia.org/wiki/Sacha_Baron_Cohen
- Tim Burton: https://en.wikipedia.org/wiki/Tim_Burton
- Beetlejuice: https://www.warnerbros.com/movies/beetlejuice
- Darknet Diaries: https://darknetdiaries.com/

Enjoy on: Apple | Spotify | Everywhere

Subscribe now

Start 5m Survey

Enjoy on: Apple | Spotify | Everywhere

This week, I’m turning the mic over to my friends at The Startup Podcast and their guest, Carta’s head of insights, for a great conversation on what you need to know about today’s fundraising environment and how AI is affecting valuations, equity, and how companies grow. Here’s how they describe this episode...

Looking to fund your startup? If you’re new to the process, fundraising can be difficult to navigate. Not only are there a myriad of ways to go about it, but it can be hard to tell whether the tips, tricks, and advice floating around are based on any evidence at all.

So, what is the truth?

And what are the actual, data-backed insights that can help you choose the best method of fundraising for your own business?

Enter: Peter Walker.

As Head of Insights at Carta, he has access to, and industry knowledge about, the vast sets of funding data that will help you cut through the noise. Today, he joins Chris and Yaniv in discussing the real data behind startup funding trends in 2025 and the key takeaways you can apply to your own startups.

In this episode, you will:

• Discover why Silicon Valley valuations often hurt founders more than they help

• Understand how AI startups now account for nearly half of all venture funding, and what that means for non-AI founders

• Learn how lean AI-driven teams are reshaping early-stage hiring, with Series A companies shrinking from 25 employees to just 15

• See why most founders misunderstand SAFE notes

• Explore why 70% of startup employees never exercise their equity

• Uncover the reasons behind why nearly 40% of startups lose a co-founder within seven years

• Get clarity on founder vesting, equity splits, and why a six-year vesting schedule may protect your company better than four

• Reframe your goals as a founder: why chasing “life-changing money” isn’t the right reason to start a company

Featured voices:

• Peter Walker - Head of Insights at Carta

• Yaniv Bernstein - Co-host of The Startup Podcast

• Chris Saad - Co-host of The Startup Podcast

• Me (Dan Blumberg) — I’m the host of CRAFTED. and the founder of Modern Product Minds. HMU if you want to build something great. I love building from zero to one.

And if you please…

• TAKE THE SURVEY: It’ll just take five minutes and I’ll give $100 to the charity of choice for one lucky respondent

• Share with a friend! Word of mouth is by far the most powerful way for podcasts to grow

• Subscribe to the CRAFTED. newsletter at crafted.fm

• Share your feedback! I’m experimenting with new episode formats and would love your honest feedback on this and other episodes. Email me: dan@modernproductminds.com or DM me on LinkedIn

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Drop me a line and let’s talk.

• Get psyched!… There are some big updates to this show coming soon!

Subscribe now

Enjoy on: Apple | Spotify | Everywhere

Start 5m Survey

Enjoy on: Apple | Spotify | YouTube | Everywhere

“If you take any great startup and look backwards, you’ll see that 90 percent of their growth came from like 10 percent of the stuff that they tried. So how do you find that 10 percent as quickly as possible?”

Matt Lerner has advised hundreds of startups on how to grow. Now, the CEO of SYSTM has written a book called Growth Levers and How to Find Them where he shares his approach. This episode of CRAFTED. is full of actionable advice on how you can grow your products and companies. Matt will tell us about the mindset shift founders need to make from thinking about their products to thinking about their customers needs. We’ll talk about jobs-to-be-done (JTBD) style interviewing and why it’s such a powerful approach, but also why at first Matt was put off by some of the overly academic language that often goes with jobs. And we’ll talk about how you can get new customers to that aha moment as quickly as possible, so they stick with your product. Plus, lots of real talk about founders and the mistakes they make.

Featured voices:

• Matt Lerner (Founder and CEO of SYSTM; the book is Growth Levers and How to Find Them)

• Me (Dan Blumberg) — I’m the host of CRAFTED. and the founder of Modern Product Minds. HMU if you want to build something great. I love building from zero to one.

Subscribe now

And if you please…

• TAKE THE SURVEY: It’ll just take five minutes and these surveys are actually really important for podcasters.

• Share with a friend! Word of mouth is by far the most powerful way for podcasts to grow

• Share your feedback! I’m experimenting with new episode formats and would love your honest feedback on this and other episodes. Email me: dan@modernproductminds.com or DM me on LinkedIn

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Drop me a line and let’s talk.

• Get psyched!… There are some big updates to this show coming soon

Enjoy on: Apple | Spotify | YouTube | Everywhere

Hey folks, it’s Thanksgiving weekend here in the US and it’s the time of year when we think about what we’re grateful for, so today I’m re-sharing some words from perhaps the most grateful person I’ve ever had on the show.

But first… Big favor, pretty please… Take five minutes to answer this survey. It will help me improve and grow this newsletter / podcast. Surveys are shockingly important to podcasters (and their sponsors), so thanks in advance:

Start 5m Survey

Kelsey Hightower is a legendary developer. And he has an incredible story. He went from sleeping in his car to becoming a pioneer in the Kubernetes world, a distinguished engineer at Google, and then... he retired. At the age of 42. Because he wanted to have more impact on the world than he thought he could have by advancing up the career ladder.

So here are 15 minutes of my original interview with him, because some of the things he said — not about tech, but about humanity, gratitude, and prioritizing what matters — have really stuck with me.

Here’s the full interview, originally released in July 2024. We cover a lot, including how he became so good at live demos, why emotion is the key to great software — and storytelling — and how it’s those “boring innovations” and mindset shifts you need to make as a technologist that will take you from “hello, world” to “hello, revenue.”

---
Featured voices:

• Kelsey Hightower: “Retired, not tired” former distinguished engineer at Google and Kubernetes Pioneer

• Me (Dan Blumberg) — I’m the host of CRAFTED. and the founder of Modern Product Minds. HMU if you want to build something great. I love building from zero to one.

Subscribe now

And if you please…

• TAKE THE SURVEY: It’ll just take five minutes. Thank you!

• Share with a friend! Word of mouth is by far the most powerful way for podcasts to grow

• Share your feedback! I’m experimenting with new episode formats and would love your honest feedback on this and other episodes. Email me: dan@modernproductminds.com or DM me on LinkedIn

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Drop me a line and let’s talk.

• Get psyched!… There are some big updates to this show coming soon

Enjoy on: Apple | Spotify | YouTube | Everywhere

Enjoy on: Apple | Spotify | YouTube | Everywhere

Hey friends,

Here’s another impromptu, slightly madcap interview recorded live from the “Croissant Studio” at Web Summit in Lisbon. This time I’m with new friend, roboticist, investor, and founder Chris Coomes.

Chris shares how and why he built X1 Pipeline, an AI platform that evaluates startups the way he would — only much, much faster. It’s something he wishes he had when looking for early stage robotics startups while at Google and Amazon.

We also talk about the strange humanoid robots wandering the convention hall at Web Summit, why “agents” is a vastly overused word and why (his take) most of the “agentic” startups he saw at the conference won’t be around next year. Plus, why plugging things in is hard for robots — and why (my take) that’s a good thing, because it means we humans will still have jobs (as plumbers and electricians, but hey!) in the future.

Enjoy this fun episode, recorded live from the “Croissant Studio” on the floor at Web Summit in Lisbon.

And if you please…

• Share with a friend! Word of mouth is by far the most powerful way for podcasts to grow

• Share your feedback! I’m experimenting with new episode formats and would love your honest feedback on this and other episodes. Email me: dan@modernproductminds.com or DM me on LinkedIn

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Drop me a line and let’s talk.

• Get psyched!… There are some big updates to this show coming soon!

• Hit reply! I read every message.

  ---

  Share CRAFTED.

  ---

  Enjoy on: Apple | Spotify | YouTube | Everywhere

Enjoy on: Apple | Spotify | YouTube | Everywhere

Hey friends,

Here’s a special Sunday edition of CRAFTED., recorded live in Lisbon (😍) from Web Summit 2025.

I sat down with Tom Haworth, founder of B13.ai, to talk about why “good enough” AI might actually be one of the most dangerous places we can get stuck.

And Tom says it’s time for the leaders of vibe coding platforms (e.g. Lovable, Replit, Cursor) to acknowledge that they’re great for “demo not memo” prototyping, but not great (today and maybe ever?) at delivering production-grade, secure code.

We also make a few detours as we detail a ridiculous week in Lisbon, including:

• How (shocker!) 90% of the conference was about AI

• Why “good enough” AI is not a good place to be

• Whether we’ll graduate to great AI

• AI’s ROI now and in the future

• Why it’s still iffy whether AI agents they can be trusted to accomplish complex jobs

• Robots wander Web Summit, do the Macarena, fall down

• How tennis great Maria Sharapova uses (IBM’s) AI

• How the presumptuous Web Summit’s app prominently suggests we all message Maria… (as if!)

• Visa wants to help creators monetize (yay! it me!), using Web3 technologies (yes, they said “Web3”; no, I was not expecting to hear a non-ironic use of that phrase)

• Why self-driving cars are the best robots — and coming soon to more of Europe

• How much Web Summit pampers (and corrupts) the media: I was like a stuffed goose. Hurray for Portuguese custard and other delicacies!

• How even the beer at Web Summit was high tech

  ---

  Subscribe to CRAFTED.!

  ---

Featured voices:

• Tom Haworth: Founder of B13.ai, a software consultancy that “empowers non-technical innovators and organizations to build with confidence, delivering market-ready solutions that we design, launch and run.”

• Me (Dan Blumberg) — I’m the host of CRAFTED. and the founder of Modern Product Minds. HMU if you want to build something great. I love building from zero to one.

And if you please…

• Share with a friend! Word of mouth is by far the most powerful way for podcasts to grow

• Subscribe to the CRAFTED. newsletter at crafted.fm

• Share your feedback! I’m experimenting with new episode formats and would love your honest feedback on this and other episodes. Email me: dan@modernproductminds.com or DM me on LinkedIn

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Drop me a line and let’s talk.

• Get psyched!… There are some big updates to this show coming soon!

• Hit reply! I read every message.

  Share CRAFTED.

Hey everyone,

I’ve gotten such interesting feedback on last week’s special Halloween / third anniversary episode featuring the anonymous CTO saying spooky things about AI and coding agents. So here’s a quick solo — voice memo style — episode for you. (Also typed out below)

The feedback ranges from people saying he’s “spot on” about the “insidious” problems that AI coding agents create, while others said “he’s holding it wrong”, i.e not using AI properly.

But first… I have a quick favor to ask you: Once a year I ask you to help me improve CRAFTED. by taking an annual survey.

Well... it’s been a year! And I have big plans for the show and some new things I’m working on, so I really want to hear from you.

And for one lucky survey-taker, I will make a $100 donation to the charity of your choice.

Please please please… It will only take five minutes…

Take the Survey

Listen on Apple | Spotify | YouTube | etc…

This voice memo episode is best listened to, but also should read well if you prefer. Here’s the full transcript:

OK, back to the feedback I’ve been getting. And if you haven’t listened to the episode, stop and go do that first so you have the context for what the anonymous CTO said about AI coding agents and why he says they’re useful, but can also often be more trouble than they’re worth.

Here’s a short note I got from a very senior security expert at one of the biggest tech companies in the world:

“Complete agreement. I go very slightly further than your CTO, but only very slightly. They’re dead on and I wish it were safe for more people to publicly say so. Actually producing code is the least of what I do.”

But most people I’ve heard from take the opposite view, saying coding agents are really useful and getting better. Here’s what one senior engineering leader texted me:

“Hey Dan! He’s wrong :) Def limits to “vibe coding” and yolo coding, but the productivity is real and increasing - on the low end, maybe a 30% boost and for the right use cases it can be a 5-10x - but it’s a skill and needs to be done thoughtfully.”

A saltier version of this comes from a from a former colleague and very senior engineer who helps startups and enterprises get the most out of AI. He said:

“Just finished listening and the only thing I can agree with this person on is that AI won’t be taking our jobs right away. I’ve been using coding agents almost entirely exclusively for writing and reviewing code since April. I barely open an IDE. I’m mostly using them to also build AI.

The fact that their first comparison was to auto-complete indicates they don’t have current experience with coding agents. Coding agents aren’t omniscient, you have to expose it to your infrastructure for it to understand it, just like you would an engineer.

I hate to use the “your holding it wrong argument” but this persons experience is in holding it wrong. They seem to expect it to be able to do _more_ than an engineer and then blame it when it doesn’t.

I use coding agents every day to understand, review and to write code. Too big, bug ridden PRs are still the humans fault that is using the coding agent not the coding agents fault. You can produce high quality, fully tested code _if_ you guide it. Does it get things wrong and go down wrong paths, yes, but show me an engineer that doesn’t do that too.”

Another former colleague weighed in and broadly agreed with this take, but also pointed out that AI companies need to do a much better job of explaining what the agents can and can’t do.

He says that marketing from Claude and the like and comments from CEO’s like Sam Altman imply that the AI is “magic productivity sauce,” not a complex tool to learn that takes practice.

I asked for an example of this disconnect between what engineers think they can do and what the coding agents actually can do and he said:

“The agents in some sense are expected to know things without context provided, given they’re trained to seem like they contain encyclopedic knowledge and the companies aren’t doing much to qualify the caveats at the marketing level.”

[For example]: “you can ask Claude about, say, docker compose and it’ll say correct stuff about that, and then you ask it to do something with that knowledge and expect it to not guess wrong, but it too often will, because it isn’t a knowledge system, it’s a good guess system, and the training data is overloaded with N different versions of docker compose with api changes, incorrect stack overflow answers, etc.

You might also tend to expect it to remember the context of your project and adapt its solution appropriately but typically you have to do real work to give it enough context at the right level to be able to expect that, even though some of these tools do index your code automatically and do their own investigation when needed etc, it just doesn’t “just work”, it generally requires active direction and supervision.

Meanwhile, a senior product leader took issue with one particular point from the anonymous CTO. Posting on LinkedIn, he said

“As I was listening to the newest “Crafted” podcast episode, the concept of “adversarial AI” came up. The interviewee kind of brushed it off saying it’s not really a thing, meaning you can’t really use AI to govern/manage other AI because they are all trained on the same things.”

So another product chimed in to agree, adding:

“You absolutely can use AI to manage other AI. It’s happening with LLM-as-Judge evals right now; that’s how you scale up your evals as a product team.”

And here’s the last one I’ll share today. It comes from a senior engineering manager at a big platform that you probably interact with everyday. I thought he’d give me some version of “the anonymous CTO is doing it wrong”, but he shared a more nuanced take, saying:

“To be honest, I agree with a lot of what this CTO said... I don’t see AGI actually “thinking” anytime soon, it’s a lot of marketing hype for sure.

That said, agentic coding is definitely effective and if there are changes generated as big as he was talking about, that feels like a misuse of AI more than evidence we can’t review everything going in. If a PR is un-reviewable by a human, the AI should be tasked with breaking things down.

I think the effectiveness of AI coding is also SUPER domain dependent. Client facing web apps have so much material out there already that these models can pull from and be effective with, while esoteric backend systems are not gonna have that same training background.

The key to me that was mentioned in the chat is that it’s all about the data. High quality data is going to be what sets apart the best models from the rest, and it is interesting how much of that data we’re in some ways giving away for free to the AI companies. Super interesting times though for sure!

---

Amen to that!

Well, I would love to keep hearing from you on this. Please keep the feedback coming... DM me or post on LinkedIn and let’s discuss!

And once again, please give me feedback on CRAFTED. I’ve just launched the show’s annual survey and it would mean a lot if you’d take five minutes to fill it out.

Take the Survey

These surveys are actually really important to podcasters and sponsors, so thank you in advance. And also for one lucky survey-taker I will give $100 to the charity of their choice.

Thanks — and see you soon!

And if you’re at WebSummit next week… me too!… let’s hang in Lisbon!

Enjoy this episode on: Spotify | Apple | YouTube | etc…

AI coding assistants promise to write your code, speed up your sprint, and maybe even make engineers obsolete. But what if the people building with them every day see something very different?

In this special Halloween edition of CRAFTED. — which also marks the show’s third anniversary! — a masked CTO shares what he can’t say publicly: that these tools are powerful, but insidious. In his view, coding assistants are great for auto-complete, but they can’t do what a human engineer does. He says they’re terrible at starting from scratch and will often suggest code that “works in a vacuum”, but not in context. And because AI can write so much code, so quickly, it’s hard to catch errors. In short, he sees an increase in short term velocity, at the expense of increased defects and an increasing dependency on systems that are untrustworthy.

I want to emphasize that this episode features the experience of one very experienced person. There are obviously others who disagree, who say AI coding agents are incredible, so long as they’re managed well.

However, there are also an increasing number of people questioning the sustainability of coding agents — they’re incredibly expensive to run — and also how good they are in the first place.

For example Andrej Karpathy, the guy who literally coined the phrase “vibe coding” and was early at OpenAI and Tesla, just said publicly on Dwarkesh Podcast that the path to AI agents is going to be a lot slower than people in the industry think it will be. He said coding agents are “not that good at writing code that’s never been written before” and that there is too much hype right now about where AI really is, with people in the industry, “trying to pretend like this is amazing, when it’s not.” He went on to say: “my Claude Code or Codex still feels like this elementary-grade student.”

Today’s guest agrees with Karpathy on a lot of this. Our guest has worked at startups, scale-ups, and big tech companies you’ve definitely heard of and today he’s at a very AI-forward company and using AI coding tools every day.

Below you’ll find key quotes and the full transcript of this episode, but it’s really best experienced by listening, so please do!

Also: to protect the guest’s identity, you’ll hear his words, but not his voice. An actor friend of mine recreated what he told me. I started by using AI for this, but then realized I knew a wonderful and kind human, so you’ll hear his voice. :)

Enjoy this episode on: Spotify | Apple | YouTube | etc…

Key Quotes

• 03:16 The myth of AI replacement: “The idea that AI can actually supplant a software engineer in their current role is basically nonsense.”

• 06:29 Why AI struggles without human input: “If you remove the human engineer from the equation, there’s no place to start from. The AI does not do well when you’re starting from scratch because it doesn’t have the real-world context or the continuous learning required to make that system better.”

• 12:21: The illusion of speed: “Coding assistants help you generate code very quickly. There’s an illusion that your velocity increases. What actually happens is you’re just shipping more bugs to production.”

• 13:30 More code than humans can review: “AI generates so much code that no human can keep that context in their head and review it in a meaningful way. At some point you just have to trust — but who are you trusting? You’re trusting the AI, and the AI cannot be trusted.”

• 14:02 AI & Junior Engineer Hiring: “The narrative that hiring trends have anything to do with AI is absurd. It’s not that AI is replacing junior engineers — it’s that companies are running lean and don’t have the bandwidth to train them.”

• 15:42: Where the AI Bulls and Bears Differ: “Whereas we see flawed systems that aren’t ready for primetime [...] they view this as ‘oh, that’s, that’s insignificant. They will get better almost immediately. It’s not a big deal.’ But we’ve been repeating this cycle for years at this point.”

• 19:50 Where AI Excels: “Where review and revise are part of the process already, that’s a really good place for generative AI because you already have a human in the loop.”

• 21:02: What builders need to unlearn “To the extent that people think these things are thinking or reasoning or on any path to AGI at all — they should discard that. These models don’t think. They’re very sophisticated pattern-matching machines, and that’s really it.”

Full Transcript:

Dan: Okay, anonymous CTO, what is so spooky and why can’t you reveal your face or name for us to have this conversation?

aCTO: Well thanks for having me, Dan. I appreciate it. I don’t know so much about spooky, but I think, what I’ve observed over the past year plus is that there’s definitely a gap between the AI’s capabilities and what it can do today and its trajectory and what people are selling on television, X, other social media platforms.

I’m an AI optimist. I believe in it as a tool to help supercharge productivity for a bunch of people, including software engineers, but the whole talk of AI coming to take people’s jobs or the idea that AI can actually supplant a software engineer in their current role is basically nonsense.

These models are just nowhere near good enough to do the same job, right? And the premise of it is basically that engineers spend all of their time simply generating code — that’s an output of the job that they do — but there’s much more that goes into the input in terms of thinking, problem solving, engaging with stakeholders, that sort of thing.

And so it can certainly generate a lot of code, but it cannot replace a software engineer. And I imagine we run into very similar situations with other professions as well, including, like, folks in the creative writing, video generation, that sort of thing.

DAN: And what is it about this moment that you can’t say this publicly?

aCTO: So, I’m working in a company that is very AI-forward. The company is leveraging AI to deliver a very useful product. And so we are delivering value to our customers for sure, but just due to the nature of who we are, what we’re building and how AI forward we are in terms of, our voice and, and branding, it would be a bit, um, uncouth of me to come out publicly to kind of state this opinion as frankly as I’m stating it to you here today.

DAN: Yet, it was important for you to say this, even though we’re gonna anonymize you, it’s important for you to say this out loud. Why is that?

aCTO: That’s a good question. Maybe I should ask my therapist. You know, I have to tell someone and, look, I’m in conversations, private conversations with, you know, a bunch of people behind the scenes and, you know, I’m speaking to you today, but I’m speaking for many people who are in the same position.

They’re working in AI. They’re making their, you know, livelihoods in AI, but they can’t actually say what’s really going on because, you know, frankly, the job market’s kind of in a shitter as well. Like some of us would rather be kind of working on other things. And so, you know, we just kind of keep these conversations to ourselves and hope that we just kind of ride this wave out.

Dan: Let’s stay closest to what you’re seeing with coding assistance, with how you’re using AI… What is it doing well? What is it not doing well, and where are the promises, in your opinion, so overblown?

aCTO: The thing it does well is it does generate code really fast, right? You can generate code much faster than any human can actually type it out. So if you as an engineer are leveraging these tools, these coding assistant tools, and you have an idea and you want to just sort of like get it typed out and have it work, when AI works really well, it knows kinda what you’re going for and it becomes like a very powerful auto complete. And so you start typing a function and based on the previous function you just wrote, and based on the work that you’ve been doing, it will auto complete to a very high degree of correctness, right? Like almost as if it read your mind. That’s when it works really well, and in that mode it’s great.

The problem is when you start to think about what led to that moment. There was a lot of work that needed to be done. A lot of code needed to be written by hand. Patterns needed to be established, things like that. In order to get to a place where AI can do its job really well, if you remove the human engineer from that equation, there’s no place to start from.

You’re starting from scratch, and the AI does not do well when you’re starting from scratch because. It doesn’t have the real world context and the continuous learning required to make that system, uh, better in a way that’s meaningful, right? It can continue to generate code and it’ll generate more code.

It’ll write over the previous code and it’ll make some improvements, but then it’s gonna introduce bugs on along the way. It doesn’t tie well to the overall system and it only has the codebase to look at, right? Now you’re thinking about, well, as an engineer I may be writing code, but within my memory, I know how our infrastructure is set up. I know what’s gonna work well within our infrastructure, what’s not gonna work well in our infrastructure. The AI has no clue about this. And if you’re vibe coding or just, you know, blindly letting the AI produce code for you, if you don’t pay attention to these things, you’re not gonna catch them. And these are like the more insidious problems that happen.

It’s not even the obvious bugs, it’s the stuff that just you catch as a human because you have the context. But if you don’t have the context, if you’re an AI and you can’t think, you’re just not gonna catch these things. And so this is really hard to explain in the abstract, and I think that’s why we can pretend it’s magical for so long, is because it is very hard to explain in the abstract.

But it’s not until you experience it, till you say, wait a minute, this is a cool tool, but this thing can’t do what I do. It’s not even close, right? I don’t care how good it is at generating code. It can’t actually take the place of a person thinking.

Dan: Yeah, I mean, I, I’ve seen similar versions of that, not in code, in my context, but I had an example recently where I interviewed someone. There was an analogy that AI had given me and it related to, for whatever reason, it related to Velcro. It had told me sort of very confidently that NASA had invented Velcro, which was not true. NASA had popularized Velcro. They didn’t invent it, but it was just sort of in there. And I just only ‘cause I happened to remember from like middle school, like the story of some guy who was hiking with their dog and the burrs get stuck to his dog and he’d like, let me research what is this thing? And that’s where Velcro comes from and it’s only ‘cause I had that memory that I, that I figured that out.

It was an example of like a really subtle thing that luckily I had the context and I didn’t, I didn’t, I’m also not naive enough to just read whatever AI says I should read, but I almost missed that one. Is that the kind of thing you’re talking about, where there’s some little nuance?

The irony is like you kind of have to be an expert to work with these tools, but then how do you become an expert if you work with these tools, if you’re younger?

aCTO: Yeah, so, that is a version of a failure mode of ai. We’re talking about hallucinations, basically because it’s a probability machine. That’s one failure mode and that’s pretty well known and understood. What I’m referring to though is situations where it doesn’t necessarily hallucinate or get something quote unquote wrong, like the syntax is right, it works in a vacuum, so to speak, but you just happen to have context about how the world works or about how your software system works in particular, that you know that even though this code is a working code, it will not work in the context of like, say, a deployment or the level of scale you expect, or whatever other context the AI just doesn’t have.

So it’s a bit more nuanced than that, but what you described is also a failure mode where it would just make things up.

Dan: Is this not something that you see getting better or could get better? Is this, is this a problem that you don’t think can go away in the, in the nearest future?

aCTO: Well, this is where you start to kind of hit the limits of my expertise. I’m a more traditional software engineer who’s versed in using these tools. I don’t build them, but based on my understanding and the way we train these models and the, and the way they work, they’re fundamentally just, you know, probability machines. And so they’ll always have a level of hallucination if they get to a place where they just don’t know how to, you know, complete, the sentence or the line of code or whatever. It’s hard to imagine getting to a place and, and frankly, in terms of whether or not it’s getting better, if I’m being honest, and I was having this conversation with a colleague not too long ago, they seem to be getting somewhat worse, and I think it has a lot to do with the garbage in, garbage out, right?

You know, you generate a lot of code, that’s maybe not good. You put it out there, it’s open source. The next model sucks that in, trains on that. And then it uses that training data as a source of like, this is the correct way to do this. And then it spits out that garbage yet again.

And then once you have that happen over and over and over again, the amount of high quality code or just data in general becomes diluted every single time. So on some level, the more we train these models with newer data that’s being coming out there, while AI is in the zeitgeist and people are using it, you might imagine they get worse over time.

Dan: This is the model collapse problem?

aCTO: Exactly.

Dan: On the topic of coding assistance, have you found, do they increase your team’s long-term productivity or do they just increase today’s coding output at the cost of some big, expensive refactoring that you’re gonna have to do down the road?

aCTO: Certainly the latter. I think that’s one of the most misunderstood things about coding assistants, because they are helping you generate code very quickly. There’s an illusion that your velocity increases, right? What this actually ends up doing is, you know, each engineer can generate code much more quickly, but at the very same time, it just means that your shipping more bugs to production, right?

So what we see is an increase of code being shipped — yes, 100%, can’t deny that — but a big increase in defects that are coming back the other side. And it’s kind of drowning teams in bug fixes and refactors. Like the AI just misses very fundamental things and it’s hard to review thoroughly because it generates so much code, right?

Back in the before times, an engineer reviewing code would maybe have, you know, five files to review… ten files… kind of seemed like a big PR, right? With AI generating so much code, it’s not uncommon to get reviews with 20, 30, 50, 100 files that need to be reviewed. No human can keep that context in their head and actually review that code in a meaningful way.

At some point, you just have to kind of trust, but who are you trusting? You’re not trusting the engineer. You’re trusting the AI and the AI cannot be trusted. Then you ship

Dan: What about another AI, an adversarial AI? Does that work?

aCTO: Well, I don’t know that there are actually any adversarial AI’s when you come down to it. It’s all trained on the same data, right? Maybe there’s some post-training optimizations that are made that make them differ, in some ways. But I think the idea of adversarial AI’s are… you know, I think it’s a misnomer. I don’t think it’s, there’s such a thing as that, honestly.

Dan: What are you seeing in terms of hiring when it comes to junior engineers? Have you slowed your own hiring of junior engineers because of AI in any way?

aCTO: No, and the narrative that the hiring trend now has anything to do with AI other than cost, I think is, is absurd. Anyone who believes that is, either you know, ill-informed or intentionally misleading you. There’s definitely an issue with hiring, particularly with junior engineers, but this is not because AI is so good that it’s taking the place of junior engineers.

I just have not observed that. I mean, that is a theory that people have. I may have even spouted this theory in the past as well, but there’s just no evidence to support that whatsoever. I think we’re still dealing with the post-COVID hiring boom trail off. I think that’s why it’s still difficult to find work.

It’s still difficult for junior engineers, because they’re gonna be the last ones to come in, right? Like you hire junior engineers when things are great and you have excess support and bandwidth to actually train and supervise junior engineers, because that’s what they need in order to grow. Well, if you’re trying to run lean, you wouldn’t take that on.

And so that’s one of the reasons why juniors are having a hard time now. It’s because these companies are shedding roles, not gaining roles, and they certainly don’t have the bandwidth to kind of onboard and invest in people who are junior. They want that senior hire that they can just snap their fingers, bring them on board… they onboard in three weeks and they’re off to the races. That’s what people want.

Dan: When you get together with other folks who are in the industry, what are you really talking about when you get real about this stuff?

aCTO: It’s as much of the people in AI as the AI itself, right? When we observe the people who are really, really bullish on the technology, they observe the same things we observe in terms of the inefficiencies, the mistakes, the inadequacies, but they walk away with a very different view of the world. Whereas we see flawed systems that aren’t ready for prime time in some instances, or are only really useful and very narrow use cases. And there’s a lot of opportunity in those use cases. I don’t want to discount that at all, but they view this as very much, “oh, that’s, that’s insignificant. This will get better almost imminently. It’s not a big deal.” But we’ve been repeating this cycle for years at this point. They don’t actually get much better. Sometimes they’ll get better in very specific use cases, but they’ll be terrible in others.

And like I said recently, it seems like at least with regard to coding, they’ve gotten a little bit worse for whatever reason. And so we talk about the people and trying to understand, you know, what’s driving this person? Like, trying to get into the head of that person and trying to understand what drives them, because you know, these are people that also make decisions that affect us and our livelihood and whatnot. And so it’s really about the people as well and how they’re handling this.

It seems like, you know, there’s a, there’s a wave of disillusionment. That’s kind of springing across the industry right now, and you kind of see it hitting certain people early, some people a little bit later to the game. Some people seem like they’re nowhere near there at all. So a lot of the times we spend our time just talking about the people involved and not so much on the technology itself.

Dan: We’re talking about AI and I’m being pretty unspecific actually: we’re really talking about generative AI and like large language model AI specifically.

aCTO: Correct.

Dan: And I know that you have a lot of experience, you know, back when it was just called machine learning. You’ve used that in a lot of context, I know. Do you have a take on this approach of like “AGI or die”? It’s like large, large, large, large, you know, crazy large language models when, you know, there are many other approaches to, and they’re far less, you know, environmentally egregious. So, I’m curious if you have a take on this path towards the most enormous model possible — or do we even need to be on this path at all?

aCTO: I definitely don’t think we need to be on this path. I think the reason why we’re on this path is that, OpenAI released ChatGPT and it blew up in terms of usage and users and I think the investor community simply saw an opportunity. I don’t even think AGI is a thing that they’re thinking about. I think this is a thing that OpenAI talks about. You know, they’re out to make money, frankly. They talk about AGI helping everyone, bringing us all together and, you know, that’s all marketing speak, but you know, they just launched a social media app… What are we doing here?

So I think what investors saw was something that resonated with consumers and they saw a business case, right? This thing blows up.

Now all of a sudden you have millions and millions of people at least trying this technology out and so that was the thing that really fueled this path in my opinion. And so then when the next version of ChatGPT comes out… And it was a lot better… I mean, that was it. That was it for investors. It was like, we are all in on this technology. They made the assumption that we would continue down this level of improvement over time.

And then, you know, you have entrepreneurs, you can’t really blame them… they’re gonna build where the dollars are going and the dollars are almost all going into the AI segment. And so now you see more startups there, and so more people are employed in this segment and then that just creates a flywheel of more investment, more investment. And so that’s kind of where, where we ended up.

But this is not necessarily following the most logical path to AGI or really anything as it relates to AI.

Dan: Are there particular use cases or, companies that you think, are applying this technology in just the right way?

aCTO: I think fundamentally in situations where review and revise are part of the process already… that’s a really good place for generative AI because you already have a human in the loop. You already have the human judgment aspect of it as a piece, as a way to actually get work done. AI just simply supercharges it.

So creative aspects… If you’re writing an essay or something like that, actually I do think that’s a great use case for AI to generate ideas, to kind of help you articulate something that you’re already thinking about. Artists who create digital art, they can describe what they’re looking for, they can get a starting point.

They can revise things with the AI, with more prompting and whatnot. This is inherently a create-review-revise sort of feedback loop. Where Generative AI is like really good at that, but you still need that human in the loop. That doesn’t mean we don’t need artists anymore. It doesn’t mean we don’t need writers anymore. Coders fit into this. It doesn’t mean we don’t need coders anymore. It just changes how those people allocate their time to a lot more sort of like review and feedback and maybe less typing or mouse clicking, whatever the domain may be.

Dan: Is there an assumption about AI, generative AI specifically, that builders need to unlearn before it’s too late?

aCTO: That’s a good question. To the extent that people think these things are thinking or reasoning or on any path to AGI at all I think they should just kind of discard that these models don’t think or reason at all. They are very, very, very sophisticated pattern matching machines and that’s really it.

And I don’t mean that to kind of dismiss the hard work that went into developing these models. Brilliant people who are, you know, far smarter than I am, put a lot of blood, sweat, and tears into this work. And so I want to commend them on that. But at the same time, the idea that these models actually think in the way that we think… Frankly, we don’t understand the way we think as humans, right?

The brain is still very much a mystery. So the idea that we could reverse engineer that and place it into a machine and produce social media applications with it… It just doesn’t make any sense. And so I think once people understand that these are just auto completes probabilistic machines that are just taking the data that’s on the internet and guessing what the next, you know, letter or numbers should be… I think that’s the thing that really will help people understand the limitations they’re faced with.

Dan: When you talk to kids, about, you know, what they should be learning right now. What’s the advice that you have or the approach that you take?

aCTO: Well, I had one conversation with a kid in particular that comes to mind and I used that time to tell them kind of what I just said to you… Not so much about the reasoning piece but AI is not an actual consciousness, right?

The thing that scares me about the kids are like the ones that are isolated and looking for a friend someone to talk to and thinking that AI is — quote unquote — their friend or anything like that.

And so when it comes to kids I’m a bit more on the existential side of things right? And kind of guard them from being very naive about what this technology is.

Setting that aside, I would say to a more mature kid, maybe someone who’s in high school who can kind of discern these things a little bit better, or even someone who’s of college age that you don’t want to outsource your critical thinking to the AI.

Use it as a tool for sure. It’s there to help you. It’ll speed you up, but as soon as you start. The process of stopping thinking and just throwing everything at AI and accepting its answers, you’ve kinda lost the script on why we’re here in the first place.

Dan: Yeah. That’s the existential thing you just mentioned.

aCTO: Yeah.

Dan: All right. Well that’s, that’s a note to end on a happy Halloween.

aCTO: Happy Halloween.

Dan: How do you plan to celebrate?

aCTO: Trick-or-treating, of course.

Dan: Right on. Thank you so much. I appreciate you doing this. this was fun and uh, I dunno if we’ll have more of these anonymous guests on the show, but if you like this let me know and maybe we’ll do this as a regular thing. It could be a fun little confessional here on the show. Thanks, man.

aCTO: Awesome.

Enjoy this episode on: Spotify | Apple | YouTube

Hey friends, here’s a jaunty debrief from PopTech, a notoriously hard conference to describe, that always features obscenely talented entrepreneurs and changemakers.

In this episode, Kwaku Aning, Sarah Rose Siskind, and I share some of the great stories and great vibes from this year’s conference, including:

• FetusGPT, Sarah’s madcap experiment to train an AI on everything her soon-to-be-born baby is hearing from inside the womb

• Why Colossal Biosciences is de-extincting the dire wolf and other “charismatic animals” (dodos and woolly mammoths are in the works) — and why de-extinction is an important goal that will help us solve lots of other problems along the way

• “Shade-as-a-Service”, a new idea from climate champion, farmer, and entrepreneur Eben Bayer, the founder of MyForest Foods (maker of MyBacon, the top-selling non-meat bacon). The idea is to launch giant parasols into the atmosphere to cool the Earth below.

• Why Tibet and Taiwan are so key to the tech industry (not to mention global stability); Tibetan PM-in-Exile Lobsang Sangay was a speaker.

• How to make progress on what matters most to you, featuring a prioritization exercise from Deep Future inventor and investor Pablos Holman

Enjoy this episode on: Spotify | Apple | YouTube

Featured Voices:

• Sarah Rose Siskind, science and comedy writer and the founder of Hello SciCom, a STEM communications agency

• Kwaku Aning, professional connector, founder/principal of RetroFuturism Consulting

• Dan Blumberg (me!), host of CRAFTED. and the founder of Modern Product Minds. HMU if you want to build something great. I love building from zero to one.

And Pretty Please...

• Share with a friend! Word of mouth is how podcasts grow!

• Subscribe to the newsletter at crafted.fm

• Share your feedback! I’m experimenting with new episode formats and would love your feedback on this and other episodes. Email me: dan@modernproductminds.com or DM me on LinkedIn

• Sponsor the show? I’m actively speaking to potential sponsors for 2026 episodes. Let’s talk!

• Get psyched!… There are some big updates to the show in 2026!

Hey friends,

There are some big updates to the podcast in the works that I’ll share soon…

In the meantime, here’s a quick (nine minute) debrief from Climate Week / UN General Assembly week with my friends Kwaku and Lendy, where we discuss:

• How seemingly normal everything felt, in spite of [...you know...] everything

• AI will destroy the climate?

• AI will solve climate change?

• AI will kill us all? (If Anyone Builds It, Everyone Dies)

• A call for AI Red Lines

• The UN takes action on AI

• A plea to “stay in the game” (even though it’s hard)

Joining me from New York are:

• Kwaku Aning, creates strategic partnerships that drive meaningful change

• Lendy Krantz, collaboration strategist, helps companies reimagine their operations in physical and virtual environments

And you can join all three of us (hi, I’m your host Dan Blumberg!) from October 7-9th at PopTech in Washington DC. It’s a great conference and I’ll be interviewing many of the technologists and futurists who will be on stage for future episodes of the podcast. If you’d like a discount code, DM me on LinkedIn or email me: dan@modernproductminds.com

As always, I love getting feedback (of any kind!), so hit reply or or email me with any/all of it.

Until next time,

Dan

What’s up with “the MIT study” that claims 95% of all AI pilots fail? Did anyone actually read it beyond the headline? (I did! And I have some thoughts on the study and the current bubbly narrative.)

Also: the good, the bad, and the quietly dystopian side of putting AI in kids’ classrooms.

And… are robots really the thing Melania should be worrying about?

That’s just some of what Kwaku Aning, return guest and founder of Retrofuturism, and I get into on this very lively, very bubbly, and very uncrafted edition of CRAFTED.

More new episodes—and a major update to the show—are coming soon. Subscribe in your favorite podcast app and to this here newsletter:

Subscribe now

Come hang with us at PopTech

Come hang with us and see live recordings of CRAFTED., at PopTech!

PopTech is a “curator of what’s next” and this will be my third time at the conference. I keep going back because I get new ideas, new inspiration, and really get to know the attendees and speakers. This year’s talk’s include “A possibilist’s guide to the future”, “AI: In service to human(ity),” “Vibe coding for human rights” and more.

To see the full list of talks and speakers, see PopTech.org and if you’ve never been before and would like a discount, DM me on LinkedIn or email me: dan@modernproductminds.com

Referenced in this episode:

• MIT study on AI profits rattles tech investors (Axios)

• Full 26-page MIT study (Scribd)

• AI Is a Money Trap (Ed Zitron)

• The Fever Dream of Imminent Superintelligence Is Finally Breaking (Gary Marcus in the NYTimes)

• How Chatbots and AI Are Already Transforming Kids’ Classrooms (Bloomberg)

• Alpha School – the “AI-Powered Private School”

• Melania Trump Has a Warning for Humanity: ‘The Robots Are Here’ (NYTimes)

Liked this episode?
You’ll also like my conversation with Khan Academy’s Chief Product & Learning Officer on what happens when AI becomes your tutor—and what it means for the future of learning.

As AI models grow larger and more powerful, they promise incredible capabilities — but at what cost?

Karen Hao is an AI journalist and her new book, Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI, is a New York Times bestseller.

We discuss whether the largest AI models are worth their hefty footprint: They consume massive amounts of electricity and water and Karen argues that smaller models better balance cost vs. benefit.

Karen, who has reported for The Atlantic, MIT Technology Review, and the Wall Street Journal, will also provide a view of AI from outside — far outside — Silicon Valley. She’s reported on AI from across the Global South and says many there feel that AI is a new form of colonialism.

We’ll hear about the fight over data centers in Chile, how New Zealand’s Maori people are using AI to preserve their indigenous language, and why it’s a problem that AI can speak any language, but can only really be policed in a few.

(This episode is a rebroadcast from October 2024. We spoke while Karen was still writing the book, so we do not discuss her deeply sourced reporting from inside OpenAI.)

CRAFTED. is produced by Modern Product Minds, where host Dan Blumberg and team can help you take a new product from zero to one... and beyond. We specialize in early stage product discovery, growth, and experimentation. Learn more at modernproductminds.com

Listen now: Spotify | Apple | etc…

AI-generated voices aren’t just realistic — they’re changing how brands, creators, and agencies bring ideas to life. In this episode, Wondercraft co-founder Oskar Serrander demonstrates how their “Canva for audio” is unlocking rapid prototyping for high-quality audio ads, content, and storytelling.

You’ll learn:

• How Wondercraft enables you to go from concept to creative in seconds

• Why when AI makes execution easy, ideas and taste matter most

• How brands can test creative faster (and smarter)

• Why audio is still such an under-leveraged medium

• The future of artificial voices and what it means for storytellers

Oskar also shares his take on where generative AI is heading, why sameness is the enemy of brand, and what this all means for the next generation of creators.

Moments:

• 00:42 Why ideas and taste matter most today

• 02:55 Why audio is still an under-leveraged medium

• 05:21 Demo: Creating an audio ad in seconds with Wondercraft

• 10:39 What's happening behind the scenes

• 14:14 Synthetic audio is weird!

• 17:28 Why Oskar is so excited for this era of human creativity

Listen/watch now: Apple | Spotify | YouTube | Snipd | etc…

Kevin Smith is building a totally new kind of podcast app.

Snipd is an AI-native podcast app and building it required a few mindset shifts.

First, what even is a podcast? The way Kevin sees it, podcasts are knowledge. So where most podcast players are, as Kevin calls them, "repurposed music players", Snipd is designed to help you learn. As people listen to episodes, they, or an AI, can save “snips” or interesting moments that they want to remember or share. And the app will also help you review what you've heard, so it reinforces what you've learned.

A second mindset shift is how Kevin had to retrain his engineering brain to build with generative AI. He no longer thinks in if-then-else statements. Rather, he asks himself: How would an intern do it? And not just one intern, but infinite interns…

I learned a ton from the way Kevin thinks and builds, and you will too.

Plus, we discuss the future of podcasting, which looks pretty… weird. You'll talk back to your podcasts, hosts may be synthetic, and shows may not even be designed (at least initially) for human ears.

Listen/watch now: Apple | Spotify | YouTube | Snipd | etc…

Chapters:

• 01:30 Introducing Snipd

• 03:50 What led Kevin to found Snipd

• 06:10 How AI changes what's possible with podcasts

• 08:45 Building with GenAI requires a mindset shift

• 11:40 How would an intern solve this?

• 12:35 How podcast listening and podcasting will change with AI

• 17:35 Why apps will become your "best friends"

• 22:00 Why you may talk back to your podcasts

CRAFTED. listeners can try Snipd, and get a free month of the premium version, here.

Listen on Spotify | Apple Podcasts | YouTube | etc…

What is a good money habit in 2025? And how do you actually help someone build one—without boring them, shaming them, or losing them in the first five seconds?

Chief Product Officer Tim Hong shares how MoneyLion designs for emotion and creates content and products that inspire people to take action.

MoneyLion is a personal finance platform used by millions of mostly younger Americans who are just getting started with their money, so, as Tim says: “It's actually less about bad habits that we fight. It's about having no habits.”

Tim also shares how AI could create a truly personalized (1 of 1) financial advisor, why most financial apps are “like going to the DMV”, and how things like open banking and embedded finance can change that…

Chapters:

• 01:30 Tim has literally measured how short our attentions spans are at a brainwave startup

• 03:49 What MoneyLion does and why content and storytelling are so important

• 08:30 What even are good money habits today?

• 12:00 How MoneyLion uses AI to create personalized content

• 13:40 "Talking to your money" with AI

• 16:30 How building with GenAI is different

• 20:30 Building with non-deterministic systems

• 24:30 "Self-driving money": Tim's not so sure people want to fully give up control

• 29:30 Why so many financial apps feel like "going to the DMV" — and how that's changing with open banking and embedded finance

—
Looking for your next episode? Here’s another fintech one you might enjoy:

—

—
Oh, and see how Dan and Modern Product Minds can help you build better products.