3D Printing Hell, AI Frontier Models & The Reality of Product Strategy

Welcome to Week 51 of the Robotic Scholar Podcast! As the year winds down and the holiday "soft week" kicks in, we’re diving into the frustrations of hardware, the breakneck speed of AI releases, and the hard truth about business strategy in the age of LLMs.

In this episode, I share my painful departure from "3D Printer Hell" as I finally give up on the Creality K2 Plus in favor of something that actually works. Plus, we celebrate the surprise drop of Gemini 3 Fast and discuss why "platform + AI" is the winning combination that ChatGPT will struggle to beat.

Finally, we talk shop on product strategy: why you must ride the frontier model wave and why bolting AI onto a failing business model won't save you from being steamrolled.

In this episode:

0:00 – Holiday vibes and the beauty of "soft weeks."

0:58 – 3D Printing Hell: Why I’m ejecting on the Creality K2 Plus and moving to the Bamboo Lab A1.

3:45 – Lessons learned in hardware: Buy the Toyota Camry, not the project car.

5:00 – Gemini 3 Fast is here! First impressions and why the timing was a masterstroke by Google.

6:30 – The Power of the Platform: Why Gemini + Google Workspace is a massive hurdle for OpenAI.

8:45 – The Bitter Lesson: Why you should stop over-engineering and start riding the frontier model wave.

11:15 – Grok + Tesla: A real-world example of AI finally meeting functional platform integration.

13:30 – The Reality Check: Why AI won't save a bad product or business strategy.

18:20 – Wrapping up and looking ahead to the final episode of the year.

Connect with the Robotic Scholar:

Substack: [Link]

X (Twitter): [Link]

Gear Mentioned:

Bamboo Lab A1 Combo

Creality K2 Plus (RIP)

Jetson Orin Nano

#3DPrinting #Gemini3Fast #AI #ProductStrategy #Robotics #GoogleGemini #ChatGPT #TeslaGrok #TechPodcast

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.roboticscholar.com

Is Siri dead? Think again. 🍎🤖In this episode of The Robotic Scholar Podcast, we’re back before the holidays with a controversial take: Apple isn't losing the AI war—they’re just getting started. We break down why the impending integration of Gemini (or a similar LLM) into the Apple ecosystem could destroy the competition through the sheer "power of default."We also have a reality check on the AI job market. Are we actually going to lose our jobs overnight, or is the "Excel" era of AI just beginning? Finally, I share a personal nightmare involving my Creality K2 Plus, an emergency stop button, and a whole lot of jammed filament.Topics Covered:Why Apple’s "laggard" strategy is actually a winning move.The math behind Apple’s 2.35 billion devices vs. ChatGPT’s user base.Why we are currently in the "Mainframe Era" of AI.A frantic update on my broken 3D printer and the nozzle disaster.Quick thoughts on the Texas Longhorns season.Timestamps: 00:00 - Intro: We’re back! 00:33 - The Siri Disappointment: A missed opportunity? 02:16 - Apple’s "Spring" Surprise: Why the game isn't over. 04:09 - The Power of Default: 2.35 Billion Active Devices. 05:36 - Rumors: Will Gemini power the new Siri? 08:21 - AI Job Market Reality Check: Are engineers safe? 09:28 - Theory: The transition from Mainframe to Personal AI. 11:15 - 3D Printing Hell: The Creality K2 Plus disaster. 13:00 - College Football Outro (Hook 'em!)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.roboticscholar.com

Hope you enjoy this episode!

As an aged father of little ones, I can’t write AND podcast at the same time. My sleep-deprived brain just can’t handle it.So yes, the written version below is, in fact, AI generated based on what I discuss on the pod. Thus, the best experience I think is the audio, but in case you can’t experience that, a written version is below.Let me know if you enjoy it by subscribing below, I hope you do! See you next week…

Slides are also here for visual reference:https://gamma.app/docs/Game-Day-Gemini-Thinking-and-Building-R2D2-Mini-iuwljzzmor6wu2o

Robotics Scholar: Week 48 Recap

Gemini 3 is brilliant but slow, the “Code Red” at OpenAI is real, and I’m pivoting the robot build.

Welcome to the Robotics Scholar Podcast (formerly the Millennial Podcast—we re-branded!). This is Week 48. If you love all things AI and robotics, you are in the right place.

We have a lot to cover, from Google’s ecosystem dominance to building R2D2. But first, I have to say it: Hook ‘em Horns.

I had a great Thanksgiving watching my Longhorns beat the crap out of Texas A&M. Big Brother is back, baby. If you’re an Aggie fan, I’m sorry, but we are the best football team in Texas. It was a lot of fun, and fingers crossed we make a run in the College Football Playoff.

Now, let’s hop into the AI stuff.

The Gemini 3 Dilemma: Brilliant, but Slow

I’ve officially switched to using Gemini over ChatGPT, especially given the new Gemini 3 upgrade. As I mentioned last week, Gemini has huge advantages simply by being part of the Google platform.

However, I have one major gripe: It is so slow.

It’s amazing to use Gemini 3 with personalization, memory, and reasoning. You get incredibly high-quality answers. But man, the speed is an issue. I don’t think they could do “Gemini 3 Fast” fast enough (pardon the pun).

I truly believe that once they fix the speed, their usage and the quality of the experience will skyrocket. I keep looking it up, asking, “When is Gemini 3 Fast coming?” and the rumors say end of the year or early next year. Please, Google, give it to me. I’m on YouTube now asking: Give me Fast.

The ChatGPT “Code Red” is Real

We need to talk about the “Code Red” at OpenAI because it is very real.

The reason Gemini is winning right now isn’t just the model; it’s the experience surrounding the model.

For example, I wanted to put all my notes into ChatGPT to drive my planning and productivity experience. I simply can’t do that. It’s buggy. Canvas doesn’t really work for that purpose yet.

Meanwhile, Google has Google Calendar, Google Tasks, and Gmail all integrated natively. Everything just kind of works. This is where ChatGPT is struggling to compete. I’m even thinking about moving to Antigravity for coding.

I think Sam Altman is right to be worried. This is going to be their biggest challenge over the next few months. Competitors are coming. They need to cancel those trillion-dollar data center plans, put their heads down, and fix the product experience.

Robotics Update: The Pivot to R2D2 Mini

Since changing the personality tones for Gemini, the AI has been very forthcoming with me. It’s been “telling it like it is.”

My original plan was to print a life-sized R2D2. I thought it would be a fun way to learn robotics and how to use the Orin Nano chip I have. But the reality? That project is going to take 6 to 8 months to print.

Gemini basically told me: “You’re wasting your time building life-sized versions. You should think about using R2D2 to learn, but focus on designing your own robot.”

So, I listened. We are pivoting to the R2D2 Mini.

It’s a great learning device. It’s simpler—just three wheels, navigation, stopping, and those classic beeps. I suspect the LLM was trained on Star Wars data from the 70s and 80s, so it should handle this well.

I’ve already got the base here and just finished printing the right foot. We’re going to do this for the next month or two to get the fundamentals down.

The Big Challenge: Designing From Scratch

This brings me to the second part of Gemini’s advice: How do you design your own robot from scratch?

I have been in software engineering, design, and product management for over 15 years. But taking a physical hardware product to production? That is a massive new challenge. Honestly, I don’t know how to do it yet.

Early research points to using platforms like Fiverr or CAD Corner for sourcing designs. I don’t know if that’s going to work, but it’s going to be an adventure finding out.

The MVP Concept: I want to keep it simple. I’m thinking of a cute home monitoring robot.

* It checks if the stove is on or off.

* It helps ensure you unplugged the curling iron.

* It monitors rooms while you are away.

Nothing crazy. Just a practical, fun experience with a cute robot face. Eventually, maybe it evolves into doing laundry, but for now, we focus on the form factor and the basics.

Wrapping Up

We’ve got the tools. We’ve got the Jetson. We’ve got the 3D printer. We’re going to figure this out.

I hope you guys enjoyed the rebrand—calling this the Robotics Scholar feels right (the “Millennial” thing was dating me a bit). And I hope you enjoyed the look at the new “studio” (aka the playroom with the kids’ Elf on the Shelf watching us).

I’m plugging away and hoping to get something out in December or January. See y’all next week!

Thanks for reading/watching. If you have any tips on hardware design or CAD resources, drop them in the comments!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.roboticscholar.com

Hope you enjoy this episode!

As an aged father of little ones, I can’t write AND podcast at the same time. My sleep-deprived brain just can’t handle it.So yes, the written version below is, in fact, AI generated based on what I discuss on the pod. Thus, the best experience I think is the audio, but in case you can’t experience that, a written version is below.Let me know if you enjoy it by subscribing below, I hope you do! See you next week…

Welcome to week 47 of the Millennial Product Manager podcast—I think that’s what we’re calling it now.

I am back down here in my dungeon. The 3D printer is going here in the kids’ playroom, and we are preparing for Thanksgiving. It’s going to be great. I won’t be doing a pod next week, obviously; I think everybody’s going to be in their food coma, drinking, watching football, and hanging out with loved ones.

I grew up in Dallas, so I’m taking the whole family back there. We get to watch the Texas game, and... I’m worried. As a Longhorn alum, I am worried. But we’ll see how this week goes.

It has been a big, eventful week. Let’s get into it.

Gemini 3.0 and the Platform Advantage

The first thing I wanted to talk about is obviously the huge release from Google this week with Gemini 3.0. I’ve been using it, of course. It’s impressive. The “pro” part—the thinking part—is a bit annoying; hopefully, they release a version of Gemini 3.0 Fast that I can use for day-to-day stuff.

But what is happening as a result of this release is that I’m really reconsidering my tech stack here. I used to think I was going to be a ChatGPT user going forward, and that was just the platform I was going to use. But honestly, the Gemini 3.0 release has really got me thinking.

This, combined with the news that Apple is likely going to use Gemini for their Siri implementation in March, highlights something important. When you use something like Google or your Apple products—your iPhone, iPad, MacBook—those offerings have a robust platform that comes with the AI solution.

It’s not just about Gemini. It’s not going to be just about whatever Siri looks like in March. It’s the fact that all that stuff works well with Gemini.

One of the things I’ve noticed is that Gemini can search all of my Gmail. I can ask questions about Gmail. It works natively. It can pull stuff from my calendar. It works with Google Search, which is obviously a powerhouse and incredibly accurate. They’re not building stuff from scratch. When I use Siri, it works with my Apple Reminders, Notes, and Mail. It has all of my previous account history in both places.

I think going forward, the winners in the AI space are going to be the ones that couple AI with their platform advantage.

It’s not just about the models. The models are great—having a frontier model is important—but it’s everything around that experience. It’s the full offering that gives something like Gemini an advantage.

Don’t discount the platform investment. Don’t discount the platform experience that you’re going to offer along with the AI experience. What we’re finding is that AI by itself is not enough.

The Struggle to Find Value in Software

If you work in tech, or if you work the kind of job that I do as a product manager, there’s always this itch to be entrepreneurial and do something on your own.

But one of the things I’ve noticed is that I’m really struggling with what I would want to invest in on the side, or as a little startup in software. I actually am struggling with just what is even valuable anymore.

Like we were talking about earlier: Platform plus AI is really valuable. It’s not enough to go build an AI-based chat experience for X, Y, Z thing. That’s not enough. I’m finding I’m having a hard time thinking of things to make that leverage AI but are valuable without the platform investment that’s required. And creating platforms is hard. Creating marketplaces is hard. It’s a lot of grind.

It changes every hour. What’s actually valuable? What’s actually useful? Whole startups are getting destroyed, whole companies are getting destroyed overnight.

There’s a big rush, and people are taking chances trying to figure out what’s going to be worth it, but man, it’s really difficult to figure out what to invest in on the software side right now.

Elon Musk and the Sunk Cost Fallacy

I came across this video of Elon Musk talking about the five key things he’s learned building things, standing in front of a rocket. I think a lot of us have seen this video.

One of the things he talks about is how often engineers and technical people don’t ask the question of if the thing that they’re working on is the right thing to be working on. They’re so heads down and focused on optimizing the thing that exists already, that they don’t stop and ask the question: Why? Why does this product exist? Why does this experience exist? Why should it continue to exist?

I’ve actually been running into that a lot lately.

Coming back to the point about how AI is totally changing the game on what’s valuable in software—there are things that I have built, or that have been built as part of my job, that I actually doubt are valuable anymore. I doubt they should continue to exist in this new world that we’re all living in.

But I’m finding that it is actually difficult to get people to move on from these implementations. Even things that were built six months ago or a year ago. It reminds me of that point Elon is making: sometimes due to sunk cost fallacy, it’s hard to walk away.

In this world we’re living in with AI, you have to ask that question more and more. Just because a system exists doesn’t mean we shouldn’t build something completely new.

Link to video:

The Steep Learning Curve of Robotics

Finally, as you know, I’ve been on this robotics adventure. You can probably hear the 3D printer going in the background.

The learning curve for robotics is steep, and that includes things like 3D printing. It’s taken me a lot of test prints to figure out how to properly print things. If you follow me here on Substack or on Instagram, you’ve probably seen some of the examples, like R2-D2.

I printed a large version of him with a ton of supports, but the problem was those supports were just glued onto him. I couldn’t get them off. It was impossible.

Then I learned how to use tree supports, and that’s gone a lot better, but I still have to learn how to remove them properly.

Then I had to transition software. The Creality printer I have comes with a default software that was just messing everything up. So I had to switch to Orca Slicer and get that dialed in—learning from TikTok and YouTube how to properly print things on Orca Slicer.

Now I’m messing with this Jetson Orin Nano board from NVIDIA, and I’m back in the engineering days of flashing firmware, loading stuff via an SD card, and installing an SSD to expand memory so it can handle real-time models and computing at the edge.

It is just a lot to learn.

Honestly, though, it’s probably the most fun I’ve had with technology in a good while. The more time I spend with it, the more I’m like, “Oh yeah, this is fun.” It’s hardcore engineering stuff that I learned in school. It fulfills a need, especially given the flux I’m feeling in the software world regarding what is valuable to build.

It is steep. It is not for the faint of heart. But few things that are worth doing are easy.

Something you think about when you’re 41 is that you think you’re done learning—that you’ve learned everything you can and you can’t learn quickly anymore. It turns out you’ve still got a pretty good motor. I definitely feel that way as part of this journey. It’s challenging when you have kids and your schedule looks different, but man, is it fun.

That’s it for this week. Thanksgiving is around the corner. Today, it felt like collectively everybody at the company just said, “Alright, Thanksgiving is almost here, let’s give up,” and canceled a bunch of meetings. I think that’s great. We’ve all worked really hard.

Happy Thanksgiving, everyone. Hope you enjoy the break, and we’ll see you when we get back.

Cheers!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.roboticscholar.com

Episode Summary

In this episode, Ajay dusts off the mic for the first time in a while and jumps into a stream-of-consciousness update on life: building cute household robots, wrangling LLM context engineering at work, and navigating a very real health transformation from 208 to 190 pounds.

Topics Covered

1. Getting Banished to the Kids’ Playroom (But Actually Loving It)

Ajay now works from the downstairs playroom.

Turns out, being in the middle of kid chaos is surprisingly peaceful.

Cameos from roaming children retrieving toys.

2. The 3D Printer Is Loud Because Robotics Season Has Begun

Running a new 3D printer in the background.

Ajay is diving back into robotics for the first time since his electrical engineering days 20 years ago.

Inspired by modern robots from Figure, Neo, and Aptronic.

Mission: build cute robots (like Herbie from Fantastic Four), not dystopian humanoid nightmares.

First steps:

Jetson Orin Nano Super

3D printer calibration

Baby-steps only; the board isn’t fully running yet

3. Why Context Engineering Is the Real Challenge in AI Products

Frontier models are powerful, but getting them the right information is the hard part.

Most legacy systems are built for deterministic inputs/outputs, not LLM reasoning.

Old search-style chunks (tiny snippets with lots of metadata) don’t cut it anymore.

Ajay and team are expanding chunks, feeding the model more human-readable context.

Modern LLMs can handle larger context windows, images, full CSVs, and more.

Best debugging tool: full end-to-end evaluation traces.

Basically: great output is 90 percent context, 10 percent magic.

4. Health Journey: 208 → 190 and Still Going

Lost 18 pounds; doctor wants another 20.

Key lessons:

Calories matter more than you think, especially when eating out.

Stopping restaurant meals drastically reduced hidden calories.

Walking became foundational.

Losing weight made running possible again, despite a bulging disc.

VO2 max improved from 28 → 40.

Oura pointed out cardiovascular age was high, likely due to stress and lack of real rest.

Solution: real rest days on Tuesdays and Thursdays, not fake weekend rest while chasing kids.

Next phase: losing the final 15–20 pounds.

Closing Thoughts

Background 3D printer noise = authenticity.

Ajay plans to include photos of the robotics workstation.

Might make this podcast a weekly thing.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.roboticscholar.com

Happy Friday everyone! Welcome back to Ship Happens, your weekly product manager newsletter.

I'm using this newsletter to share at least three things l've come across this week to help you build better product. Subscribe so you don't miss these when they come out:

On to this week's thoughts and updates:

1. Everyone Wants a Magic Data Insight Button 🧙‍♂️📊

This week, I ran a poll on LinkedIn asking product managers what tool they most needed — 72% said they wanted AI-powered insights from their data. So I did what any curious PM would do: I built a prototype in Lovable called Trend Whisperer.

You can upload a CSV and get insights in seconds — it's not production-ready, but it's real code, real feedback, and a real reminder that prototyping is moving faster than ever. From idea to prototype in days? That’s the new normal.

2. The Rise of the Super IC and the Future of Work

I keep mentioning Elena Verna because, well, she keeps being right. This week, her post on the shift in full-time employment echoed something I’ve been living myself: the rise of the Super IC.

I've recently gone back to being an individual contributor, and it’s a whole different game now. With AI and better tooling, ICs can operate at massive scale. Product, design, engineering, even go-to-market work — the lines are blurring. The “solo entrepreneur” archetype is creeping into full-time jobs. And guess what? It's working.

3. AI Products: The Basics Still Apply 🤖

Despite all the buzzwords, building great AI products still means building great products. Full stop. Understand your customers. Solve real problems. The same product fundamentals still apply, whether you're working with LLMs, mobile apps, or something else entirely.

No amount of AI magic is going to fix a product no one wants. But pair a sharp understanding of the basics with new tech? Now you’re cooking.

4. The Luxury of Being Offline

Kareem Rahma shared a post on Substack Notes about how attractive it is when people are truly offline — and it hit home. I've been leaning into that lately. Less doomscrolling, more walks. Less noise, more clarity.

We weren’t built to consume an endless firehose of content. And the deeper I go into product and tech, the more I value the quiet time where actual thinking happens. Try it. Your brain might thank you.

Tools of the Week

Google Calendar + Todoist — I just discovered the calendar integration, and I’m obsessed. It lets you assign tasks to blocks of time like a boss.

ChatGPT Canvas + Notes — With memory features improving, I’m now using ChatGPT as my primary thought partner and journal. Way more fluid than traditional note apps.

What I'm Reading / Watching

AI Powered Search — On page 90-ish. Just hit the section on reflective intelligence. A great read if you want to understand AI through the lens of how people search and how products learn.

Children of Dune — Getting back into this sci-fi classic. It’s dense, but if you’ve read the first two, it’s a rewarding continuation.

Formula 1: Drive to Survive — Yes, I’m late to the party. Yes, it’s still amazing. It’s like Succession for racing fans. Highly recommend.

That's it for this week! Thanks everyone for tuning in.

If you've found this helpful, please consider showing your support by subscribing:

You can now follow along on Spotify or YouTube as well. I’ll be back around this time next week with more useful product manager things!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.roboticscholar.com

Happy Friday everyone! Welcome back to Ship Happens, your weekly product manager newsletter.

I'm using this newsletter to share at least three things l've come across this week to help you build better product. Subscribe so you don't miss these when they come out:

On to this week's thoughts and updates:

At the top: I've created a podcast and video version of these newsletters! I hope you take advantage of these experiences. I hope they're helpful. Hopefully you can listen to this stuff if it's useful on your run, on your commute. Or watching in your side panel while you're doing PRDs.

1. Do things that make your palms sweaty

I've been going through orientation this week and meeting a lot of my new coworkers, and one of the things that came up was an icebreaker: "What was some of the best career advice you've ever gotten?"

I had two. One we can talk about another time. But the other one was that you should do things in your career that make your palms a little sweaty.

You should do things in your career that make you uncomfortable. And I have to be honest, some of what I've been doing lately is a little bit uncomfortable. It's making my palms a little sweaty — working in AI and working in a new space.

I feel like I am living out that advice right now. And it's good advice. You should be thinking about how you can move forward in your career and make yourself feel a little uncomfortable — that means you're learning and growing.

Now, some might interpret that advice as: make sure you're doing things that you don't want to do, or that you have to go into management, or do something specific. You don't.

I think it's really more about personal growth and doing things that you find challenging and interesting and that really light your fire. I feel like that's the journey that I'm on right now and I'm really excited about it!

2. Apple should just give up on Siri

Unpopular opinion alert! But I'm really starting to believe that Apple should just capitulate in the voice assistant race — and capitulate to something like ChatGPT.

You could consider me your tech grandpa because I've been doing this since 2007. 👴🏼 One of the advantages of doing that is that you have a long lens into history. Like, you can remember a lot of stuff that sometimes gets lost in the sands of time.

A lot of people forget that Apple bought Siri in 2010. I had to look this up to get the info right…they bought Siri in 2010 — it wasn't even a homegrown product. And they've been trying to make it great since then, FOREVER ago.

This latest iteration of Siri that's supposed to be coming out is supposed to be incredible. It's supposed to have all of these AI features. And… it hasn't happened.

At some point you have to accept that Apple is, at best, a little behind on the AI side of things. At worst, just not able to deliver the kind of AI experience that users expect now, especially with the advent of ChatGPT, Gemini, DeepSeek, Perplexity… all these new products that are frankly amazing.

You have to lean into the things you're good at. And potentially outsource or yield ground in places you're not. This was all spurred by me doing some research recently on iOS and ChatGPT integration.

One thing I learned is that ChatGPT’s integration in iOS goes beyond just being a release valve for Siri. Previously I thought you’d initiate Siri, and if Siri can’t do something, it punts to ChatGPT.

But if you use the hotword "ChatGPT" before your voice command, you can go straight into ChatGPT. I’ve made advanced voice mode a hot button on my home screen. And it is just so much better than Siri. I don’t know if Siri will ever be that good.

Marques Brownlee did a video on this too, talking about something similar.

Apple has been working on this for 15 years. That’s a long time to be committed to something and not have it work. I don’t know anything about what’s going on inside of Apple, of course, but if I was there, I’d ask: is this ever going to happen or not?

Not to stoke the fires here. I’m just a guy. I work in tech. But ChatGPT as an acquisition target for Apple would make a lot of sense.

Google’s got Gemini. OpenAI’s tech is valuable — not just the model, but the experience. Maybe Apple should go after ChatGPT and OpenAI. Maybe that’s the end game…

3. Why OpenAI is probably raising money on ChatGPT the app — not the models

It’s making more and more sense to me why OpenAI would raise money based on ChatGPT and not the models themselves.

For years there’s been this assumption that whoever figures out AI at scale is sitting on a gold mine. And I think that’s true — but not in the way we expected.

We thought the LLMs would be the gold mine. That they’d generate billions or trillions in value. But turns out… they’re enabling tech. And that value is eroding because the cost of running LLMs is dropping quickly.

Now there are great free models — DeepSeek, Gemini, Meta's new Llama 4 models.

So what is actually valuable then? The ChatGPT experience.

You can see OpenAI separating OpenAI the company from ChatGPT the product. It makes sense. Because the value is not just the model — it’s the app.

And this ties into what we’ve been reading in AI Powered Search as part of the book club (around page 70). Good search experiences parse the query. They don’t just take it at face value. And the processing layer? That’s not happening in the LLM itself. It’s happening in ChatGPT.

Same thing with Perplexity — their secret sauce is in that query processing and ranking layer. ChatGPT is in the same space.

But it’s more than that. ChatGPT is becoming my life assistant. I use it as a daily driver.

Canvas is a great step for documents and notes, but there’s more potential. Tasks doesn’t really work the way I want it to. Projects doesn’t support the full feature set. You can’t use Tasks or Deep Research in Projects right now.

I’m sure they’re working on it. But it just shows you: ChatGPT needs more love. The value is there. And it makes sense they’d raise money on ChatGPT — not the models.

Because model costs are dropping to near zero. But ChatGPT’s value is climbing.

Remember when image generation melted their servers? That’s a sign of real demand. And a reminder that good product takes effort — to help users understand how new tech fits into their lives.

If they just announced "we have image generation," people would’ve been like meh. But they let it go viral — anime, art types, model training — it hit.

That’s what makes ChatGPT so valuable.

4. iOS is getting really complex

This might just be because I’m getting older. (Tech grandpa strikes again.)

But I found myself watching an explainer video just to understand ChatGPT’s new features in iOS. And it really made me realize: iOS is getting complicated.

There are so many features now. Apple does a good job pruning what doesn’t work, but it’s time for a cleanup. Hopefully that’s coming in iOS 19.

5. Heart health update

If you’ve been reading, you know I’ve been on this health journey during my break. Trying to reduce stress and put good habits in place before heading back to work.

I posted a thread about stress on LinkedIn (thanks for all the replies!) — and Tom Hale, CEO of Oura and my former boss, left a comment that stuck with me.

He reminded me that stress is a canary in the coal mine for heart health. VO2 max and cardiovascular age matter just as much.

Mine is about five years older than where it should be…

And I was like DAMN IT!!! I keep trying to fix this. Last time I made progress I was running regularly — six weeks of solid running — and it worked.

But I injured my back. (Tech grandpa strikes again! 👴🏼) So I went back to cycling and weightlifting, and it didn’t help. My cardio age climbed, and stress took over.

I was also doing saunas, but not consistently. Now I’m trying a new routine:

* Zone 2 cycling on off-days

* Weight training once a week

* Daily walks with the dog

* Sauna almost every night

It’s starting to work again!!!

My cardio age is dropping, largely due to a change in my VO2 Max.

Still a ways to go, but it’s trending right. More to come…

That's it for this week! Thanks everyone for tuning in.

If you've found this helpful, please consider showing your support by subscribing:

You can now follow along on Spotify or YouTube as well. I’ll be back around this time next week with more useful product manager things!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.roboticscholar.com

Elena Luneva and I had a great live session on communication with stakeholders and beyond! Lots of fun talking about what works, what doesn’t at all levels, and some of our presentation fails lol. Enjoy!

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This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.roboticscholar.com