u/davesaunders

I spent about 30 years in product development, and my YouTube channel is where I dig into AI and startup claims from that angle. The newest deep dive covers what happens when AI writes a whole app and 1.5 million keys end up sitting in the open: https://www.youtube.com/watch?v=u_09yNph1ME

I spent thirty years running product development before I started making YouTube deep dives, and this one took longer than usual to get right. Duolingo went AI-first, the internet revolted, and every take I saw stopped at the backlash. I wanted to know what the company's own filing said about the decision, and the gap between that and the public story is bigger than I expected.

I want critique on the video itself, not just the topic: does the pacing hold, does the argument land, is there a spot where you'd click away? https://youtu.be/7bVKLPZ5FEA

Commonwealth Bank of Australia cut 45 call-centre roles in July 2025 and named an AI voice bot as the reason. Six weeks later they asked those people back, and put in writing that the decision was an error.

My video walks through why that happens, and it comes down to the containment rate. The bot closes the easy calls, so what reaches a human is the residue: longer, angrier, with no easy calls left to balance out a shift. The callers it can't finish just ring back, so one failed contact turns into two. Containment measures the bot. It doesn't measure the work.

That's the sort of thing I put out on my channel, Dave Saunders. Mostly operator-level breakdowns of where AI actually lands inside a business, from about 30 years of building products.

https://www.youtube.com/watch?v=MJs1Sn2j1as

Small channel, long-form analysis for founders and operators. The format is taking a claim everybody repeats and checking whether it survives contact with the source.

The latest one is the statistic that AI beats lawyers at contract review, 94% to 85%. It traces back to a 2018 vendor whitepaper: five NDAs, twenty lawyers, and the report itself says the answer key was assembled from the best answers of all the participants, the vendor's own AI included. So the system under test helped write the key it was graded against.

My structural problem is everything that comes before that. I spend the first several minutes being scrupulously fair: nobody hid anything, the caveats sit right there in the original document, and it took eight years of repetition to strip them out. I'm convinced that fairness is the only reason the criticism lands at all. I'm also fairly sure it's where I lose the people who clicked expecting a takedown.

So the question for anyone else making analysis or commentary: do you front-load the payoff and backfill the fairness afterward, or earn it in order like I'm doing? I've watched channels do both and I honestly can't tell whether the front-loaders are winning on structure or just winning because they're bigger and the algorithm forgives them more.

Video is here if the pacing question is easier to answer by watching than by reading me describe it: https://youtu.be/85PpZKHQR4Q

I made a video about the asking part, because that's where the gap actually lives. Three moves, and none of them are prompt tricks: say what done looks like before you start, feed it the context it can't see, then take the 60% and drive the rest yourself.

The receipt I use is one of my own. A deck into an 11-page MRD at roughly 80% complete, in under 30 minutes of real work. The trap shows up right after a win like that, which is why the video doesn't stop there. The early success is exactly when you drop your guard, and METR's trial found experienced developers 19% slower on their own mature code while feeling 20% faster.

Long-form deep dive on my YouTube channel, aimed at founders and operators rather than at people collecting prompts.

https://www.youtube.com/watch?v=YOknZhmmhmk

That second number is the one I built a video around. BCG ran the study, and they disclosed their own conflict, which I say plainly on the mic. Inside the frontier of what the model handles well, the consultants got roughly 40% better. On a task deliberately rigged to sit just outside it, the same people came in 19 points worse than the control group.

Here's the thing about that boundary: it is invisible from the inside, and the model sounds equally confident on both sides of it by design. You get the 60% that's right and the 40% that isn't, and the 60% only helps if you already know the subject well enough to catch the 40%. Then comes the part that actually costs people money, which is that you stop checking. Deloitte issued a refund over it. A lawyer filed cases that did not exist.

I make long-form deep dives on my channel, mostly for founders and operators. This one closes on a three-question test for telling solid ground from the ragged edge.

https://www.youtube.com/watch?v=um-0pf3TFec

The number everybody quotes is the 55.8% greenfield speed-up. The one nobody quotes is METR's randomized trial, where experienced developers working on their own mature repos finished 19% slower with AI, and walked out of it believing they had been 20% faster.

Both results are real, and reconciling them is the whole video. I spent 30 years shipping product before I started making these, so what I wanted to build was the mechanism underneath: token prediction, the context window, the agentic loop, and who still owns the architecture at the end. Then an honest survey of the three tool tiers, and the Stack Overflow numbers that explain the mood (84% using, 29% trusting, 66% worn down by answers that are almost right).

It's a long-form deep dive on my YouTube channel, slow and mechanism-first, not a tips video. If you're deciding how much of your own workflow to hand over, measure your own drift instead of trusting the feeling.

https://www.youtube.com/watch?v=og1pI9_9Plc

Most AI tool videos I watch are demos that stop right before the part I care about, which is where the thing falls over. So I made the other version: the five workflows I actually run every week, and the exact point each one breaks.

I've been doing product development for about 30 years, and the pattern here is familiar. Most of what gets sold as AI right now is machine learning with better marketing on it. The FDA's cleared device list has well over a thousand products running machine learning, and the category quietly got renamed along the way.

My channel started at zero not long ago and the first subscribers came from DMs, so I'm not going to stand here and hand out growth advice. This is just the honest version of a tools video, because the other kind is everywhere already.

https://www.youtube.com/watch?v=9TXHM3M0nCM