[cladopa]

I don't thing the problem is AI, but the mindset and trainning. I have probably as many or more AI projects that this man has but they are extremely useful, even if most of them I won't even sell.

This is like a kid playing videogames instead of studying, you take the console away and force the kid in front of a book and the kid will spend most of his time looking at the wall and dreaming.

I am engineer with very deep programming background that have managed people, with real experience in the real world.

One of the best things about AIs is that you can test crazy ideas and create prototypes very fast. Only one in a hundred will work great in the real world, but you have to create the 100 before to know.

Creating the 100 before AI was extremely expensive, and took so much time.

For me it is liberating and gives me focus because I can spend so little time testing prototypes and spend real time in what is really important and works.

This is something I learned from game developers: If you are going to create a game, you spend a weekend testing the dynamics and the gameplay of your prototype to know if is is fun. You use boxes, no textures, no complex sounds of music.

Then if it works and is is so fun, you create the game! You can spend 2 years creating the game after that.

You don't spend two years doing a Game only to realise later that is not fun, and you either spend 3 more years or abandon it at this moment.

[bwfan123]

> friction = focus, focus = product

AI tools have put friction where it should be - by eliminating incidental friction. By incidental friction I mean, things that were really not ambiguous, but were made so due to lack of access to resources.

As an example, if i needed to navigate, I used a paper map. There was friction in pulling out a map, planning a route etc. This took time. With digital mapping apps this sort of incidental friction is not there.

Real friction is inherent ambiguity. For example, what product does the market need ? By eliminating incidental friction, AI allows us to focus on the smallest hard-problem where there is real-friction.

[leedrake5]

This distinction between ambiguous and incidental friction is brilliant. Was just talking about it with my wife because your specific example of a map has been a running debate between us. I just pick the least cost path via the algorithm to the point where I have poor spatial awareness. My father in law insists on using paper maps because he wants to know where everything is to be helpful. For me, the ease of the algorithm solves incidental friction. For him, it eliminates helpful ambiguous friction by denying him the opportunity to learn.

The wrinkle that needs to be added is that there are no truly universal rules as to what counts for incidental vs ambiguous friction - the definitions are relative to individual/project goals. I am working with some scientific instruments to map out chemical data, and 3d modeling is needed. I don’t particularly care about 3d modeling - it is incidental to me. The chemistry is the focal point. So the STL files are vibe coded so I can keep my focus on chemistry. But if I were working for the latest marvel movie, the reverse would be true. The actual chemistry would need to fit the script and the visual effect intended. To a scientist the visualization just needs to be good enough. To the film director, the world building physics and chemistry instead become the supporting actor. The challenge introduced by AI is that in ruthlessly eliminating incidental friction, you are being deliberate about what you choose not to learn. This is fine at a task level - but how many of us “found” our current expertise through incidental friction in the first place? I never wanted to do chemistry, I went to school for something else. But incidental friction led to discovery. That is my biggest worry, particularly for students and early career folks.

[jmux]

In the past I found that I had poor spacial awareness - a few months ago I started using navigation apps solely in the “north up” orientation. this is much less intuitive when navigating, but forces me to think spatially about where i am and I’ve found has helped me retain much more spatial context about my environment.

I’m much more able to navigate without the map than I was before I started this experiment - and it’s had the added benefit of giving me the ability to know what part of the 101 I’m on solely by the angle and shape of the nearest exit.

I hope eventually AI gets to a point where I can split the difference between ambiguous and incidental friction like this, although tbh I’m not really sure what that would look like.

[theshrike79]

Wait until you hear about languages where the spatial context is inherent in the language.

People who speak those languages have an almost supernatural way of knowing where is north intuitively, because they HAD to do it to communicate.

[apsurd]

I was hunting for a way in to side with your father in law. But you landed the point: friction is relative.

Digital maps clearly solve the end-goal needs for most people. But like your father in law, there’s definitely a loss in that exchange.

Bearings are incredibly useful. I remember navigating myself and my partner out of a small town on vacation by the position of the sun. It was international so we didnt have internet at the time. Im never going to live that one down!

[bwfan123]

> That is my biggest worry, particularly for students and early career folks

Agree, and I have the same worry, and so did the OP to the point that he considers canceling his AI subscription. Most of have to first get through the incidental friction to be able to arrive at expertise to address ambiguity.

[CamperBob2]

You don't spend two years doing a Game only to realise later that is not fun

That does happen on occasion, the commonly-cited example being Half-Life. How awesome would it have been if the Valve team hadn't had to waste so much time, money, and personal energy on their initial failed prototype?

Unfortunately most studios ship their failures, either because they don't realize they built something crappy or because the alternative is bankruptcy. A cynic would say that if AI can reduce the cost of experimentation, it will only result in more bad games, while an optimist would argue that it will result in more good games. I think we'll find that they're both right.

[Aurornis]

> You don't spend two years doing a Game only to realise later that is not fun, and you either spend 3 more years or abandon it at this moment.

People do this all the time. It's such a common problem in startups that all of the books, courses, advisors, and everyone else with experience talks about finding product market fit early and shipping MVPs to validate the product.

It's the most common startup advice and people still ignore it and build unvalidated things for years anyway.

It's too easy to get started on your big idea and then switch to a rhythm of working on the next task without ever stopping to validate the big direction

[prmph]

But it is also not always or often the best to flit from one idea to the other, never going deep, chasing signs of validation that may never come unless you put in enough work.

Almost any reasonable idea that is not obviously bad (as in having some clearly insurmountable technical or market hurdles, etc) can be made to work.

What makes ideas work (and this is what separates those with a true entrepreneurs mindset from those just playing the lottery of "MVP"s) is the creation of what I call a viability field around them.

When a person decides to create an app, or some other product, or a certain business, they have not exhaustively considered all the possible things they could do and decided that this is the objectively most viable thing to pursue. They commit to a space they like ot be in, that feels like a good match for their skills and temperament, etc.

Then, within the general idea, the key I think is to begin creating a product, the development and marketing of which acts as a substrate for the learnings you need to succeed in that market. AI used judiciously can help you to cover ground more quickly, or at least be a bit more fearless about attempting that.

Basically you should build something that allows you to learn, to pivot, to adapt as necessary until you find product market fit. Rarely, you'd need to throw everything out, but even then I often argue it is matter of personal decision rather than any fundamental roadblock. If you you are really committed to an idea, and have developed it enough (not just a 1 week MVP) to deeply understand its context, there is almost always a tangent you can take that brings you to success without throwing out everything.

[bko]

Strongly agree. I would take it one step further, consider this line:

> Slopping out a 10,000 LOC untested Python/JS mess in 5 minutes helps nobody. The thought of this happening in every commercial environment simultaneously is horrifying.

You should def try to commercialize your slop and get feedback. No one cares about your tech stack or whether it's maintainable. Does it add value? You'll get a strong signal as to whether it does or not. And adding value, picking the right problem in the right domain, that's the hard part. You can always re-write or clean it up. Didn't Javascript start out as a weekend project? (maybe not a great example depending on how much you like JS)