A local "thought leader" wrote a ridiculous piece about software leadership in a post-LLM world, when clearly they'd never actually used an LLM to build anything, or actually led a team using LLMs. Lots of hand-waving but obviously no real experience.

As gp says, there's a big difference between theory and practice here, and a lot of the things we needed when we weren't using LLMs are still needed when we are, but it takes a bit of actual practice to work this out. It's still not at the stage where an Ideas Guy can make a real working product without someone on the team actually knowing how to develop software.

At least in my experience, so far. But the world is changing fast.

The last "Thought Leader" worth listening to was put to death by Athens.

Some that came after might be worthy of the title, but those who claim it for themselves aren't.

> Who has the hubris to make such strong and unwavering statements about a future nobody can see?

LinkedIn influencers

How come all the talking heads telling us AI has made whatever we've learned or built obsolete never point the lens back at themselves? I personally thing knowledge from entrepreneurs who learned their lessons in previous decades is valuable, but if what they're spouting is true it doesn't make any sense to listen to them, i.e. these thought leaders are desperate to tell you of their irrelevance. Same thing for every CTO who tells me AI replaces developers. That's a stretch, but if we could do that, don't you think it would be trivial to replace your job?

Have you ever sweat bullets at 3 a.m.? While Claude spins in circles unable to fix production without breaking five other things?

You will!

Interfaces will definitely evolve. Visual graphs and representations are useful but speaking to an agent will become mainstream as its faster than typing. Also the ability for agents to code on the fly will open up different interfaces. For instance you could say "show me the impact that our marketing campaign x over this time period" and out comes graphs that were coded. Drawing might even make a comeback for instance when designing a website you just cross out things you don't want, draw boxes of where you want things, talk at the same time saying what you want in that box. Then some people are using virtual relativity. Not everything will become a chatbot but its definitely going to evolve with chatting being an integral part of the user interface.

Yeah, it's crazy to think an opaque chatbot will be preferable to a well designed UI for most users. People don't like badly designed UIs, but I'm pretty sure most people under 40 prefer a well designed UI to a customer service agent. We call customer service because the website doesn't do what we want, not because we don't want to use the website.

I usually find chatbot interfaces completely infuriating. I know the UX paradigm of click-reduction went the way of the dodo (and rightfully so, because it was based on bullshit research,) but I think it’s funny that completely removing the user’s agency and visibility into any process, and turning 3-click processes into 200 keystroke processes is the hot shit right now.

I have a lot of respect for Steve Blank, but my heuristic by now is to ignore any breathless posts that state “teams are doing X with AI, if you are not doing the same you’re behind”.

The much more useful posts are “my team and I are doing X with AI”. Of course, the challenge there is that the ones who are truly getting a competitive edge through AI are usually going to be too busy building to blog about it.

I'm not, but this is not a great introduction. It's handwavy and makes the assumption that AI dev tools are much farther along than they are. I have seen this a lot lately; the farther up the management chain and farther away from putting hands on code, the more confident people seem to be in the power of AI tools.

For big complex real world problems, and big complex real worlde codebases, the AIs are helpful but not yet earth shattering. And that helpfulness seems to have plateaued as of late.

I am extremely skeptical of posts like this.

Another option I like: organic.

GPT-4o is 23 months old.

One year ago, the models were only slightly less competent than today. There were models writing entire apps 3 years ago. Competent function writing is basically a given on all models since GPT3.

Much of the progress in the past year has been around the harnesses, MCPs, and skills. The models themselves are not getting better exponentially, if anything the progress is slowing down significantly since the 2023-2024 releases.

One year before 1969 we had never been to the moon. In the 70s credible scientists and physicists predicted that large martian colonies would exist before the year 2000.

If a metric goes from 0 to 2 it doesn't mean it's on a long-lived exponential trajectory.

> One year ago models could barely write a working function.

This is a false claim.

Claude Code was released over a year ago.

Models have improved a lot recently, but if you think 12 months ago they could barely write a working function you are mistaken.

Sigmoids look a lot like exponentials early on.

We can’t say for sure yet which trajectory we are on.

This comment is getting punished for the incorrect timeline (I would know, I've been harping on about AI getting good at coding for ~2 years now!) but I do think it is directionally correct. Just over 3 years ago, (publicly available) AI could not write code at all. Today it can write whole modules and project scaffoldings and even entire apps, not to mention all the other stuff agents can do today. Considering I didn't think I'd see this kind of stuff in my lifetime, this is a blink of an eye.

Even if a lot of the improvements we see today are due to things outside the models themselves -- tools, harnesses, agents, skills, availability of compute, better understanding of how to use AI, etc. -- things are changing very quickly overall. It would be a mistake to just focus on one or two things, like models or benchmarks, and ignore everything else that is changing in the ecosystem.

Seems extremely disingenuous to say that one year ago models could barely write a working function. In fact, there were plenty capable of writing a working function with the right context fed in, exactly as today.