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Comment by iammjm | Hacker News Reader

“ According to Mr Yotzov’s study, nine in ten executives report no impact of ai on their firm’s productivity over the past three years.”

Brutal stuff.

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AI will probably end up reducing lots of firms productivity because costs to defend against cyber attack is gonna skyrocket. Even for tech firms writing more code (the only thing AI is really good at so far) doesn't scale linearly with profits

Particularly when you can't use Fable 5 to secure your software, you don't have access to Mythos 5, and you got your access to GLM-5.2 and other Chinese and/or open models capable of doing so taken away.

Why would it obviously give attackers the upper hand? If we accept the premise that AI helps find vulns more efficiently, shouldn’t this be at least equally helpful in securing against attacks and make that increasingly efficient?

Maybe people believe that the process complexity is not symetric. I don't blame them. Building an house is more complex than destroying it.

It's still an additional cost that doesn't drive revenue.

everything you build potentially increases the surface area of attack. If the benefit of building it is zero or negative then all you've done is increase your risk profile.

It won't do that for free, companies (and open source maintainers) will have to pay for the tokens used finding vulnerabilities in their own software. That increases the cost of releasing the same product/feature/amount of code vs what it was before.

Good point. Within the next few years black hat open models will be able to do a lot of damage for very little cost.

It could easily turn into a Red Queen situation where companies that don't spend hard on security are going to face huge potential losses, and both sides will be scrambling constantly to get an advantage.

Here’s what I’ve seen: it can make skilled people more productive by acting like a “mech suit for your brain.”

For unskilled people, it creates a false sense of productivity, but if the user of the tech does not understand what they are doing they can’t apply the tool productively or judge its output or deal with the things beyond its ability.

So like all other tools before it: it works best in the hands of a skilled user.

Just like in all other fields.

There is no tool that makes an unskilled user skilled, but there are many that can amplify or extend the capability of skill.

> it can make skilled people more productive by acting like a “mech suit for your brain.”

If mech suits existed and people actually lived in them they would either get super fat or super skinny. In either case they'd lose all muscle mass.

All technologies are a double edged sword like this, simultaneously strengthening and weakening.

https://newlearningonline.com/literacies/chapter-1/socrates-...

Humans are tool makers and this is what happens when you make tools.

Problem is AI weakening our brain, arguably the only thing that distinguish us from animals. As if decreasing brain size of modern humans was not enough.

A lot of people are slowly realizing that code generation was not the bottleneck in their internal development process, and waving a magic wand to make that go faster doesn't actually lead to more revenue.

Next you'll be telling me that using number of PRs deployed in a week as a KPI is a bad idea.

One of the weirdest things about the whole AI boom is the mass formation psychosis where everyone started acting like lines of code written was an important metric for software development productivity.

We've known for decades that more lines of code isn't good. Bill Gates was joking about it in the OS/2 days!

We've known for decades that the actual creation of the code is the smallest part of an actual software developer's job.

We've known for decades that "every line of code is a business decision" that has to be written with an understanding of the underlying goal, and the hardest part of training up software engineers is making them understand that.

We threw all that out because "the chatbox writes code really fast, software is a solved problem!"

This is a rewriting of history that I don't really like. Back before LLMs and before that again, it was very hard to write code. That's why so many people asked questions and figuring out how to write a piece of code was a big part of the value add of a programmer. It separated the ones who knew their stuff from the ones who didn't. Yeah sure software design was also important but it was less important than the actual writing of the code, which is why a lot of old code had bad designs. It took decades to figure that stuff out.

> Back before LLMs and before that again, it was very hard to write code. That's why so many people asked questions and figuring out how to write a piece of code was a big part of the value add

This may be true if you work in unfamiliar area or on frontier of programming. But this is not were most of programmer jobs are. That writing code itself is hard is a view of non-programmer. Programmers know that past some threshold of familiarity designing and weighting decisions becomes central problem.

Sure it may be that way now. But if you go back 20 years to for example video game programming, the hard part was figuring out how to solve the problem, not spend weeks or months trying to figure out the abstract design of the code itself. 10-20 years ago a lot of the value of programming was figuring out how to do something, which is by its nature about writing the code, I would argue.

Edit: Also, even outside of video games, I don't remember people being obsessed about software design 10-20 years ago the way they are now. You had some design principles of course and there were obviously bad or redundant ways of doing things, but it's on a whole other level the past 5 years at least from what I remember.

You're very definitely misremembering.

10 years ago was the peak of DDD and Context Mapping, 20 years ago was when The Wiki ruled and everyone was obsessed with design patterns, 30 years ago was UML and Object Mapping... and on, and on, and on.

Fashions change, but one of the few constants in our industry is that everyone has always been obsessed with software design.

Most people will pocket AI gains for themselves.

So from the top it doesn't look like much has changed, whereas workers are trying to offload as much work as they can onto their claude subscription.

People in desperation to min/max work per unit dollar, will leverage AI to free themselves from as much work as possible, while still claiming credit for the work.

So as the labs start ratcheting up the price, people will pay more and more to keep their "secret" assistant.

Wouldn't that explanation predict that lower ranking employees would be more impressed with AI than higher ranking ones? Because what we actually observe [0] tends to be the opposite. Executives are the most bullish on AI, followed by Managers, with employees having the least adoption.

[0] https://businesschief.com/news/why-are-executives-using-ai-m...

Those studies are of workers using company provided AI.

They call out the MIT study in the article, which found very low adoption of corporate AI initiatives.

However if you read that study, they found that almost every worker uses AI heavily, but they just use their personal AI account.

Again, people are using their own AI to benefit themselves, it makes the most sense. Using company AI shows that the AI is doing xyz, and maybe perhaps you aren't that valuable. Using your own AI makes it look like you are doing xyz, and you are the valuable one.

The same source (https://www.nber.org/papers/w34836) also says:

> ...these same executives predict sizable effects over the next 3 years, predicting that AI will boost productivity at their firms by an average of 1.4%, raise output 0.8%, and cut employment 0.7%.

So clearly they're going to spend more on AI.

More concerningly:

> In contrast, employees anticipate that AI will raise employment 0.5% at their firms in the next 3 years, highlighting an expectations gap between employers and employees.

I think what's happened, so far at least, is productivity has been moved around.

Anyone doing consulting has seen it, where now the "I vibe coded this last week" competition is way more intense. Basically the people with the problems now have a chance to spin up something that looks like the solution they want, they then get in trouble and need someone to sort it out.

LLMs _are_ great tools for software development but if you haven't noticed their near complete inability to reason about why what they're doing might work you haven't been trying hard enough.

I guess there is an inverse effect to bubbles (things go linearly up and then abruptly down), where nothing happens for a long time until all things happen at once.

If programmers play it right that'll be us working less and delivering the same results. Who said everything has to benefit the capital class?

What's the play for software engineers that lands them in a better place but doesn't leave money on the table for the capitalists?

Speaking as a programmer, the net effect so far is that we work more and deliver far more results and get paid the same or less.

And I run my own firm... but I have to compete against a global market of other programmers using similar tools who are also trying to compete on price, and then there are hordes of new entrants who can vibe-code things that superficially look like they'll meet a customer's needs and are better at marketing than I am, so my area of business gets constrained to just customers who need help after the problems with the vibe-coded solution appear, the vendor who made it is long gone, and they already spent an inordinate amount of money on the vibe-coded solution since they thought it was complete and it looked pretty good.

Most of my past roles had very simple code at the important deliverable level, on bizarre and arcane platforms that would have little context for frontier LLMs. Most the creativity and complexity was in tooling for research/development/testing.

Agentic coding could help a lot for the latter, not necessarily the former.

Programmer work product (typically) benefits the capital class, directly via product engineering or indirectly via platform engineering.

If the aspiration of a 10x or more programmer is enabled through AI then the capital class win, in the typical case. Software eats the world.

I am surprised they don't record a decrease, maybe this year could be better?

Not really. Most things take more time to transform than people think, even if capabilities exist. Which is not to say that they do exist, but rather, independent of whether they exist, that this is not very surprising.

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