> when prices go down instead of up. It is generally considered harmful: both because it is usually brought on by something really bad (like a severe economic contraction)

Or, you know, technological improvements that increase efficiency of production, or bountiful harvests, or generally anything else that suddenly expands the supply at the current price level across the economy. Thankfully, we have mechanisms in place that keep the prices inflating even when those unlikely events happen.

Does anyone else agree with this the premise of this article? Is it sensible to put off building things now because it will get even cheaper and faster later?

Maybe the time value of time is only increasing as we go.

Technology has always been deflationary. But you don't put off buying a computer because it will be cheaper next year. Nobody seems to be putting off buying GPUs despite scary depreciation and a blistering pace of new product introductions that are ever cheaper faster and better.

I'm still ahead of a company starting later:

I have the legal structure, i know my collegues, i have potentially employees and more capacity.

The problem is not that a startup is starting after you but you do not give yourself time to keep an eye on AI and not leveraging it when its helpful.

We leverage AI and ML progress constantly and keep an eye on advances. Segment Anything? Yepp we use it. Claude? Yes Sir!

The impulse to make the comparison makes sense. The reality is a bit different, probably leans in the right direction but is buffered by learning. I’ll explain. There is no upside to delaying purchasing if you think things will get less expensive. There is however upside in building today even if you have to rebuild tomorrow, and that upside is in learning the problem space. Specifically, what is likely to be trivialized and what truly requires domain knowledge. Horizontal apps? Little domain knowledge to encode. Vertical app? More domain knowledge to encode. Separately, there are more ways to differentiate than distribution alone, see verifier’s law. Problems that are challenging to verify are challenging for AI to trivialize.

"One of the main problems is that if people expect prices to keep going down, they'll delay purchases and save more, because they expect that they'll be able to get the stuff for less later."

That is why we are all waiting to buy our first personal computers and our first cell phones.

Economists have managed to be ludicrous for a very long time and yet we still trust them.

There’s a fun version of this in futurist space travel speculation.

Let’s say you have the a fusion rocket and can hit 5% the speed of light. You want to migrate to the stars for some reason.

So do you build a generational ship now, which is possible, or… do you wait?

Because if you build it now someone with a much better drive may just fly right past you at 20% the speed of light.

In this one the answer is to plot it out under the assumption there is no totally undiscovered major physics that would allow, say, FTL, and plot the curves for advancement against that.

So can we do this with software? We have the progress of hardware, which is somewhat deterministic, and we know something about the progress of software from stats we can make via GitHub.

The software equivalent of someone discovering some “fantasy” physics and building a warp drive would be runaway self-improving AGI/ASI. I’d argue this is impossible for information theoretical reasons, but what if I’m wrong?

What I don’t understand is that how is this automatically good for startups?

Say I have a startup that vibe codes “AI for real estate”. What about customer acquisition?

On the other hand, if I’m Zillow, why can’t I just throw a developer on the same feature and automatically have a customer base for it?

If you look at most of the YC funded startups these days, they are just prompt engineers with no go to market strategy and some don’t even have any technical people and are looking for “technical cofounders” that they can underpay with a promise of equity that will statistically be meaningless.

>> First, models getting better makes AI-based applications easier to build, because they can be simpler.

Don't conflate easy with simple. I'd argue they are actually easier and far more complex.

In the end, the article says:

> writing functioning application code has grown easier thanks to AI.

> It's getting easier and easier for startups to do stuff.

> Another answer might be to use the fact that software is becoming free and disposable to your advantage.

For me, the logical conclusion here is: don't build a software startup!

The models themselves represent the biggest deflation cases I've ever seem.

The charged cost of a frontier model is ~200x lower than 2 years ago, and the ones we are using now are much better - although measuring that and how much is challenging. Building a "better than GPT-4" model is also vastly cheaper than building GPT-4 was... perhaps 1/100th?

> Desktop app.... though Electron and Tauri have made it easier

Ugh. I don't like that kind of 'desktop' apps. Huge bloat with a blip of actual app.

"But building the same functionality has undoubtedly become simpler."

I disagree with this statement. It has become simpler, provided you don't care about it actually being correct, and you don't care about whether you really have tests that test what you think you asked for, you don't care about security, and other things.

Building the same thing involves doing the things that LLMs have proved time and again that they cannot do. But instead of writing it properly in the first place, you now need to look for the needle in the haystack that is the subtle bug that invariable get inserted by llms every single time I tried to use them. Which requires you to deeply understand the code anyway. Which you would have gotten automatically (and easier) if you were the one writing the code in the first place. developing the same thing at the same level of quality is harder with an LLM.

And the "table stakes" stuff is exactly the thing I would not trust an LLM with for sure, because the risk of getting it wrong could potentially be fatal (to the company, not the dev. Depends on his boss' temperament) with those.

With re. to the first two paragraphs, it's crazy how someone can be so massively brainwashed.

Good explanation for why hiring stopped. If AI is improving rapidly why hire engineers now that you might not need in 6-12 months?

Infact, the opposite is true. There is tech inflation. Coding will get easier, but that doesn't mean tech is getting easier. It will only get more complex and will drive more segregation.

Tech is dividing the society and driving a wedge deeper. There is a huge population that are being thrown wayside by the high-speed tech highway. Which means the tech is getting more and more unreachable.

AI assistants are only going to make this worse, by removing the direct touch between users and the tech. Tech becomes just unmanageable for average person.

Just like how you were able to do all repairs for your bike, as a kid. But you can't do the same for your car now. Tech never gets easier or reachable.

What will become apparent is that when coding costs go to 0, support and robustness costs will be the new "engineering" disciple. Which is in reality how things work already. It is why you can have open source code and companies built on providing enterprise support for that code to companies.

If you want to build a successful AI company, assume the product part is easy. Build the support network: guarantee uptime, fast responses, direct human support. These are the shovels desperately needed during the AI gold rush.

Did google search and stackoverlow and internet deflate programming ?

Comparing with reading books you might have or need to order, read, or get from the library, you bet.

There still are some interesting problems to tackle. Maybe more than before. So who knows.

Does anyone really read any of this stuff anyway?

One indicator that I'm watching for deflation is the concept that money is not accepted. Things like social media followers or likes are used as substitutes. People can't buy things like concert tickets or latest merchandise because all the bots have already bought them. Businesses will claim they can't provide a custom service. In tech, this would be spending a modest amount of money on online ads when in reality an ad campaign can only be "bought" with debt.

I think analysis around inflation, deflation, and consumer prices are valid but they are part of an understanding from economies of 100 years ago. Money loses value when you can't do anything with it. Tech and AI runs on debt, and an extraordinary amount of it. Is that really money? I don't think so.

Deflation may suffer from Goodhart's law. Because we've repurposed all of available human resources for mitigating against it, the variables we used to measure it cease to become useful. Our central measure for the economy are things like the stock market and the unemployment rate which have prevalent and valid criticisms that policy makers ignore. They truly don't indicate what's occurring on main street and I'm afraid that we will be in a deflationary spiral without knowing it.

I disagreed with some points and agreed with others based on my experience and the data I have available, but the last few sentences really weakened the overall point.

> Giga AI, a company building AI customer support agents, claims to have sworn off the "forward deployed engineer" model of custom software favored by many other successful startups, in favor of software that customizes itself—only possible because of coding agents.

Giga AI is not a publicly traded company and they have zero legal liability or possible downside for lying, and massive upside for lying. They also don't have real customers and are not in positive revenue. The trend is that everyone who has said this was lying.

When there's tangible evidence of this, I think it will be an important part of the discussion. Until then, saying "claims" and "but I don't really know" but then paraphrasing their press release without analysis is about as sophisticated and as honest as tweeting "people are saying."

The author should take their own advice and wait six months when these claims will be easier to substantiate and support the analysis far more strongly.

Imagine, people aren't spending already because they have no money, things keeps getting pricier and pricier, people buy less and less trying to put aside in case something happens like an accident. And "higher people" still thinks "lower price bad". I sure hope the wall hits so hard countries fall.

As someone building AI infrastructure, this deflation idea shows up very differently for us. Application founders can afford to say they will wait for the next model, but infrastructure founders cannot. The value we create is not in shipping features faster. It is in absorbing the volatility that sits underneath the entire AI stack.

Models keep getting cheaper and more capable every few months. However, the underlying compute economics do not deflate at the same rate. GPU provisioning, inference orchestration, bandwidth constraints, latency guarantees, regulatory requirements, and failure handling do not become magically simple because a new model improved its reasoning. In reality, each improvement on the model side increases pressure on the infrastructure side. Bigger context windows, heavier memory footprints, more parallel requests, and more complex agentic workflows all increase the operational burden.

For infrastructure teams, waiting does not help. The surface area of what needs to be built only grows. You cannot delay autoscaling, observability, scheduling, routing, or privacy guarantees. Applications will always demand more from the infrastructure, and they will expect it to feel like a commodity.

My view is that technical deflation applies much more to application startups than to infrastructure startups. App founders can benefit from waiting. Infra founders have to build now because every model improvement instantly becomes a new expectation that the infra must support. The baseline keeps rising.

The real moat in the next era is not the speed of feature development. It is the ability of your infrastructure to absorb the increasing chaos of more capable models while keeping the experience simple and predictable for the user

Reminds me of the https://en.wikipedia.org/wiki/Osborne_effect