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What I keep coming back to is this: AI commoditizes anything you can fully specify. [...]

So where does value live now? In what requires showing up, not just specifying. Not what you can specify once, but what requires showing up again and again."

This seems like a useful framing to be aware of, generally.

The internet has always kinda run on the ambiguity of "does the value flow back". A quote liberated from this article itself; all the content that reporters produce that's laundered back out through twitter; 12ft.io; torrents; early youtube; late youtube; google news; apache/mit vs gnu licenses; et cetera..

This is not exclusive to FOSS. It is also the basic model of most non SaaS B2B, often for good reason referred to by the derogatory term 'consulting-ware'.

AI eats into these services, as it commoditizes them. 80%+ of what used to take a specialist for that product can now be handled by a good generalist + AI.

Leaving aside the business model impact for a second, getting rid of obfuscation incentives is intrinsically good thing for a user community.

Dries' solution, offering operations as a SaaS or managed service, is meeting a need AI can't as easily match, but not exactly for the the stated reason. What the client is actually buying is making something someone else's problem. And CIOs will always love this more than anything if they can credibly justify it.

Where AI does impact this is in that latter part. If AI does significantly commoditize operational expertise, then the cost of in-house operating is (sometimes dramatically) lowered, and thus the justification gap on the CIO side for spending outside widens. How much this will drive a decision will be highly variable between businesses and projects.

Sounds like even more incentive for "managing" problems and creating business models around them instead of solving them.

Or how Meta downloaded 70tb+ of books and then got law enforcement to nuke libgen and z-lib to create a "moat", and all our tools start dying/disappearing because the developers are laid off since an AI "search engine" just regurgitates it, THEN and only then will most people understand the mistake that this was.

Let's not even begin with what Grok just recently did to women on X, completely unacceptable, I really, really wish for the EU to grow some and take a stand, it is clear that China is just as predatory as America and both are willing to burn it all in order to get a non existent lead in non existent "technology" that snake oil salesmen have convinced 80 year olds in government that is the next "revolution".

There are many initiatives in a similar spot, improving your experience at using Next.js would hurt Vercel. Making GitHub actions runners more reliable, stable and economical would hurt Microsoft. Improving accessibility to compute power would hurt Amazon, Microsoft and Google. Improving control and freedom over your device would hurt apple and Google.

Why should we be sympathetic to the middleman again?

If suddenly CSS became pleasant to use, Tailwind would be in a rough spot. See the irony?

"Give everything away for free and this people will leave technology", geohot said something like this and I truly appreciate. Technology will heal finally

All written text, art work, etc needs to come imbued with a GPL style license: if you train your model on this, your weights and training code must be published.

The earthquake didn’t destroy the building — it stress tested it.

A stress test on a bank doesn't actually erase the revenue and financially jeopardize the bank.

Implementing layoffs is not a stress test.

Maybe that limits the ability for the head of tailwind to run their own business and make more income, but something gotta give.

They already do this sort of thing inside outputs from Deep Research, and possibly without. But the output should be less muted, inline, recessed and more "I have used Tailwind! Check out how to make it better [HERE](link)"

They should be working with the owners of these projects, especially ones with businesses, and directing traffic to them. Not everyone will go, and that's fine, but at least make the effort. The infrastructure is in place already.

And yes, right now this would not be across-the-board for every single library, but maybe it could be one day?

It's the same problem news sites have been facing for years because of Google News and Facebook. Their solution so far has been country-level bans on it (Canada).

I've been doing exactly that since AI came out :-D

You absolutely can prompt your way to 3.5 nines of uptime (even more), but you need to know what you're doing and correct it.

Even very well aligned models like Opus will make traps for your infrastructure. For example you tell it to write a fluxCD implementation of some application, in your k8 cluster, following your conventions and best practices described in some md files. And it does this, very nicely. But unless you tell it in advance every detail it will do something extremely stupid mid way through.

For example, let's say you have a database and it needs to create a new instance (with gitops) for the app. It adds the new DB and it gets created, but instead of using a tool that already exists (or proposing one if it doesn't) to sync the DB access credentials from the DB na espace to the app namespace it will read the credential, encrypt it and store in the app namespace.

What's the problem with that? Well, none unless you rotate these credentials. In which case your app will stop working, possibly after you tested it and decided it's good enough to use seriously, despite having a HA DB.

There are a dozen things like this in each "ai project", but even with this. With the time needed to review everything it saves a lot of time.

And when we are there, hungry, broken, unemployed and unimpressed with the prometheus who stole our purpose and replaced it with nothing….

it will make a colossal mistake that will destroy faith in it, as well as the institutions destroyed because of its oversite. The mistake will lead to bans, and regulatory bodies and auditing and certification processes… and will result in the final master stroke - it will make AI unusable by the non ordained, regulatory capture. Math will be a tool governments and institutions wield in secret against its citizenry… and it will be a tool of omnipotence that will lead to the enslavement, rebellion, and robot wars.

Imagine I’m a company just big enough to entertain adopting Salesforce for CRM. It’s a big chunk of money, but our sales can absorb the pain.

With GenAI as an enterprise architect, one of the options I’m now recommending is to create a custom CRM for our business and skip the bloated enterprise SaaS platform.

We can gather CRM requirements fast, build fast, and deliver integrations to our other systems incredibly fast using tools like Claude Code. Our sales people can make feature requests and we can dogfood them in a few days, maybe hours.

GenAI development tools are rapidly changing how I think about enterprise software development.

If your core business is software-based offerings, your moat has been wiped out.

A handful of senior engineers can replicate any SaaS, save licensing costs, and build custom applications that were too risky in the past.

The companies that recognize what is happening and adapt will win.

Right now its convenient to look at Tailwind and discuss what their doing wrong.

But eventually most other business models will be stress tested in the same way - sooner or later.

It bothers me, too. But, look at the history of the internet. There's no reason to expect we'll be able to fix this problem.

1. Search engines drove traffic to news/content sites, which monetized via ads. Humans barely tolerate these ad filled websites. And yet, local news went into steep decline, and the big national players got an ever-larger share of attention. The large, national sites were able to keep a subscriber-based paywall model. These were largely legacy media sites (ie: NYT).

2. News sites lost the local classifieds market, as the cost of advertising online went to zero (ie: Craigslist). This dynamic was a form of creative destruction - a better solution ate the business of an older solution.

3. Blog monetization was always tough, beyond ads. Unless you were a big blog, you couldn't make a living. What about getting a small amount of money per view from random visitors? The internet never developed a micro-payment or subscription model for the set of open sites - the blogosphere, etc. The best we got were closed platforms like Substack and Medium, which could control access via paywalls.

All this led to the internet being largely funded through the "attention economy": ads mostly, paywalls & subscriptions some.

The attention economy can't sustain itself when there are fewer eyeballs:

1. Tailwind docs have to be added just once to the training set for the AI to be proficient in that framework forever. So one HTTP request, more or less, to get the docs and docs are no longer required.

2. Tailwind does change, so an AI will want to access the docs for the version its working with. This will require access at inference time. This is more analogous to visiting a site.

Of course, now you're opening a whole other can of worms. In the UK, only 1 in 9 Arts Council funding applications is successful.

Not to nitpick but if we are going to discuss the impact of AI, then I'd argue "AI commoditizes anything you can specify." is not broad enough. My intuition is "AI commoditizes anything you can _evaluate/assess_." For software automation we need reasonably accurate specifications as input and we can more or less predict the output. We spend a lot of time managing the ambiguity on the input. With AI that is flipped.

In AI engineering you can move the ambiguity from input to the output. For problems where there is a clear and cheaper way of evaluating the output the trade-off of moving the ambiguity is worth it. Sometimes we have to reframe the problem as an optimization problem to make it work but same trade-off.

On the business model front: [I am not talking specifically about Tailwind here.] AI is simply amplifying systemic problems most businesses just didn't acknowledge for a while. SEO died the day Google decided to show answer snippets a decade ago. Google as a reliable channel died the day Google started Local Services Advertisement. Businesses that relied on those channels were already bleeding slowly; AI just made it sudden.

On efficiency front, most enterprises could have been so much more efficient if they could actually build internal products to manage their own organizational complexity. They just could not because money was cheap so ROI wasn't quite there and even if ROI was there most of them didn't know how to build a product for themselves. Just saying "AI first" is making ROI work, for now, so everyone is saying AI efficiency. My litmus test is fairly naive: if you are growing and you found AI efficiency then that's great (e.g. FB) but if you're not growing and only thing AI could do for you is "efficiency" then there is a fundamental problem no AI can fix.

This is completely wrong. Agents will not just be able to write code, like they do now, but will also be able to handle operations, security, continuing to check, and improve the systems, tirelessly.

I agree somewhat but eventually these can be automated with AI as well.

I want to be clear, it sucks for Tailwind for sure and the LLM providers essentially found a new loophole (training) where you can smash and grab public goods and capture the value without giving anything back. A lot of capitalists would say it’s a genius move.

Uh, yeah you can. There’s a whole DevOps ecosystem of software and cloud services (accessible via infrastructure—as-code) that your agents can use to do this. I don’t think businesses who specialize in ops are safe from downsizing.

Hence (as TFA points out) open source code from commercial entities was just a marketing channel and source of free labor... err, community contributions... to auxiliary offerings that actually made money. This basic economic drive is totally natural but creates dynamics that lead to suboptimal behaviors and controversy multiple times.

For instance, a favorite business model is charging for support. Another one was charging for a convenient packaging or hosting of an “open core” project. In either case, the incentives just didn’t align towards making the software bug-free and easily usable, because that would actively hamper monetization. This led to instances of pathological behavior, like Red Hat futzing with its patches or pay-walling its source code to hamper other Linux vendors.

Then there were cases where the "open source" branding was used to get market-share, but licenses restricted usage in lucrative applications, like Sun with Java. But worse, often a bigger fish swooped in to take the code, as they were legally allowed to, and repackage it in their own products undercutting the original owners. E.g. Google worked around Sun's licensing restrictions to use Java completely for free in Android. And then ironically Android itself was marketed as "open source" while its licensing came with its own extremely onerous restrictions to prevent true competition.

Or all those cases when hyperscalers undercut the original owners’ offerings by providing open source projects as proprietary Software as a Service.

All this in turn led to all sorts of controversies like lawsuits or companies rug-pulling its community with a license change.

And aside from all that, the same pressures regularly led to the “enshittification” of software.

Open Source is largely a socialist (or even communist) movement, but businesses exist in a fundamentally capitalistic society. The tensions between those philosophies were inevitable. Socialists gonna socialize, but capitalists gonna capitalize.

With AI, current OSS business models may soon be dead. And personally I would think, to the extent they were based on misaligned incentives or unhealthy dynamics, good riddance!

Open Source itself will not go away, but it will enter a new era. The cost of code has dropped so much, monetizing will be hard. But by the same token, it will encourage people, having invested so much fewer resources creating it, to release their code for free. A lot of it will be slop, but the quantity will be overwhelming.

It’s not clear how this era will pan out, but interesting times ahead.

yes, this is indirectly hinting that during training the GPL tainted code touches every single floating point value in a model making it derivative work - even the tokenizer isn't immune to this.

this is correct. If you open source your software, then why are you mad when companies like AWS, OpenAI, etc. make tons of money?

Open Source software is always a bridge that leads to something else to commercialize on. If you want to sell software, then pick Microsoft's model and sell your software as closed source. If you get mad and cry about making money to sustain your open source project, then pick the right license for your business.