When AI Breaks the Business Model: What Tailwind CSS and Stack Overflow Teach About the Next Wave of Disruption
- Jayant Upadhyaya
- 1 day ago
- 9 min read
AI is changing the future of work fast. Most people see the positive side first: speed, automation, and new tools that make hard tasks easier. But there is another side that is just as important. AI is also breaking old business models, even when the product itself is still widely loved and used.
One of the clearest examples comes from the developer world, where AI tools are already deeply embedded in daily workflows. Watching what developers are talking about is one of the best ways to understand what disruption in knowledge work will look like next. Software engineering is currently the area where AI gives the biggest productivity boost, but it is also the area where disruption is happening first and most visibly.
Two stories show this clearly: Tailwind CSS and Stack Overflow. Both are foundational parts of how developers work. Both were once strongly tied to open web habits. And both now face a reality where AI can deliver the value without sending users to the source.
This is not only a coding story. It is a preview of what will likely happen to many information businesses, tools, and professional services in the coming months and years.
Why Developers Are a Preview of the Future
There is a reason developer stories matter for everyone, even non-technical people.
Developers are early adopters of AI tools. They test new products quickly. They change workflows quickly. When they find something that saves time, it spreads fast.
In 2025, developers experienced a major shift in how work gets done. Many tasks became “vibe-coded,” meaning the work became more about guiding an AI system than writing every line manually.
A key prediction behind this trend is that other knowledge work sectors will go through a similar shift. The process may happen in 2026 for other jobs the way it already happened in 2025 for software engineering.
This also means that developers are experiencing disruption first. They are dealing with changes in identity, workflow, and value. Some developers are asking big questions about what it means to be a developer when AI can write code and build systems faster than any human.
But it is not only an individual problem. It is also a business problem.
When AI changes how people use tools, it can destroy the business model of the companies behind those tools, even if the tools remain popular.
The Tailwind CSS Story: Popularity Up, Revenue Down
Tailwind CSS is an open-source CSS framework. It helps developers build web interfaces faster. It is extremely popular, both among human developers and AI coding tools. Many AI coding assistants generate Tailwind classes by default because it has become a standard way to build modern UIs.
Tailwind’s business model is common for open source:
the core framework is free and open source
there are paid offerings, often called a “plus” tier, that generate revenue
This is a typical setup: the free product grows adoption, and a portion of users convert into paid customers.
What went viral was a small moment that exposed a bigger shift. A user asked on
GitHub for a text-only version of the documentation so AI tools could ingest it more easily. The CEO, Adam Wathan, responded that the feature made sense, but the company was under pressure. He explained that 75% of the engineering team had just lost their jobs due to the impact AI had on the business. He said that time spent on free community features was time not spent trying to keep the company stable and ensure the remaining team could keep getting paid.
That response became a symbol of a new kind of problem: AI did not reduce Tailwind usage. AI reduced Tailwind’s ability to turn usage into revenue.
The key numbers that made people pay attention
documentation traffic was down 40% compared to 2023
revenue was down close to 80%
Tailwind usage was higher than ever
This is what made the story so striking. The framework was not dying. In a sense, it was winning. Developers and AI tools were using it more than ever. But the business behind it was shrinking.
The New Catch-22: AI Drives Usage But Cuts the Funnel
Tailwind’s situation shows a very specific kind of business problem.
Many companies rely on documentation as a funnel. Developers visit docs to learn how to use the tool. While they are there, they discover:
paid products
premium features
templates
UI kits
services
enterprise options
If AI tools can answer developers’ questions directly, developers stop visiting the docs. The AI already “knows” what it needs because it trained on the information, scraped it, or pulled it from public sources.
So the product becomes more used, but the marketing channel becomes weaker.
In Tailwind’s case, the docs were a key way people discovered the paid tier. When doc traffic dropped, fewer users discovered the paid offerings, and revenue collapsed.
This breaks a common assumption in tech: “If usage grows, revenue will follow.” In an AI-heavy world, that is not always true.
Why This Scared People: The “Graveyard of Abandonware” Fear
The Tailwind story sparked strong reactions because it suggests a wider risk.
If open-source projects cannot capture value from their popularity, the maintainers may not be able to keep them alive. That creates a future where important software becomes abandoned.
This is not a small niche fear. Tailwind is not a random side project. It is often described as one of the most used CSS frameworks in the world. It is used in huge numbers of websites and products, and it has become part of modern web infrastructure.
So if even Tailwind struggles to monetize, it raises a bigger question:What happens to the open-source ecosystem when AI agents use everything but nobody pays the builders?
Some people framed Tailwind as a warning sign. Not because Tailwind is weak, but because it is strong and still got hit.
The Response: Sponsorship as a Rescue Mechanism
Even though the situation looked dark, the story did not end there.
The CEO recorded a podcast while walking, talking openly about what the company was facing. People responded to the honesty and transparency. That content gained massive attention, and then donations and sponsorships started coming in.
Several companies announced sponsorship, including:
Google AI Studio
Lovable
Supabase
Vercel
and many others
This looked like a “public good” moment. Tailwind was framed as foundational infrastructure. If the ecosystem depends on it, the ecosystem has a reason to fund it.
This led to a new possibility: maybe the future of open-source infrastructure is not traditional sales funnels, but sponsorship and patronage from the companies that benefit from it.
However, sponsorship does not fully solve the deeper issue. It may help Tailwind now, but it does not guarantee a stable model for every open-source project that faces a similar shock.
Stack Overflow: A Different Example of the Same Pattern
Tailwind is not the only example.
Stack Overflow was one of the most important sites in the developer world for over a decade. It was a place where people asked and answered programming questions. It was like a specialized forum with high-quality expertise.
During the 2010s, Stack Overflow was close to critical infrastructure for software development:
beginners used it for basic questions
experts used it for rare problems
obscure languages had small groups of specialists who answered tough questions
Even before modern chatbots arrived, Stack Overflow seemed to have peaked. But after ChatGPT launched, the decline became undeniable.
Why? Because AI could answer questions instantly.
Instead of searching through old threads, reading multiple replies, and piecing things together, developers could ask an AI system in normal language and get an answer immediately.
This shift removed the main reason many people used the site.
The numbers show how extreme the decline became
At one point, Stack Overflow reportedly had around 300,000 queries per month at its peak. It also had long periods where it stayed above 150,000 to 200,000 queries per month.
Then it collapsed. Eventually, it dropped to a number similar to its earliest days, almost like going back in time to the first month in 2008.
This story is often described as “death by LLM,” meaning large language models removed the need for the platform.
The Bigger Problem: When the Open Web Stops
Producing Knowledge
Stack Overflow raises a different issue than Tailwind.
Tailwind’s problem is value capture. The product is used, but people do not find the paid offering.
Stack Overflow’s problem is demand. People do not need the site as much because AI provides answers.
But there is a deeper effect: if people stop contributing knowledge openly, the internet produces less high-quality structured data.
This creates a strange long-term risk:
AI benefits from human-generated knowledge
AI reduces the incentives to create public knowledge sites
fewer public knowledge sites means less training data in the future
So the same AI systems that reduce the need for Stack Overflow may also reduce the future supply of the kind of knowledge that helped train AI systems in the first place.
Was AI Really the Main Problem for Tailwind?
Not everyone accepted the Tailwind story at face value.
A strong counterargument is that Tailwind’s business model may have already been fragile, and AI simply exposed it faster.
One critique pointed out several issues:
it was not obvious that Tailwind even had paid products
the paid offerings were not front-and-center
some offerings were one-time purchases, not recurring revenue
donations were part of the support model, which can be unstable
AI reduced doc traffic, but strong marketing could potentially redirect attention
This view says: AI did not “kill” Tailwind. AI revealed a weak funnel and weak value capture.
In this argument, the real issue is not governance or disruption. It is that the business did not have enough moat, enough recurring revenue, and enough direct conversion paths.
This also suggests Tailwind can adapt, just like other open-source companies did.
Examples often used:
MongoDB improved monetization through managed services
Elastic shifted toward hosted offerings
GitLab built enterprise tiers
The point is that open source has struggled with monetization for years. AI makes the problem sharper, but the problem is not brand new.
What the Fix Might Look Like
If AI reduces the value of “answering questions” or “selling through docs,” the solution may involve building value that AI cannot easily replace.
Some paths that get mentioned often include:
1) Services and enterprise contracts
Companies pay for:
support
reliability guarantees
compliance
security reviews
onboarding
custom training
long-term maintenance
This is hard for AI to replace because it involves responsibility and human judgment.
2) Deep integrations
Instead of selling a UI kit or a one-time template, products can embed into workflows:
dashboards
deployment pipelines
analytics systems
internal company tools
The deeper the integration, the harder the product is to replace.
3) Recurring value
One-time purchases are harder to scale in a world where AI can generate similar outputs instantly. Recurring products, subscriptions, or continuously improving services may hold up better.
4) Value tied to scale
Some offerings improve as they get used more:
hosted services
community-driven improvements
marketplaces
performance at scale
These create compounding benefits that are not easy to copy with a single AI response.
Why This Is a Preview for Information Businesses Everywhere
Tailwind and Stack Overflow are developer examples, but the underlying pattern applies widely.
If the value of a business is:
answering questions
packaging information
acting as a discovery layer
relying on content traffic for conversions
AI can break that model quickly.
AI can ingest the information and deliver the answer without sending users to the source. That means:
fewer page visits
weaker funnels
less ad revenue
less conversion to paid offerings
less incentive to publish information publicly
This is why the Tailwind story feels bigger than Tailwind. It hints at what might happen to many businesses that sit between the user and the information.
Two Possible Futures for Open-Source “Public Goods”
Tailwind also highlights a special category: infrastructure tools that become public goods.
Once enough companies depend on a project, the project becomes hard to let die. The ecosystem needs it.
That leads to new models.
Future 1: Big company patronage
A major AI company or infrastructure company could:
sponsor projects long term
invest strategically
acquire key open-source projects
fund maintenance as ecosystem infrastructure
This creates stability, but it also creates new power dynamics. It could shape the direction of open source based on sponsor priorities.
Future 2: Automatic contribution systems
Another idea is built into AI coding tools themselves. If an AI agent is using open-source libraries while generating code, the tool could automatically contribute money back to those projects.
For example:
a small percentage of token spend could be redirected
the distribution could be based on usage
payment could happen in the background
This would create an automatic funding loop between AI use and open-source maintenance.
It is not clear if this will happen, but it represents a new way of thinking: if AI tools depend on open source, funding open source should be part of the AI tool economy.
Conclusion: AI Creates Leverage, But Also Creates Breakage
AI is giving developers more leverage than ever. But it is also creating breakage in workflows, culture, and business models.
Tailwind shows how AI can increase usage while destroying revenue funnels.
Stack Overflow shows how AI can remove the need for a site built entirely on open knowledge sharing.
These are not isolated stories. They are early examples of a broader shift:
AI reduces the need for discovery layers
AI weakens content-driven funnels
AI challenges how public knowledge gets funded
AI forces businesses to rebuild value capture
The important lesson is not “AI kills everything.” The lesson is that AI changes what value means and how value gets captured.
The companies and projects that survive will likely be the ones that:
move away from fragile funnels
build recurring value
create deep integrations
offer services and reliability
or become funded as public goods by the ecosystems that depend on them
The developer world is dealing with this first. Other industries are likely next.
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