SaaS · PiniShv

WordPress Just Let AI Agents Publish to 43% of the Web. Now What?

Sun, 22 Mar 2026 19:00:00 +0200

On March 20, WordPress.com announced that AI agents can now write, edit, and publish posts on any WordPress.com site through MCP. Not just draft. Publish. The agent can also manage comments, update metadata, fix alt text, organize tags, and read the site’s design system to match its visual style.

WordPress powers 43% of all websites. That’s 20 billion pageviews and 409 million unique visitors a month on the hosted platform alone.

They just gave AI agents a publish button to nearly half the web.

What it actually looks like
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You connect your preferred AI client (Claude, ChatGPT, Cursor, or anything MCP-enabled) through wordpress.com/mcp. Then you tell it what you want in natural language. “Write a post about our Q1 product updates, match our brand voice, schedule it for Tuesday.” The agent drafts it, formats it to your site’s design system, and publishes.

Posts default to draft status. All actions get tracked in the Activity Log. User role permissions are enforced: Contributors can draft but not publish. There are guardrails. But the core capability is clear: an AI agent can now autonomously manage a publication pipeline end-to-end.

This builds on MCP support WordPress introduced in October 2025, which was read-only at the time. The jump from “read my site” to “publish to my site” happened in five months.

Meanwhile, YouTube is going the other direction
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In January 2026, YouTube terminated 16 channels with a combined 4.7 billion views and 35 million subscribers. The reason: mass-produced AI content with little to no human involvement. Channels running AI voiceovers over Wikipedia articles. Fake movie trailers. Repetitive content with minor variations pumped out daily.

YouTube’s updated monetization policy is explicit: content with “little to no human involvement” doesn’t get monetized. YouTube CEO Neal Mohan said the platform “welcomes creators using AI tools to enhance storytelling” but draws the line at AI replacing storytelling entirely.

Two of the biggest content platforms on the internet. One just made autonomous AI publishing easier than ever. The other is actively punishing it.

What’s actually happening here
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The divergence makes sense when you look at what each platform values.

WordPress is infrastructure. It doesn’t care what you publish. It cares that you use WordPress to publish it. More content, more sites, more hosting revenue. Opening MCP write access makes the platform more useful for the agentic era. If AI agents are going to generate content at scale, WordPress wants to be the rails.

YouTube is an attention marketplace. It cares deeply about what gets published because its revenue depends on people watching. AI slop that nobody wants to watch degrades the product. YouTube has a direct financial incentive to filter, because advertisers don’t pay for content humans skip.

The difference isn’t philosophical. It’s economic. WordPress sells picks and shovels. YouTube sells eyeballs.

The SaaS connection
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I wrote recently about how the SaaS bargain is breaking. The old model was: rent generic software because custom is too expensive. AI collapsed the cost of custom. Same thing is happening with content.

The old model was: pay for a platform because creating and managing content at scale was hard. WordPress just made it trivially easy. An agent can maintain an entire content pipeline. So what’s the platform’s value when the hard part disappears?

WordPress is betting the value shifts from “helps you create content” to “is where content lives.” Infrastructure, not interface. That’s a defensible position if they’re right.

But the WordPress announcement and the YouTube crackdown point to the same underlying question: when content becomes nearly free to produce, how do you maintain quality? WordPress’s answer is “that’s your problem.” YouTube’s answer is “that’s our problem, and we’ll enforce it.”

For anyone building on either platform, the lesson is the same one from the SaaS article: the value isn’t in the generating anymore. It’s in the judgment, curation, and trust layer on top.

AI can publish to 43% of the web now. The question isn’t whether it will. It’s whether anyone will want to read what it publishes.

Experimenting with AI-driven content workflows? Seeing the quality shift on platforms you use? I’d love to hear what you’re noticing. Find me on X or Telegram.

SaaS Is Dead. We Just Haven't Stopped Paying for It Yet.

Sat, 21 Mar 2026 00:00:00 +0000

In one of my pieces, I argued that most AI companies are just wrappers around someone else’s API.

This is the same story from the other direction.

A lot of SaaS companies are discovering that being the interface for generic business logic isn’t much of a moat when software becomes cheap to generate, cheap to modify, and easy to integrate.

For years, the SaaS bargain was simple. You paid recurring rent because building custom software was slow, expensive, risky, and annoying to maintain. Vendors amortized that complexity across thousands of customers. In return, you accepted a workflow that kind of matched your needs, a UI you learned to tolerate, and “customization” that usually meant some settings, a few webhooks, and a bigger invoice.

That bargain is breaking.

What I Actually Mean When I Say SaaS Is Dead
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I don’t mean software delivered over the internet disappears.

I don’t mean every company rebuilds Netflix, payroll, or payment infrastructure from scratch. And I definitely don’t mean every system of record gets ripped out and replaced by some weekend vibe-coded toy.

What I mean is this: the old SaaS model of selling generic workflows through proprietary interfaces, charging per seat, and treating light customization as a competitive moat is losing its reason to exist. That model only worked because the alternative was painful. Now the alternative is getting cheaper every month.

McKinsey’s analysis of gen AI disruption in software from mid 2024! puts it bluntly: natural-language interfaces can reduce incumbency advantages, vendor switching could potentially double, and $35 billion to $40 billion in software spend could shift toward internal builds. That’s not a fringe prediction. That’s McKinsey telling enterprise buyers the math is changing.

The Builder’s Math Changed
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If you know how to build software, a huge percentage of SaaS products have already started to look weird.

I open a pricing page for some niche productivity tool and my first thought is no longer “should I buy this?” It’s “how long would it take me to build 80% of this?”

And the uncomfortable answer is: probably not very long.

Not because I suddenly became a genius. Because the entire environment changed. I have AI coding tools that can scaffold the boring parts. I have open source projects that already solved half the problem. I have mature infrastructure: hosting, auth, databases, UI kits, workflow engines, and APIs for almost everything. In many cases I don’t need to build from zero. I need to assemble, adapt, and trim.

That’s a completely different economic equation than even two years ago.

GitHub’s 2025 Octoverse reports that AI-related repos now exceed 4.3 million and more than 1.1 million public repos import an LLM SDK. Microsoft Research found a 26% increase in completed tasks across nearly 5,000 developers using AI coding assistants. OpenAI built Codex, Anthropic shipped Claude Code, Cursor keeps expanding what a single developer can do in a sitting, and there are dozens more. This isn’t theoretical anymore. The tooling is here and people are using it.

Here’s the deeper problem for SaaS vendors: I don’t need a perfect replacement. I need something good enough, fast enough, and tailored to me.

A SaaS vendor has to build for a market segment. I only need to build for one user: me. I don’t need feature breadth. I need fit. I don’t need a polished onboarding flow for a million customers. I need the thing to work with my files, my naming, my workflow, and the three annoying edge cases that always break every generic product.

Once software becomes cheap enough to personalize, generic software starts to feel overpriced even when it’s technically “good.”

And if I don’t want to build it myself? There’s a solid chance somebody already built most of it in the open. Projects like Appsmith, ToolJet, Budibase, and Supabase have large communities and active development. Better yet, I can spin up Open WebUI and have my own ChatGPT running locally in minutes. A lot of what used to justify a subscription is now a GitHub search away.

The Enterprise Version
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The organizational version of this matters even more.

For years, companies bought SaaS because custom internal software was expensive, slow, and hard to justify. So they adapted themselves to the product. They changed processes to fit the tool. They renamed stages to match the vendor’s mental model. They built workarounds around missing features. They bought another product to patch the first one. Then an integration layer to connect both. Then an analytics layer because the reporting was bad. Then a consultant because the entire stack became unmanageable.

This is how you end up with “modern software stacks” that are really just expensive collections of compromise.

And Zylo’s data backs this up: SaaS spend averages $4,830 per employee, with an average of $21 million wasted annually on unused licenses. That’s not efficiency. That’s organizational inertia disguised as technology strategy.

AI changes the economics of that compromise.

If I’m running an organization today, I’m not just asking “which SaaS tool should we buy?” I’m asking “which capabilities should remain external, and which workflows should we own?”

Very different question.

Because most organizations don’t actually need generic software. They need software that matches their operating model, their approvals, their language, their exception handling, their reporting, their compliance boundaries, and the weird little pieces of organizational DNA that no horizontal SaaS vendor will ever care about enough to model properly.

That’s where small, sharp internal product-and-engineering teams become strategic. Not giant old-school IT projects. Not six-year ERP fantasies. Small teams focused on building the layers that make the company operate like itself instead of like someone else’s template.

Internal dashboards. Admin surfaces. Approval flows. Cross-system orchestration. Agent layers. Copilots. Task automation. Exception handling. Thin interfaces over existing systems. Tools that reflect how the company actually works.

Gartner expects 90% of enterprise software engineers to use AI code assistants by 2028, and at least 55% of software engineering teams to be building LLM-based features by 2027. Honestly, I think that timeline is already outdated. That report is from mid-2025. As of March 2026, I can’t believe there are companies still letting their developers write code without an AI agent involved. If your engineers aren’t using one, you’re already behind. But that’s a different article, and it’s coming soon.

The smart enterprise move isn’t “replace every system of record tomorrow.” It’s “stop paying premium rent for every workflow that sits on top of those systems.”

Keep the data where it makes sense. Keep the regulated core. Keep the infrastructure you genuinely don’t want to own. But build the working layer closer to the business. Build the layer people actually touch. Build the logic that differentiates how you operate.

Because once the orchestration, interface, and workflow logic can live above multiple tools, the individual tool becomes less important. Gartner predicted 40% of enterprise apps would feature task-specific AI agents by 2026, up from less than 5% in 2025. We’re in 2026 now. Look around. The old UI moat is already thinning.

The Business Model Problem
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A lot of SaaS companies aren’t really selling software. They’re selling the fact that custom software used to be too expensive.

That’s the real moat they had. Not code. Not design. Not even product vision, in many cases. Just the historical cost of the alternative.

If that cost collapses, a lot of “software businesses” are suddenly revealed as workflow rent.

Per-seat pricing becomes harder to defend when one employee with AI assistance can do the work that used to require a whole team buried in dashboards. Generic interfaces become harder to defend when the real interface is language. As Databricks’ CEO put it, the system of record stays but the product becomes “invisible, like plumbing.” Slow product roadmaps become harder to defend when internal teams can ship the exact missing feature themselves. Vendor lock-in becomes harder to defend when the business logic starts moving out of the app and into an orchestration layer the customer controls.

This is the same reason I was skeptical of wrapper companies. When your main value is “I’m the layer in front of something else,” you should be very nervous when that front layer becomes cheap, replaceable, or user-generated.

A surprising amount of SaaS has that exact problem.

What Still Works
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Now, to be fair, the dumbest version of this thesis is also the loudest.

No, not every SaaS company dies. Not everything becomes an internal tool. Most organizations are not going to rebuild entire ERP stacks from scratch because a model can now generate React components and SQL queries.

A lot of SaaS still brings real value. Security. Compliance. Reliability. Operational maturity. Ecosystem depth. Support. Domain expertise. Auditability. Trust.

And some categories remain extremely durable because the hard part was never the UI. The hard part was becoming the system of record. The hard part was surviving regulation. The hard part was handling real edge cases at scale. The hard part was building a network, a marketplace, or a trusted operational layer.

Those products survive. Probably thrive, if they adapt.

Here’s what I think stays strong:

Infrastructure products, where the burden of operating them matters more than a thin layer on top. Nobody’s vibe-coding their own Stripe integration or rolling a custom Datadog.

Systems of record in regulated or mission-critical environments. Healthcare, finance, legal. The compliance overhead alone justifies the vendor relationship.

Platforms with real ecosystems, where switching costs come from partners, integrations, and data gravity, not just habit. Think Salesforce’s AppExchange or Shopify’s app marketplace. The platform is sticky because the ecosystem is.

Products with proprietary data advantages, where the software gets better because thousands of customers use it and the vendor learns things no single company could learn alone.

Software deeply embedded in execution, not just documentation. The tool isn’t where you record what happened. It’s where the work happens.

But generic horizontal workflow SaaS? The kind that charges you forever for helping you move objects between columns, forms, dashboards, and approval states? That category is in real trouble. Because that’s exactly the kind of thing AI plus internal tooling plus open source attacks from every direction at once.

Where This Ends Up
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Here’s the question I think matters more than “is SaaS dead?”

Why are we still training humans to think like software?

Why are people still learning vendor terminology, vendor navigation, vendor permission models, vendor workflow assumptions, vendor reporting limitations, and vendor field structures just to do basic work? Why is the rigid thing in the relationship still the software?

That made sense when software was expensive and humans were adaptable. It makes a lot less sense when software is increasingly the cheaper thing to change.

For twenty years, businesses adapted themselves to software because they had no practical alternative. Now software is becoming adaptable enough to fit the business. And once that becomes the default expectation, a lot of SaaS starts to look less like innovation and more like historical baggage with a monthly invoice.

The future isn’t no software. It’s software that’s cheaper to create, closer to the user, closer to the organization, easier to adapt, and far less entitled to recurring rent.

SaaS isn’t dying as a deployment model. It’s dying as an excuse. And once buyers internalize that, the clock starts ticking.

Disclaimer: This article references specific companies, products, and industry analyses for illustrative and educational purposes. Information about market trends, revenue figures, and business strategies is based on publicly available sources, including McKinsey, Gartner, GitHub, Zylo, and TechCrunch reporting, available at the time of writing. I have not independently verified all claims. The analysis and opinions expressed are my own. I have no financial interest, business relationship, or affiliation with any companies mentioned. This is commentary, not investment, legal, or business advice.

AI Wrapper Companies: Is This Real or Just API Theater?

Tue, 04 Nov 2025 00:00:00 +0000

A company recently raised $8 million for an AI-powered legal assistant, according to industry reporting. Impressive, right? Except when you dig into the product, it’s essentially GPT with some prompt engineering and a document upload interface. The entire “AI” part is OpenAI’s API. The entire “company” is a wrapper.

This isn’t an isolated case. Most of what’s getting funded as “AI companies” right now isn’t AI at all. It’s interfaces to someone else’s AI.

Customer service chatbots that are really just GPT-5 with custom prompts. Content generation tools that are Claude with a nice editor. Analytics platforms that are essentially API calls to various models with dashboards on top. An entire ecosystem of companies whose core technology is “we call someone else’s API and make it look pretty.”

And the scale of this is massive. According to various industry analyses and reports, somewhere between 65% and 92% of AI startups launched in the past two years are primarily wrappers. Not companies training models. Not companies doing AI research. Just companies making it easier to use someone else’s AI.

This raises uncomfortable questions. Is this real innovation or are we watching a bubble inflate in real time? Will these companies exist in three years? And maybe most importantly: how is this different from all the companies that wrapped AWS services in a UI and sold them as products?

What We’re Actually Looking At
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Let me be specific about what these wrappers look like in practice.

The typical pattern: A founder identifies a specific problem (legal document review, fitness coaching, HR candidate screening, whatever). They build an interface where users input their data. Behind the scenes, that data gets formatted into prompts and sent to OpenAI or Anthropic APIs. The response comes back, gets formatted nicely, and gets presented to the user as if the company built the intelligence themselves.

The barriers to entry are astonishingly low now. You can build an MVP in weeks using tools like LangChain or LlamaIndex to orchestrate API calls. You don’t need a research team. You don’t need GPU clusters. You need product intuition and decent engineering to make the wrapper feel seamless.

The economics are attractive too. No R&D costs for model development. No infrastructure for training. Just API costs that scale roughly with usage. A founder can launch, find product market fit, and start generating revenue before a traditional AI company even finishes recruiting their research team.

And it’s working. ProfilePicture.AI reportedly made over $2 million in its first year generating headshots using Stable Diffusion. AI email writers for Shopify stores are doing six figures monthly. Numerous meeting transcription tools, resume builders, and code documentation generators have launched and found paying customers. All wrappers. All making real money.

But here’s the catch. In March 2023, OpenAI reportedly raised API prices by up to 20% for some tiers according to industry reporting. Companies built entirely on GPT suddenly saw their margins compress overnight. They couldn’t negotiate. They couldn’t switch easily (because all their prompts were tuned for GPT). They just had to eat the cost or pass it to customers and risk churn.

These businesses are built on foundations they don’t control. When the model providers decide to compete directly in their vertical, what protection do they have? When a new open source model emerges that’s 80% as good but runs for pennies, how fast does their competitive advantage evaporate?

The Legitimacy Question
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So is this a real business or just timing the hype cycle?

The bear case is straightforward. These aren’t defensible businesses. They have no moats. Anyone can replicate them. Users are starting to notice they’re just paying markup on API calls they could make themselves. Churn rates are brutal (industry reports suggest 60-65% annual churn for some wrapper categories, nearly double typical SaaS benchmarks). When the AI hype settles, these companies disappear.

The critique that stings most: they’re not building anything that lasts. Every improvement to the underlying models happens without them. Every innovation comes from somewhere else. They’re entirely dependent on the goodwill and pricing decisions of their API providers. That’s not a technology company. That’s a reseller with extra steps.

The bull case is more nuanced. Yeah, these are wrappers. So what? Most successful SaaS companies are wrappers around something. The value isn’t in rebuilding infrastructure. The value is in solving specific problems really well.

A marketing agency doesn’t need to train their own models. They need AI that integrates with their CRM, understands their workflow, and produces content in their brand voice. A wrapper that solves that specific problem is valuable even if the underlying intelligence comes from OpenAI.

The key word here is “specific.” Generic wrappers (basic ChatGPT interfaces with minimal customization) are commodity plays with no future. Specific wrappers (AI that solves exact problems in particular verticals) can build real businesses.

I think both arguments have merit. The legitimacy comes down to value addition. If all you’re doing is saving users a trip to ChatGPT, you’re not adding value. If you’re integrating AI into workflows in ways that genuinely solve problems users can’t solve themselves, you’re building something real.

The question each wrapper company needs to answer: could my users get 80% of this value by just using ChatGPT directly? If yes, you’re in trouble.

The AWS Comparison
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This feels familiar because we’ve seen it before. Huge sections of the SaaS economy are wrappers around AWS services.

Take database management tools. Many are just interfaces to RDS and DynamoDB. Take deployment platforms. Many are orchestrating EC2, Lambda, and S3 with nice UIs. Take monitoring tools. Many aggregate CloudWatch data with better visualization.

These companies built billion-dollar businesses by wrapping AWS. So why wouldn’t AI wrappers work the same way?

The similarity is real. In both cases, you’re building on infrastructure you don’t own, adding a layer of abstraction, and charging for the convenience and specialization. The playbook is proven.

But there are critical differences.

AWS is stable. API contracts rarely break. Pricing changes are gradual and predictable. Services have long deprecation cycles. You can build on AWS and expect your foundation to look similar in three years.

AI is chaotic. Models improve dramatically every few months. API features change. Pricing is unpredictable. An update to GPT can break carefully tuned prompts. Open source alternatives appear overnight and undercut commercial APIs. You can build on OpenAI today and have no idea what your foundation looks like next year.

AWS has competition. You can architect for portability between AWS, Azure, and GCP. Lock-in exists but it’s manageable. Multi-cloud strategies work.

AI has concentration. OpenAI and Anthropic dominate. Open source models are catching up but aren’t there yet for many use cases. Switching costs are real because prompts don’t transfer cleanly between models.

The biggest difference: AWS wrappers succeeded because they added orchestration value in a stable environment. AI wrappers need to add value in an environment that’s changing faster than they can adapt.

The survivors will be those who build genuine workflow integration, proprietary data advantages, or multi-model strategies that reduce dependency on any single provider. Just like Snowflake succeeded by being cloud agnostic, AI wrappers might succeed by being model agnostic.

But many won’t make it. The speed of change in AI is just fundamentally different from the speed of change in cloud infrastructure.

Will This Last?
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Here’s the honest assessment: most won’t. But some will.

The ones that won’t last are generic wrappers with no differentiation. If your value proposition is “ChatGPT but easier,” you have maybe 18 months before either OpenAI makes their interface good enough or users figure out they don’t need you. I’ve already seen this happen with early wave ChatGPT wrapper apps that briefly had traction and are now ghost towns.

The ones that might last are building real moats. Take Harvey AI, the legal assistant that reportedly raised over $100 million. According to public information, it’s built on language models they didn’t create, but they’re training on legal-specific data, integrating deeply with law firm workflows, and building features around compliance and confidentiality that generic models don’t handle. The wrapper was the entry point. The moat is everything they built around it.

Or look at what Jasper has done in content marketing, based on publicly available information about their evolution. They reportedly started as a wrapper around GPT-3 for marketing copy, then built brand voice training, integrated with marketing tools, added workflow management for teams, and created templates for specific use cases. They went from “GPT but easier” to “content workflow platform that happens to use AI.” That’s defensible.

The pattern is clear: wrappers work as starting points, not end points. You use the wrapper to validate demand and find product market fit fast. Then you build something that’s hard to replicate. That might mean:

Going deep in a vertical where you understand domain-specific problems better than anyone. It’s not enough to wrap GPT for legal work. You need to understand legal document structure, compliance requirements, confidentiality standards, and how lawyers actually work. That knowledge becomes your moat.

Or it means accumulating proprietary data that makes your AI better than generic alternatives. Every customer interaction trains your system on industry-specific edge cases. Over time, you’re not just calling an API anymore. You’re calling an API plus your accumulated learning.

Or it means integrating so deeply into customer workflows that switching costs become real. When your AI features are embedded in tools teams use every day, tied to their data, and customized to their processes, you’re not competing on model quality anymore. You’re competing on ecosystem integration.

The companies I’m skeptical of are those treating the wrapper as the entire business. They found a prompt that works well. They built a nice interface. They got some initial traction. Now they’re trying to ride that for as long as possible without building anything defensible underneath.

That doesn’t work. Either model providers will compete directly (OpenAI is already doing this in multiple categories), or competitors will replicate your wrapper in days, or customers will figure out they can do it themselves, or API prices will crush your margins.

Sustainability in AI wrappers requires a path from wrapper to platform. If you can’t articulate that path, you’re building a timing play, not a company.

What This Means If You’re Building One
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If you’re building an AI wrapper (or thinking about it), here’s what you need to do in the first 90 days:

Pick a vertical and go deep. Don’t build “AI for content.” Build “AI for technical documentation in regulated industries.” Specificity is your only protection against commodity competition. You need to understand your vertical better than any generalist competitor ever will.

Plan your moat on day one. Before you write your first line of code, answer: what will be hard to replicate 12 months from now? If the answer is “nothing,” don’t build it. Your moat might be proprietary data accumulation, deep integrations, domain expertise, or network effects. But you need to know what it is before you start.

Build for model agnosticism from the start. Don’t tightly couple to GPT-5. Abstract your model layer so you can swap providers, use multiple models for different tasks, or switch to open source alternatives as they mature. The companies that survive will be those that can adapt when (not if) the model landscape shifts.

Track your unit economics religiously. If API costs are 40% of revenue and climbing, you don’t have a business. You have a temporary arbitrage that ends the moment your provider raises prices or your customer realizes they can call the API directly.

Focus on workflow, not features. Don’t just add AI capabilities. Integrate them into how users actually work. The wrapper that saves users three steps becomes essential. The wrapper that adds one AI feature to an existing workflow becomes optional.

Have a 12-month defensibility roadmap. What are you building this quarter that makes you harder to replace? If your answer is “we’re improving the prompts and the UI,” you’re not building defensibility. You’re just iterating on your wrapper.

The hard truth: if your entire value proposition is “I make it easier to use GPT,” you’re one product update away from irrelevance. ChatGPT’s interface gets better every month. Their enterprise features improve. Their API capabilities expand. If ease of use is all you offer, they’ll eat your lunch.

And if you’re evaluating AI companies (as an investor, potential customer, or someone considering joining), look past the AI claims. Ask what they’re actually building. Ask where the intelligence comes from. Ask what happens if OpenAI raises prices by 50%. Ask what their plan is when GPT-5 makes their current approach obsolete.

The companies with good answers to those questions might be worth betting on. The ones without answers are just riding the wave until it breaks.

The Real Question Nobody’s Asking
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Here’s what keeps me up at night about the wrapper economy: we’re watching hundreds of millions in venture capital fund businesses whose core assumption is that the AI layer stays stable and accessible.

But what if it doesn’t?

What happens when OpenAI or Anthropic decide they’d rather own the application layer themselves? They have the models, the distribution, the brand recognition, and increasingly, the understanding of which use cases matter. Every API call is a signal about what customers want. They’re literally watching the entire market test product ideas in real time.

Why would they let wrapper companies keep that value when they could just build it themselves?

We’ve seen this movie before. AWS launched services that competed directly with their biggest customers. Google built features that killed entire categories of apps. Platform providers always move up the stack eventually.

The bet every AI wrapper company is making is that they can build defensible businesses faster than platform providers can build competing features. Maybe some will. But most won’t.

The AI wrapper boom is real. The money is real. The traction is real. But so is the fragility. We’re in the phase where everything works until suddenly it doesn’t.

Treat wrappers as starting points, not destinations. Use them to find product market fit fast, then build something that survives contact with an evolving platform. The companies that get this will thrive. The ones that don’t are just timing the hype cycle.

And if you’re building one right now? You’ve got maybe 12-18 months to figure out what makes you defensible. After that, the platform providers will have learned what works and the easy money will be gone.

The clock is ticking.

Disclaimer: This article mentions specific companies and products as examples for illustrative and educational purposes only. All information, including revenue figures, funding amounts, and business strategies, is based on publicly available sources, industry reports, and media coverage available at the time of writing. I have not independently verified all claims and cannot guarantee their accuracy. The analysis and opinions expressed are my own and do not represent statements of fact about any company’s current operations or performance. I have no financial interest, business relationship, or affiliation with any companies mentioned. This content is commentary and analysis, not investment, legal, or business advice. If any company believes information about them is inaccurate, please contact me and I will review and update as appropriate.

SaaS · PiniShv

WordPress Just Let AI Agents Publish to 43% of the Web. Now What?

What it actually looks like #

Meanwhile, YouTube is going the other direction #

What’s actually happening here #

The SaaS connection #

SaaS Is Dead. We Just Haven't Stopped Paying for It Yet.

What I Actually Mean When I Say SaaS Is Dead #

The Builder’s Math Changed #

The Enterprise Version #

The Business Model Problem #

What Still Works #

Where This Ends Up #