The shift from apps with AI to AI with apps: Why your next app should live inside Claude

In 2026, the battle isn't whether AI is embedded in your app. It's whether your app can embed in AI.

On January 26, Anthropic and OpenAI simultaneously announced support for MCP Apps, a protocol that lets developers build interactive interfaces that render directly inside Claude and ChatGPT. Within 48 hours, Figma, Asana, Slack, and a dozen other major tools had shipped integrations. Within a week, developers were building everything from 3D visualizations to interactive dashboards to real-time collaboration tools.

This isn't just another API release. It's a platform shift comparable to the iPhone SDK launch in 2008, the Chrome Web Store in 2010, or the Slack App Directory in 2015. And like those moments, the developers who move now will claim territory that becomes nearly impossible to displace later.

Here's why MCP Apps might be the biggest developer opportunity of 2026, and why you need to start building this month, not this quarter.

The distribution advantage you can't ignore

Let's start with the brutal economics of software in 2026:

• Customer acquisition costs have hit all-time highs. The median B2B SaaS company now spends $1.32 to acquire $1 of ARR.
• Users have subscription fatigue. The average knowledge worker already juggles 12+ SaaS tools and actively resists adding more.
• Traditional discovery channels are saturated. Product Hunt launches get lost in noise. SEO takes months. Paid ads burn cash.

Now consider the MCP Apps distribution model:

You're building inside a platform with 100M+ monthly active users who are already paying for Claude Pro or ChatGPT Plus. These aren't cold prospects; they're people who've already pulled out their credit card for AI tooling and are actively looking for ways to get more value from their subscription.

When someone asks Claude "help me visualize this sales data" or ChatGPT "create a project timeline for Q2," your app can appear right there in the conversation. No separate signup flow. No context switching. No convincing them to try yet another tool.

This is the same distribution advantage that made developers rich building:

• Chrome extensions (tap into 3 billion Chrome users)
• Slack apps (access to 20M+ daily active users in 750k+ organizations)
• Shopify apps (instant access to 2M+ merchants)

But MCP Apps might be even better. Unlike platforms where users must actively seek out your extension, MCP Apps surface contextually when users express a need. The AI assistant itself becomes your distribution engine, recommending your tool when it's relevant.

The app directories in Claude and ChatGPT are the new App Store. And right now, they're mostly empty. Categories that will eventually have dozens of competitors currently have zero or one. The land-grab phase is happening right now.

Why "apps in AI" beats "AI in apps"

For the past two years, every software company has been racing to embed AI into its products.

Notion added AI writing.

Figma added AI design.

Salesforce added Einstein GPT.

The pattern was clear: take your existing app, sprinkle some LLM magic, hope users care.

MCP Apps inverts this entirely. Instead of putting AI in every app, it puts every app inside AI.

Why does this inversion matter?

• The context retention problem: When you're deep in a Claude conversation about redesigning your pricing page, and you need to mock up the design, switching to Figma breaks your flow. You lose the conversation context. You have to mentally track what you were trying to accomplish. By the time you've exported the design and switched back, you've forgotten two of the insights Claude surfaced. With MCP Apps, Figma renders inside the conversation. The design happens inline. The context never breaks. This is a fundamental change in how work gets done.
• The trust arbitrage: Users already trust Claude and ChatGPT. They've given these tools access to sensitive information, company data, personal context. When your app shows up as a native integration inside Claude, you inherit that trust. You skip the "Is this safe to connect?" evaluation phase that normally kills conversion.
• The prompt-to-outcome compression: Traditional workflow: "I need to analyze customer churn" → open analytics tool → remember login → find right dashboard → export data → paste into Claude → ask for analysis → get insights. MCP Apps workflow: "Analyze customer churn" → Claude surfaces your analytics app → interactive dashboard appears → Claude analyzes live data → insights generated with drill-down capability, all in one conversation. The time from intent to outcome collapses from minutes to seconds. And once users experience that compression, they don't go back.

Early data from Anthropic's launch partners confirms this. Teams using Asana through MCP Apps report being 40% faster at project updates because they no longer have to context-switch between planning discussions and the project management tool. Slack message drafting happens inline with the strategic conversation about what to communicate, not as a separate task you do afterward.

The first-mover window is open (but closing)

Platform shifts create temporary asymmetries. Right now, there are massive advantages to being early:

• Category ownership: When users think "CRM in Claude," which app comes to mind? Right now, that mental slot is empty. Six months from now, it won't be. The first credible CRM app that ships MCP integration will own that space, and displacing them will require being 10x better, not just 2x better. Consider how this played out with Slack apps. When Slack launched their App Directory, Zoom was one of the first video tools to integrate. Even though Google Meet and Microsoft Teams had larger user bases, "Slack call" became synonymous with "Zoom" because Zoom moved first. They captured the default choice position and held it for years.
• Network effects favor incumbents: As more users adopt your MCP app, Claude and ChatGPT learn the patterns. The models get better at knowing when to surface your tool. User reviews and ratings accumulate. Other developers build integrations that depend on your app being present. Every week you wait, these moats get deeper for whoever moves first.
• Enterprise adoption cycles are long: Fortune 500 companies are rolling out Claude and ChatGPT now. Their IT teams are evaluating which MCP apps to approve for company-wide deployment. Getting into those approved lists early means you ride a multi-year enterprise adoption wave. Missing the window means waiting for the next procurement cycle (often 12-18 months away).
• The OpenAI + Anthropic alignment is rare: These companies are normally fierce competitors. The fact that both are supporting the same MCP Apps standard is unprecedented. It means you can build once and deploy to both platforms. This kind of certainty doesn't last. In 18 months, we might see proprietary extensions and platform fragmentation. Right now, there's a unified standard. Use it.

What this means for different types of builders

The opportunity looks different depending on what kind of developer you are:

For indie hackers

Build single-purpose MCP apps that solve specific workflow problems. The beauty of MCP Apps is that you don't need to build a full-featured SaaS product to capture value. A focused tool that does one thing really well inside Claude can command $10-50/month and serve thousands of users.

Think about apps like:

• A customer research tool that synthesizes interview notes and surfaces insights
• A personal CRM that tracks relationship context across conversations
• A code documentation generator that creates interactive API references
• A meeting prep assistant that pulls relevant context and generates agendas

These are weekend projects that could become $50k/year MRR businesses if you're first in your category. The operational overhead is minimal; you're not managing auth, hosting complex infrastructure, or building a full UI. You're providing a thin layer of value inside a platform that handles everything else.

For startups

If you're building a new product in 2026, the question isn't whether to add MCP support; it's whether to build MCP-native from day one.

Making your core product work inside Claude/ChatGPT can reduce onboarding friction by 10x. Users don't need to learn your interface, remember another login, or figure out where you fit in their workflow. They just ask Claude for what they need, and your tool surfaces at the right moment.

This changes your go-to-market entirely. Instead of spending six months on product-led growth funnels and activation emails, you focus on making your MCP integration excellent. The AI assistant does the onboarding. The conversation context does the activation. You just need to deliver value when called upon.

Several Y Combinator companies from the W26 batch are already building this way. They're treating Claude and ChatGPT as their primary interface, with their standalone web app as secondary. Early metrics suggest they're hitting activation rates 3-4x higher than comparable SaaS companies because users never experience a "cold start."

For enterprise software vendors

If you sell to large organizations, MCP Apps is your path to becoming the default tool in your category.

Enterprise buyers are already asking their vendors: "Does this work with Claude?" and "Can we use this through ChatGPT?" The companies that can say yes (with a demo showing their tool rendering natively inside the AI assistant) win deals.

This is especially true in categories where the enterprise has already standardized on Claude or ChatGPT as its AI platform. If your competitor ships MCP integration before you do, they become the "works seamlessly with our AI stack" option. That's a competitive advantage that's hard to overcome with features alone.

The procurement timing matters. Large enterprises are making their AI platform decisions right now: Q1 and Q2 of 2026. They're choosing between Claude, ChatGPT, and Microsoft Copilot. And they're evaluating which MCP Apps to roll out alongside their chosen platform. If your tool is ready, you're in the initial deployment. If it's not, you're waiting for the next review cycle.

For agencies and consultancies

Most companies want MCP integration but lack the bandwidth to build it themselves. This creates immediate consulting opportunities.

You can offer MCP integration as a service: audit a company's existing tools, build custom MCP Apps for their internal workflows, train their teams on using AI-native interfaces. Many agencies are already pivoting to offer this, charging $25k-$100k per engagement.

The strategic play is to build repeatable MCP components for common use cases (internal knowledge bases, approval workflows, data visualization, document processing) and then customize them for each client. You're not building from scratch every time; you're assembling proven patterns.

The strategic questions to ask now

Before you start building, consider these decisions carefully:

Should you build a standalone app or go MCP-first?

If you're starting fresh, MCP-first makes sense. Let Claude/ChatGPT be your UI layer. You focus on backend logic and data. This dramatically reduces development time and maintenance burden.

If you already have a standalone app, MCP integration should be additive. Your existing users still get value, but you also open a new distribution channel and reduce friction for new users who prefer working within their AI assistant.

Is your tool better as embedded UI or conversational actions?

MCP Apps support both rich interfaces (dashboards, forms, canvases) and pure tool calls (data retrieval, processing, actions). Some tools need visual interfaces. Others work better as conversational commands.

Data visualization? You need embedded UI. Email sending? Conversational actions are fine. Project management? Probably both (users want to see timelines visually but also quick-add tasks conversationally).

Which platform do you target first: Claude or ChatGPT?

The MCP Apps spec is universal, so technically you can support both from day one. But in practice, you'll optimize for one platform's users first. Your target customer should drive the choice:

• Claude users skew more technical and are earlier adopters.
• ChatGPT has a broader consumer reach and larger enterprise deployments.

The good news: once you've built for one platform, adding the second is straightforward. The protocol is shared, and the SDK handles cross-platform compatibility.

How do you balance open-source contribution with competitive moats?

MCP is an open protocol, and Anthropic encourages developers to open-source their MCP servers. This creates a tension: contributing to the ecosystem helps everyone build faster, but giving away your code helps competitors.

The pattern that's emerging: open-source the basic integration, keep the advanced features proprietary. Share the MCP server that connects to your API, but keep the business logic, data models, and premium features closed. You get community contributions and goodwill without giving away your competitive advantage.

The next six months matter

Platform shifts reward speed over perfection. The iPhone SDK launched in March 2008. By July, developers who'd shipped apps (even rough ones) had built sustainable businesses. By 2009, their categories were saturated, and new entrants struggled to gain traction.

We're in March 2008 of the MCP Apps moment. The platform just launched. The tools are new. The ecosystem is forming. And the developers who ship working apps in the next six months will claim positions that compound for years.

You don't need a perfect product. You need a working integration that solves a real problem. Ship it, learn from users, iterate fast. The goal is to be "the X app for Claude" before someone else claims that title.

The canonical example: when the Chrome Web Store launched, LastPass was one of the first password managers to ship an extension. They weren't the most technically sophisticated password manager. They weren't the most secure. They were just first and good enough. That early mover advantage translated into 25 million users and a $220 million acquisition.

The same dynamics are in play with MCP Apps. The developers who move now will build the canonical tools in their categories. They'll own the distribution, accumulate reviews, and ride the growth curve as adoption of Claude and ChatGPT accelerates.

Where to start

Here's your minimal viable action plan:

Week 1: Read the MCP Apps documentation. Clone the ext-apps repository. Pick an example closest to what you want to build. Get it running locally.

Week 2: Strip out the example logic. Connect it to your actual service or build a simple prototype. Don't worry about polish, focus on proving that your core value proposition works inside the MCP Apps model.

Week 3: Test with real users. Get 5-10 people to try your MCP app and watch them use it. You'll immediately see where the experience breaks down and what works better than you expected.

Week 4: Clean up the rough edges. Add error handling. Write basic docs. Submit your app to the Claude and ChatGPT directories.

Four weeks from idea to launched MCP app. That's the timeline you're working with if you want to be early.

The developers who built iOS apps in summer 2008 built lasting businesses (even though the App Store was rough, documentation was sparse, and nobody knew if it would work). They recognized a platform shift and moved fast.

MCP Apps might be even bigger. AI adoption is happening faster than mobile did. The user base is larger. The potential use cases are broader. And the window for first movers is just as wide open.

The platform is live. The documentation exists. The examples are public. The only question is whether you'll build now or watch others claim your category.

Start building.

Resources to get started

• MCP Apps Documentation: Complete technical guide.
• Quickstart Guide: Get your first app running in 30 minutes.
• Example Apps Repository: Working examples you can fork.
• MCP Apps SDK: NPM package for building apps.
• MCP Apps Announcement: Original launch post from Anthropic.