OpenAI DevDay 2025: Apps Kill Assistant Competition

Source: Dev.to

TL;DR

• DevDay 2025 introduced Agent Kit (visual multi‑agent builder) and Apps SDK (native apps inside ChatGPT).
• The hype focuses on Agent Kit vs. automation platforms (Zapier, Make, n8n, Lindy).
• The deeper disruption comes from Apps: a new interaction paradigm that could lock users into ChatGPT.
• For business leaders: understand the distribution vs. flexibility trade‑off and decide whether to adopt an “OpenAI‑first” stack or stay model‑agnostic.

Who Am I?

I’m Dr. Hernani Costa, founder of First AI Movers.

• Newsletter: > 5 000 AI‑focused professionals.
• Consulting: dozens of companies on AI transformation.

I’ve seen platform shifts reshape entire industries – and DevDay 2025 is one of those moments.

The Numbers Tell the Story

These aren’t just vanity stats; they represent the scale at which OpenAI can introduce new interaction paradigms.

OpenAI’s Own Words

“We never meant to build a chatbot; we meant to build a super‑assistant, and we got a little sidetracked.”

This admission signals a strategic pivot: return to the original vision of AI as the ultimate productivity layer.

Four Strategic Categories from DevDay

Agent Kit vs. Existing Automation Platforms

Key Takeaways

1. Model flexibility remains a wedge – enterprises that need to switch foundation models will still favor platforms that support many providers.
2. UX intimidation persists – visual workflow designers are still niche; OpenAI may normalize them, expanding the overall market rather than just redistributing users.
3. Distribution vs. flexibility trade‑off – OpenAI’s massive reach brings lock‑in; companies that prize vendor independence may stay away.

My Take on the Agent‑Builder Landscape

• Historical pattern: Enterprise software tends to fragment into multiple viable solutions serving distinct segments.
• OpenAI’s niche: Capture the “OpenAI‑first” developer community.
• Established platforms: Retain customers who prioritize breadth, flexibility, and multi‑model support.

The Real Game‑Changer: Apps in ChatGPT

Why It Matters

• Deep integration: Unlike previous AI app‑store concepts, the Apps SDK enables “talking to apps” – ChatGPT retains context of what you’re doing inside each app and can act on that context.
• Potential lock‑in: If critical workflows move inside ChatGPT, switching providers becomes painful.

Demo Highlight – Coursera

A user watches an educational video, pauses, and asks ChatGPT:

Can you give me a summary of the last 5 minutes and suggest a practice problem?

ChatGPT, via the Coursera app, pulls the exact video timestamp, generates a concise summary, and surfaces a relevant quiz – all without leaving the ChatGPT UI.

Other Early Partners

• Canva – Generate designs from natural‑language prompts, edit them inline.
• Zillow – Search listings, ask for price‑trend analysis, schedule viewings.
• Spotify – Create playlists based on mood described in conversation.
• Figma – Modify components, get design suggestions, export assets.

Strategic Implications

What Should Leaders Do Now?

1. Audit your critical workflows – Identify which processes could be moved into a ChatGPT‑centric model.
2. Pilot an Apps integration – Start with a low‑risk partner (e.g., internal knowledge base) to evaluate the context‑preserving experience.
3. Maintain model‑agnostic pathways – Keep the ability to switch foundation models or assistants for strategic flexibility.
4. Watch the ecosystem – Monitor which apps get early SDK support; prioritize those that align with your business priorities.

Closing Thought

While the industry debates whether Agent Kit will dethrone Zapier or n8n, the real disruption is likely to come from Apps inside ChatGPT – a paradigm that could make the assistant itself the operating system for digital work.

If your workflow isn’t already running inside ChatGPT, you may soon find yourself playing catch‑up.

Integrated Contextual Power of Apps in ChatGPT

To explain a complex concept, ChatGPT has full context of the video content to provide relevant clarification. This isn’t just convenience – it’s a qualitatively different learning experience that can’t be replicated by switching between Coursera and ChatGPT as separate applications.

The Zillow integration demonstrates similar contextual power: after browsing property listings, users can ask ChatGPT questions that Zillow’s app can’t answer directly—like proximity to dog parks or school‑district quality—while maintaining full context of the specific properties under consideration.

These examples reveal why Apps in ChatGPT represents a different category of integration than traditional app stores or plugin ecosystems. The Model Context Protocol creates bidirectional context sharing that enables genuinely new user experiences rather than just convenient access to existing functionality.

The Context Black Hole Strategy: Why Switching Costs Just Became Massive

Here’s the strategic insight that most analysis has missed: Apps in ChatGPT might function as a context black hole—once users experience seamless AI assistance integrated with their workflows, the switching costs to competing platforms become enormous.

A concrete scenario

1. Learning workflow – Imagine using Coursera’s educational content with ChatGPT’s tutoring capabilities for several weeks.
   • Your learning conversations, progress insights, and personalized explanations all live within the ChatGPT context.
2. Attempting to switch – Now imagine moving to Claude or Gemini for your learning assistant.
   • You lose everything: the contextual understanding of your learning style, your previous questions, your areas of struggle, and the accumulated knowledge of your educational journey.

The switching cost isn’t just about choosing a different AI model—it’s about abandoning weeks or months of personalized context that makes the assistant truly useful.

Extending the dynamic

• Real‑estate search – A ChatGPT‑assisted property hunt accumulates context about your preferences, budget constraints, location priorities, and decision‑making patterns. Switching to a different AI assistant means starting that entire context‑building process from scratch.

From a competitive‑strategy perspective, this represents defensible differentiation through accumulated context rather than superior technology. Even if Anthropic or Google develops better foundational models, the friction of recreating established workflows and context within new platforms creates substantial user retention.

My prediction: Within 12 months we’ll see users who are technically aware that competitor models might perform better on specific tasks, but who remain locked into ChatGPT because their integrated workflows and accumulated context make switching prohibitively costly.

Implications for AI Strategy

For developers

• The question is no longer just “which model is best?” but “which platform provides the most comprehensive development and deployment infrastructure?”
• OpenAI’s Agent Kit, combined with its API ecosystem, creates a compelling solution for teams that don’t require multi‑model flexibility.

For enterprises

• Evaluation criteria expand beyond model performance to include integration depth, context persistence, and workflow continuity.
• Companies need to assess whether OpenAI’s platform advantages outweigh the risks of single‑vendor dependency.
• An AI‑readiness assessment for EU SMEs should now evaluate platform lock‑in risks alongside capability requirements.

For competitors

• The strategic response can’t focus purely on model‑capability improvements.
• Anthropic, Google, and other foundation‑model companies need platform strategies that provide comparable workflow integration and context persistence—or they risk becoming infrastructure providers for OpenAI’s platform.
• Digital‑transformation strategy increasingly requires platform‑level thinking, not just model selection.

Historical Parallel

During the mobile platform wars, having a superior mobile operating system (e.g., Windows Mobile’s features) wasn’t sufficient to compete with iOS and Android’s ecosystem advantages and developer mindshare. A similar dynamic may be emerging in AI platforms.

OpenAI DevDay 2025 represents more than product announcements—it’s the first clear articulation of platform strategy in the post‑ChatGPT era. While the developer community debates whether Agent Kit threatens existing automation platforms, a more significant disruption may come from Apps in ChatGPT, creating new interaction paradigms and incurring switching costs.

Strategic Takeaway for Business Leaders

We’re entering a phase where AI competitive advantage comes not just from model capabilities, but from platform integration depth and accumulated context. Organizations developing AI strategies must assess both immediate functionality and long‑term implications of platform lock‑in. This requires executive AI advisory that goes beyond tool selection to platform‑architecture decisions.

• Developers & founders: The agent‑building space remains competitive and viable, especially for teams that prioritize model flexibility and avoid vendor lock‑in. However, the bar for user experience and integration depth has been raised significantly. Workflow automation design now requires platform‑level thinking.
• Broader industry trend: We’re transitioning from AI as a capability to AI as an interface layer. The companies that win won’t necessarily have the best models—they’ll have the most seamless integration between AI intelligence and daily workflows. This shift demands AI automation consulting that addresses platform strategy.

, not just implementation tactics.

Written by Dr. Hernani Costa and originally published at First AI Movers.

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