Somewhere in a product meeting right now, a team is celebrating a feature they just shipped.
They bolted AI onto their app. A little sparkle icon in the corner. A chatbot that writes captions. A “smart suggestions” panel nobody asked for.
They high-fived. They updated the homepage to say “Now with AI.”
They think they just secured their future.
They didn’t.
They just built the software equivalent of a point-and-shoot camera with an “Upload to Instagram” button.
Think about how absurd that is for a second.
Picture a 2013 Canon PowerShot. A dedicated device whose entire reason for existing is that it takes better photos than your phone. And now the engineers have added a button on the back that says “Share to Instagram.”
Except the camera has no cell signal. No native app store. No cellular data. No idea who your friends are. So to actually use that button, you’d have to transfer the photo to your phone anyway, which is the exact device that already has Instagram, already has your account, already knows your friends, and already takes photos that are good enough.
The feature isn’t a lifeline. It’s a punchline.
And it’s the single best way I’ve found to explain what is about to happen to a massive swath of the software industry.
The Thing Nobody Wants to Say Out Loud
We are living through the early days of a shift as fundamental as the one that killed the standalone camera. Except this time it’s happening to software, and it’s happening a lot faster.
I’ve been saying a version of this for years. Back in early 2023, when everyone was rushing to launch hyper-specific AI tools, I could see three steps ahead, and what I saw was a graveyard of abandoned single-purpose tools. That graveyard is filling up fast. This is the next wave of the same collapse, and it’s coming for a much bigger category than copywriting apps.
For the last two decades, the software business ran on a simple premise. You find a task people do. You build an app that does that one task really well. You charge a monthly fee. You add features every year to justify the fee. You build a moat out of integrations and habit and switching costs.
That premise is cracking.
It’s cracking because the interface for getting things done is changing from “open the app that does the thing” to “ask the AI to do the thing.” And the AI doesn’t need your app. It needs your app’s API.
That’s the part most people haven’t fully internalized yet. So let me make it concrete.
Your AI Can Already Use the Same Doors Your Apps Use
Here’s the technical reality that makes all of this possible, stripped of jargon.
Modern AI agents can call external tools. This is a real, shipping capability, not a promise. It’s called function calling or tool calling, and it’s the mechanism by which an AI model outputs a structured request to hit an external API and take an action in the real world.
When you ask an AI agent “What’s the weather in Paris?” it doesn’t hallucinate a guess. It recognizes it needs live data, calls a weather API, gets the answer, and hands it back to you. The same mechanism that fetches weather can post to a social network, update a spreadsheet, send an email, or schedule a message. Tool calling is exactly what transforms an AI from a passive text generator into an active agent that interacts with external systems like Salesforce or GitHub.
Then it got standardized.
In late 2024, Anthropic released the Model Context Protocol, an open standard for connecting AI systems to the tools and data they need. The industry nickname for it tells you everything: it’s the “USB-C port for AI applications.” One universal plug. Build the connection once, and any compliant AI client can use it. And the models doing the calling are getting frighteningly good, with the top ones now hitting near-perfect accuracy on multi-turn tool calling benchmarks.
Read that again, but think like a business owner instead of an engineer.
Every social scheduling tool, every email app, every project manager connects to the outside world through APIs. That’s how they do what they do. And now your AI agent connects to those exact same APIs through the exact same doors.
So here’s the uncomfortable question. If your AI can walk through the same door your scheduling app uses, why are you paying a separate subscription to the app that just walks through the door?
Where the Analogy Comes From, and Why It’s Not a Metaphor
I keep coming back to the camera because it isn’t a hypothetical. It’s a graveyard we can visit.
In 2010, camera makers shipped nearly 109 million digital cameras with built-in lenses. By 2023, that number had collapsed to just 1.7 million, a decline of roughly 94 percent according to the Camera and Imaging Products Association. Statista’s chart of the crash is the kind of cliff you don’t recover from. An entire global industry, gone in a little over a decade.
Here’s what people forget about that collapse. The smartphone did not win by taking better photos. For years, a dedicated camera took obviously better pictures than any phone. The camera makers knew it. They said it constantly. They were right.
They lost anyway.
They lost because the phone had one thing the camera could never bolt on: it was already the center of your life. It already had your contacts, your connection, your apps, your identity, and it was already in your pocket. As photographer Chase Jarvis famously put it, the best camera is the one that’s with you.
The camera companies responded exactly the way software companies are responding to AI right now. They panicked and bolted on features. They added WiFi. They added companion apps. They added “instant share.” They tried to become a little bit smartphone.
It didn’t work. Because you can’t out-feature a platform shift. A better camera with a WiFi chip is still a second device you have to carry, charge, and justify. The problem was never the missing feature. The problem was that the entire category had become a feature of something bigger.
That’s the trap. And software just walked into it.
The AI Feature Trap
Let me be clear about what I am and am not saying, because there’s a lazy version of this argument I want to avoid.
I’m not saying every app with AI in it is “just a ChatGPT wrapper.” That dismissal is intellectually lazy, and I’ve argued against it directly. The model is the engine. The app is the car. A great car built around that engine, one with real context, real workflow, and real integration, is a legitimately valuable product. That’s the whole thesis behind how I built Magai.
The problem isn’t AI inside a product. The problem is the rigid, single-task product bolting on AI it was never built to hold. And here’s why that specific move is doomed, not just risky.
When a single-purpose app adds an AI feature, that feature can only ever know about that one app. The AI inside your scheduling tool knows about your scheduling tool. That’s it. It doesn’t know the blog post you drafted this morning. It doesn’t know the email your customer just sent. It doesn’t know your calendar, your brand voice across every channel, or the three other projects you’re juggling.
It’s an AI trapped in a broom closet.
Meanwhile, the AI agent you already use knows all of it. It’s the one you talk to every day. It has your context. It has your voice. It has your history. And it can reach into the scheduling tool’s API and do the scheduling itself. Same engine, richer inputs, and richer inputs produce dramatically better outputs.
So now compare the two experiences.
- Option A: Log into a separate app. Learn its particular AI widget. Feed it context it doesn’t already have. Copy and paste your brand voice into it for the hundredth time. Pay a monthly fee for the privilege.
- Option B: Turn to the AI you already use and trust and say, “Take this and schedule it across my channels for next week.” Done. No new login. No new subscription. No re-explaining who you are.
That’s not a close call. You get the idea, right? The bolt-on loses not because AI is bad, but because a broom-closet AI can never compete with the one that already lives at the center of your work.
Why This Was Mathematically Inevitable
There’s a deeper reason this keeps happening, and it isn’t about marketing or funding or timing. It’s a law.
In 1956, a British cyberneticist named W. Ross Ashby published the Law of Requisite Variety. The premise is simple: in any system, the element with the most flexibility controls the system. Not the strongest. Not the best funded. The most flexible.
The inverse is the part that should sting if you’re building a one-trick app. The most rigid element in any system loses control of its outcome. Not eventually. By definition. The moment the environment throws more variety at a system than it can respond to, that system loses.
A social scheduling tool has one job. A transcription app has one job. A single-purpose product is, by design, a low-variety system. Drop it into an environment where users can now ask one conversational agent to do anything, and the outcome is already written. That’s not a theory. That’s math.
The winners in AI are the high-variety systems: the flexible platforms and agents that bend without breaking. The losers are the rigid ones that can only do the single thing they were built to do. You cannot rebrand your way out of your own architecture.
The Social Media Scheduler That Actually Survives
Let me get specific, because I’ve been saying this one for years and the moment has finally arrived to prove it.
Social media scheduling tools are the clearest example of this entire thesis. Their core job is mechanical: connect to a bunch of social APIs, format a post, and push it out on a timer. That is precisely the kind of task an AI agent absorbs in a single conversation. So the instinct across the category right now is to panic and bolt on AI.
- AI caption writers
- AI hashtag generators
- AI “best time to post” widgets.
Sparkle icons everywhere.
That is the losing move, and it’s losing for two reasons.
First, it destroys their margins. Generative AI features are expensive to run. Every caption, every suggestion, every “regenerate” click burns tokens the company has to pay for.
A scheduling tool bolting on heavy AI is voluntarily attaching a high-variable-cost feature to a low-margin subscription, all to duplicate something the user’s own AI already does.
That math doesn’t get better over time. It gets worse.
Second, and this is the fatal one, it’s the wrong product entirely. The user does not need your AI to write the caption. Their AI already writes it, in their voice, with their full context.
What the user actually needs is a clean, reliable way for that AI to reach every social network at once.
So here’s the play, and it’s the whole point: the scheduling company that wins is the one that builds the single unified API and MCP server for social publishing.
Think about the friction that exists today. If you want your AI agent to publish across platforms, you’d have to wire up a separate API or MCP connection to each individual network, each with its own auth, its own rate limits, its own quirks, its own approval hoops. That’s miserable.
Nobody wants to manage six fragile connections.
Now imagine one company solves that. You connect your AI agent to a single endpoint, and that endpoint fans out to every social network for you in a unified way.
One auth. One reliable interface. One place that handles all the platform-specific mess behind the scenes.
Your agent says “post this everywhere on Thursday at 9am,” and it just works.
That company doesn’t get absorbed by the platform shift. That company becomes infrastructure the platform shift runs on. They stop competing with the user’s AI and start powering it.
It’s the same lesson MCP was built on: the winner isn’t the one with the fanciest chatbot bolted on, it’s the one that becomes the universal plug everyone else connects through.
The scheduling tools racing to bolt AI onto the old model are optimizing for a world that’s ending. The one building the unified publishing layer for agents is building for the world that’s arriving.
I’ll say it plainly: the companies doing it the old way will torch their margins first and become irrelevant inside three to five years.
The saddest part here for me is I’ve actually shared this with a few big players in the social media scheduling game. I know the CEOs for some of these companies, and I’ve pleaded with them to understand.
They have yet to get it.
And it will suck when their whole business crumbles because they didn’t listen. I honestly hope I’m wrong.
“But My App Does It Better”
This is the exact argument the camera companies made. And they were telling the truth. Their product was genuinely better at the one thing it did.
They still lost.
Here’s the principle, and I want you to sit with it because it’s the whole ballgame: Winning at one task doesn’t matter when that task comes free with a tool people already have.
- The dedicated GPS unit was better at navigation than early phone maps. Gone.
- The iPod was better at playing music than an early smartphone. Gone.
- The Flip camera was better at shooting quick video than a 2010 phone. Gone in about two years.
None of them lost a feature war. They lost a context war. The phone didn’t beat them on their turf. It made their turf irrelevant by absorbing it into a device that did a hundred other things you needed more.
Your app being marginally better at scheduling, or transcribing, or summarizing, or formatting, buys you a little time. It does not buy you a future. Not when the AI people already talk to every day can do the same job at 90 percent quality without asking them to open, learn, and pay for anything new.
Convenience and context beat quality.
Every time.
Ask a camera company.
What Actually Survives
I’m not writing a eulogy for all software. I run a software company. I believe in it deeply. So let me be precise about what survives and what doesn’t, because the line matters.
What dies is the one-trick pony. The app whose entire value proposition is performing a single task that an AI agent can now do through an API. If your product can be fully described as “it connects to a service and does one repetitive thing with it,” you are the point-and-shoot camera. The AI agent is the smartphone. The clock is running.
What survives falls into a few categories.
The platforms that own the destination
You don’t schedule a post to nowhere. The social network itself is the destination. Networks, marketplaces, and platforms that own the actual place where the value lives don’t get absorbed. They’re the thing the agents connect to.
The tools that become the unified layer
This is the scheduling insight generalized. Software that turns itself into the clean, reliable, unified API or MCP server for a whole category becomes the rails the agents run on. Instead of competing with the user’s AI, it powers it. That’s not a bolt-on. That’s a business model built for the new world.
The high-variety platforms and agents
The new center of gravity. The flexible layer everything else plugs into. This is the smartphone in the analogy, and it’s the side of Ashby’s Law you want to be on. If you’re building the thing people talk to, the thing that holds their context and voice and history and can adapt to whatever they throw at it, you’re not the one getting absorbed. You’re doing the absorbing.
The dangerous middle is everyone else. The vast field of single-purpose apps that mistook “we do one task” for “we have a business.” They’re about to discover the difference.
The Honest Test
If you build software, here’s the gut check. Ask it plainly, and don’t flinch from the answer.
If a capable AI agent could connect directly to the APIs my product depends on, is there any real reason a user would still open my app instead of just asking their AI to do it?
If the honest answer is “because we do it a little better,” you’re the camera company circa 2012. You have a runway, and you need to use it. Not to add a sparkle icon. To become one of the things that survives: own a destination, become the unified layer for your category, or become the high-variety platform itself.
If the honest answer is “because we hold their data, their relationships, their complex process, and their trust in ways an API call can’t replicate,” then you’re on solid ground. Build there.
And if your entire AI strategy is a chatbot bolted onto a product that does one thing an agent can already do, I’d gently point out that you’ve built an “Upload to Instagram” button on a device with no signal. It looks like progress. It photographs well in a launch announcement. It changes nothing.
The Shift Is Already Here
The interface for software is collapsing into conversation. The apps are becoming the tools the conversation reaches for, not the places people go. The value is migrating from the app that does the task to the agent that orchestrates every task, and to the unified layers that agent plugs into.
This is not a five-year forecast. Tool calling ships today. The open standard for connecting agents to everything already exists and already has broad industry support. The only variable left is how fast users change their habits, and habits change fastest when the new way is dramatically easier. This one is dramatically easier.
The camera companies had a decade of warning and mostly wasted it insisting their pictures were better.
They were.
It didn’t matter.
The riches aren’t in the niches anymore.
They’re in the range.
So here’s the question I’d leave with anyone building, buying, or betting on software right now.
When people can simply ask their AI to do the thing your app does, will they still have a reason to open your app?
Answer that one honestly. Everything else follows from it.
