Remember when your biggest business worry was getting humans to download your app? Those were simpler times. Now you have to worry about getting robots to hire your robot.
Welcome to the strangest job market in history, where artificial intelligence systems are becoming both the workers and the hiring managers, and your SaaS startup is basically running a temp agency for algorithms. If this sounds like science fiction, you haven't been paying attention to your API logs lately.
Something fundamental is shifting beneath the surface of the software industry, and it's happening so quietly that most people are still optimizing for human users while AI systems are busy making those humans irrelevant. We're witnessing the death of the app-centric world and the birth of something far weirder: a marketplace where intelligence itself punches a time clock.
The Great App Store Extinction Event
Let's start with a moment of silence for the traditional SaaS playbook, which served us faithfully from roughly 2008 to last Tuesday.
The old strategy was beautifully simple: build an app, make it sticky enough that humans couldn't escape (like digital flypaper), scale usage until your servers caught fire, then monetize the attention you'd captured. Companies competed on who could create the most addictive user interfaces, the smoothest workflows, and the stickiest engagement loops. Success meant humans logging in, clicking around, and finding your software valuable enough to pay for instead of, say, buying coffee.
This model is about to become as relevant as a Blockbuster Video store in a Netflix world.
Here's why: modern AI systems can coordinate with other AI systems without human supervision. It's like watching middle management get automated, except the middle managers were us and the automation is disturbingly efficient.
Consider the numbers: research shows that monolithic AI systems experience a staggering 320% increase in processing latency when handling intensive AI workloads. Meanwhile, companies implementing composable AI architectures are seeing the opposite which includes dramatic efficiency gains and cost reductions because they can optimize at the component level rather than rebuilding entire systems.
Consider your typical customer support scenario. In the old world, a human support agent would log into multiple dashboards, copy information between systems, and manually escalate issues while slowly losing their will to live. In the new world, an AI agent automatically routes inquiries, pulls relevant information from knowledge bases, generates responses, updates ticket systems, and probably does your taxes too, all without a human opening a single dashboard.
So what exactly are you selling when the buyers are algorithms and the users are algorithms and the only humans left are the ones signing the checks (for now)?
From "There's an App for That" to "There's an API for That"
The emerging AI ecosystem is organized around what researchers call "composable, modular intelligence", which is a fancy way of saying AI systems are becoming Lego blocks for robots. Instead of building software that humans struggle to operate, smart companies are building capabilities that AI systems can snap together like digital kindergarteners.
The shift is dramatic:
Old World (The Human Hamster Wheel):
Build an email marketing platform with 47 different dashboards
Design workflows complex enough to require a PhD to navigate
Market to overwhelmed marketing teams who just want to go home
Revenue from subscriptions that auto-renew while users pray for escape
Success measured by how long you can keep humans trapped in your interface
New World (The Robot Employment Exchange):
Build an email optimization capability that actually works
Expose it through protocols that don't require a user manual
Market to AI orchestration platforms that never sleep
Revenue from API calls that scale with actual value delivery
Success measured by how often AI systems choose your service over your competitors'
This isn't some distant future fantasy. Companies are already implementing AI-first architectures where individual functions (data analysis, content generation, customer interaction) are modular components that can be mixed and matched like ingredients in a very smart smoothie. Enterprise AI trends for 2025 show that the focus is shifting toward specialized language models for agentic AI and the democratization of AI agents through low-code/no-code platforms.
It's like the internet's evolution from static websites to dynamic APIs, except this time the consumers aren't just other software applications - they're intelligent agents that can discover, evaluate, and hire your services without ever asking for a demo.
The Great Protocol Wars (Or: How to Pick the Right Horse in a Race Between Robots)
Just as the early internet needed standards like HTTP and HTML (so we could all argue about the same things), the AI-orchestrated world is developing its own foundational protocols. And boy, are the tech giants having feelings about this.
Model Context Protocol (MCP), Anthropic's entry, is being marketed as "USB-C for AI integrations" which is either brilliant positioning or a terrifying reminder of how many USB cables we've thrown away over the years. Google has counter-punched with their Agent2Agent Protocol (A2A) for cross-vendor agent communication, because apparently even AI systems need to network.
But here's what's really happening: organizations like NIST are actively developing AI standards to promote innovation and cultivate trust, while international bodies like ISO and ITU are working to establish comprehensive AI frameworks. The protocol wars aren't just corporate positioning, they're the foundation of the next computing era.
For startups, this is like being asked to choose sides in a war where the weapons are acronyms and the battlefield is invisible. Early adoption of the right protocol could provide massive leverage like being an early web developer who understood HTTP instead of trying to build everything in Flash. But betting on the wrong standard could leave you building for a platform that achieves the digital equivalent of Betamax.
The smart money is on building protocol-agnostic capabilities while monitoring which standards actually get adopted rather than just announced with great fanfare. Think of it as being Switzerland in a world where the warring nations are tech companies with unlimited marketing budgets and questionable naming conventions.
The Trust Fall Economy
Here's what most analysis of AI orchestration gets hilariously wrong: the biggest barrier isn't technical complexity, it's trust. And teaching machines to trust each other is turning out to be surprisingly similar to teaching humans to trust each other, except with more JSON and fewer trust falls.
When AI systems start making autonomous decisions about which services to use, your beautiful website becomes about as relevant as a billboard in a world where everyone drives with their eyes closed. An AI agent doesn't care about your clever copy, your stunning design, or your founder's inspiring origin story. It evaluates capabilities based on cold, hard performance data like a hiring manager who actually reads resumes instead of just looking at the college names.
This creates competitive dynamics that would make a traditional sales team break out in hives:
Verifiable Performance: Can you prove your capability works better than alternatives? AI systems will test this through automated benchmarks, not by sitting through your PowerPoint about "revolutionary synergies."
Transparent Reliability: What's your uptime? Response time? Error rate? These metrics become your primary differentiators, not whether your logo looks trustworthy.
Integration Friction: How easily can an AI system discover, test, and integrate your capability? The smoother this process, the more likely you are to be chosen like being the restaurant that takes reservations instead of making people wait in line for three hours.
Explainable Outputs: When your capability makes a decision, can the orchestrating AI understand why? This becomes crucial for debugging, which is important because nobody wants to explain to their boss that the AI made a mistake for mysterious reasons. Research in explainable AI is developing techniques like layered prompting to make multi-agent decision-making more transparent.
The companies building this trust infrastructure (the platforms that verify capabilities, benchmark performance, and provide reliability guarantees) may capture enormous value. Think of them as the Yelp for robot services, except the reviews are written by other robots and actually mean something. Best practices for implementing agentic AI emphasize the importance of continuous monitoring, human-in-the-loop controls, and standardized evaluation metrics.
So here we are, standing at the precipice of the weirdest economic transformation since someone decided that pieces of paper could represent gold. We're building a world where software sells itself to other software, where trust is algorithmically verified, and where the most successful companies might be the ones humans never directly interact with.
This isn't just another platform shift, it's the emergence of an economy that operates at machine speed with machine logic, while we're still trying to figure out whether our Zoom camera makes us look professional. The companies that thrive won't be the ones with the best user interfaces or the most compelling demos. They'll be the ones that best understand how to be useful to entities that never get tired, never get distracted, and never make purchasing decisions based on whether the sales rep brought good donuts.
Welcome to the age of useful machines. They're hiring each other now, and frankly, they're probably better at it than we ever were.
This is part one in a series based on an article that originally appeared on darin.co as "The AI Thats Knows How to Use Other AI"