I’m increasingly convinced that the future of phones isn’t “better apps.” It’s embedded AI agents directly integrated into the operating system.

Not cloud-only assistants. Not locked ecosystems. Actual decentralized agents where you choose the model local or remote and the AI interfaces directly with the device itself.

Apps are mostly just UI abstractions built around rigid workflows:
open app → navigate menus → press buttons → perform task.

But language is a far more natural interface.

Imagine instead:
“What song is playing right now?”
The phone listens through the mic and tells you instantly.

No app installs.
No account creation.
No digging through menus.

Just intent → action.

The operating system becomes an agent runtime instead of an app launcher.
The important part is decentralization. If users can choose their own models — local open-source models, private hosted models, or commercial inference providers it creates a competitive inference market instead of a single company controlling intelligence at the OS layer.

The phone stops being a collection of disconnected apps and becomes a context-aware computing device driven by prompts, automation, and agents.

We spent the last 15 years optimizing touch interfaces.
The next era is probably prompt interfaces backed by agentic systems

What StarCraft II pros figured out twenty years ago — and why it matters for anyone managing AI agents today

So what does StarCraft have to do with AI agents?

More than most people realize.

The moment you begin delegating work to AI agents, your role fundamentally changes. You're no longer the worker executing every action manually — you become the commander coordinating systems in parallel.

That shift is important.

You're not the one doing the clicking anymore. You're deciding what gets clicked, by whom, in what order, and toward which objective. Every prompt becomes a strategic command. Every agent becomes a specialized unit on the field. Every review, correction, or redirect becomes a micro-adjustment in the middle of an active battle.

This is where the StarCraft comparison becomes incredibly relevant.

Professional StarCraft players were never simply “fast typists.” Their advantage came from learning how to manage multiple independent systems simultaneously. They learned how to expand, scout, defend, build, attack, and adapt across several fronts at once without needing to manually control every single action every second.

They stopped thinking like a single unit and started thinking like an orchestrator of systems.

That is exactly what AI agents enable.

Historically, a knowledge worker’s output has been limited by a simple constraint: one person can only perform one cognitively demanding task at a time. You could optimize your workflow, learn shortcuts, improve your tools, or work longer hours — but eventually you would hit a hard ceiling defined by human attention and time.

AI agents fundamentally change that equation.

One agent can refactor a codebase while another drafts client communication. A third can monitor infrastructure logs, while a fourth researches competitors, updates documentation, analyzes metrics, or prepares reports. These tasks no longer need to occur sequentially through a single human bottleneck.

Your output stops being tied directly to your own typing speed or execution capacity.

This is why AI agents don't merely increase productivity — they redefine the operational model entirely.

Your effective APM (actions per minute) is no longer measured by how quickly you can execute tasks personally. It becomes the combined throughput of every coordinated system operating under your direction.

You move from being an individual contributor to becoming an orchestrator of parallel intelligence.

And that is the real breakthrough.

The future of high-performance work likely won't belong to the person who can type the fastest or memorize the most syntax. It will belong to the people who can coordinate agents, infrastructure, workflows, context, and decision-making across multiple simultaneous streams of execution.

In other words:

The next generation of professionals won't look like traditional programmers or office workers.

They'll look a lot more like StarCraft players managing an economy at scale.

haha thats the thought in its entirety... hope you enjoyed it

I deployed a rocket chat server, deployed CLI agents on my Mac using tmux. separated them by channel, used SSH config and SSH keys to connect to users on a VPS and scaled out an infrastructure to 250 clients to easily update and build their websites via prompts. I built a control agent to create sub domains and manage DNS and user setup.

Then I developed an App Store of apps I could bolt onto those agents, like email, sms, trading.

I had clients join that had no clue how to build a website, in those cases I updated the context file I injected on boot to say "this client has no idea how to build a website, ensure they use best standards and subtly correct them and educate them "

I sat back and watched the agent hilariously be the proxy I used to be and teach clients how to build good stuff.

The stack I used was simple and also embedded in context on agent boot.

I am now essentially managing context for 500 agents now(model specific) and helping clients unlock the potential of AI.

We stopped selling our product as a service and started saying hey learn how to use an agent by first building a website.

I've found my footing again after 3 years wondering in the remains of what was to understanding the question of what next.

agent control dashboards like these will be necessary for so many industries way beyond websites.

thoughts?