Gen 5 AI and the Enterprise Harness War | Simon Green<br>Skip to content<br>Theme

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I think we are entering Gen 5 of the assistive AI era.

Forget coding for a minute. Coding AI is the noisy bit. It is easy to demo, easy to benchmark and easy to argue about because code either runs or it does not.

I am more interested in everyone else.

Knowledge workers, operators, home users and normal people with inboxes, calendars, spreadsheets, school forms, customer tickets, booking systems, expenses, policies, PDFs, meetings and all the other small jobs that fill a day.

Most people do not want to become prompt engineers. They just want things done.

For the first few generations we judged AI mostly like a better brain. Is it smarter? Is it faster? Does it know more? Does it make fewer mistakes? How much can it keep in context?

All of that still matters, but I think the more interesting shift is happening around the model. The interface is moving. AI is starting to move from another place you visit into the place where the work happens.

Gen 1: the better answer box

Gen 1 was the GPT-2 and GPT-3 era.

For most people, it barely existed. For the people who did use it, it was basically a better Ask Jeeves. You typed something in and it gave you an answer. Sometimes a surprisingly good one. Sometimes nonsense. Sometimes something weirdly confident and wrong.

It was impressive, but passive.

The user carried the work. The AI answered questions.

Gen 2: the custom knowledge box

Gen 2 was the GPT-4 and Custom GPT era.

This was when every company, team and enthusiast started uploading documents, writing giant prompts and creating little domain experts. Policy bots, HR bots, legal Q&A bots, sales enablement bots and all the “ask our docs” assistants.

A lot of this was useful. Some of it still is. But most of it was Q&A against a blob of knowledge. The AI knew more about your domain, but it still mostly sat there waiting for you to ask the right question.

The user still had to know where to go, what to ask and what to do with the answer.

Gen 3: the assistant with hands

Gen 3 was when tool calling and identity started to matter.

The assistant could access systems. It could look things up. It could take bounded actions on your behalf. This is where customer service agents, Agentforce-style demos, Microsoft Copilot and the first serious personal AI alternatives to Google started to appear.

This was a real step forward. The AI could do things, not just answer things.

But it was still usually trapped inside a workflow, product or vendor-defined surface. It helped you use software. You still spent most of your day inside the software.

Gen 4: the coworker

Gen 4 is where we largely are now.

This is the GPT-5 onwards era, and also the point where Claude got very good. Better tool use, bigger context, more reliable long-running work, agents handing work around, fan outs, subtasks, background execution and all the superpower demos.

The Cowork era.

Claude Cowork, Microsoft Copilot Cowork, Codex, Claude Code and the rest of that pattern. The specific product names matter less than the product shape.

You give the AI a larger, messier task and it can make real progress. It can build the deck, analyse the spreadsheet, draft the plan, clean up the files, investigate the issue, pull together the research or turn a vague ask into something that looks like work product.

Microsoft describes Copilot Cowork as a way to turn intent into action across Microsoft 365, with Cowork able to send emails, schedule meetings, create documents, post in Teams and manage a calendar, while asking users to approve actions before they happen. (Microsoft) Anthropic has been pushing a similar product shape with Claude Cowork and plugins, where plugins can bundle skills, connectors and sub-agents into a ready-to-use package. (Anthropic support)

Coding has been the obvious proof point because code is easy to verify. It runs or it does not. Tests pass or they fail. But the same shape is coming for normal work too.

The assistant is starting to feel less like a chat window and more like a junior colleague you can hand things to.

Gen 5: the agent becomes the surface

Gen 5 is the bit I think is emerging now.

The model still matters, obviously. But the model is no longer the whole story. The system around the model matters just as much.

A Gen 5 system needs to connect to tools, remember useful context, run in the background, react to events, work across channels, ask for approval when needed, hand work to other agents and stay out of the way until something actually needs your judgement.

That is the shape of Gen 5.

MCP is part of this because it gives AI applications a standard way to connect to external systems, tools, data and workflows. The newer MCP work around auth, registration, triggers and events is interesting because it moves the pattern from “the user asks and the agent calls a tool” towards “something...