**Stop Saying Your Work Is Too Complicated: Office Workers Are Running Out of Time to Adapt**

Recently, in a conversation, I said something rather blunt:

“Many people spend their whole lives as workhorses. Give them AI, and all they can think to do is ask it to teach them how to be workhorses.”

That remark came from a frustration I keep encountering at work.

I introduce people to Codex and explain that they can hand work over to an agent. They often respond with complete conviction: “My work is exceptionally complicated. AI can’t do it.”

They have not seriously tried it, supplied the necessary material, explained the result they want, or even found out which tools it can use. Yet they have already reached their conclusion.

Explain an approach, and they say their business is different. Suggest a concrete solution, and they introduce another condition they never mentioned before. By the end, the discussion feels little different from arguing with an internet troll who has no intention of being reasonable. They are defending the same conclusion throughout: “It could never do my work.”

I am losing patience with these discussions. This matters because it concerns what these people will be able to earn a living from next.

When I say “word-processing work,” I mean something much broader than typing documents.

Accountants organizing supporting documents and reconciling accounts; salespeople maintaining customer records and preparing proposals; legal staff reading contracts and comparing clauses; customer service staff organizing issues, looking up policies, and drafting replies; operations staff consolidating data and preparing reports—if the work mainly takes place on a computer, its source information can be digitized, and its final deliverable is also digital, it falls within the scope of this discussion.

“Words” here comes closer to the broad meaning of language in a large language model: text, numbers, tables, rules, code, and the relationships they express.

These things can be read, compared, calculated, transformed, and recombined. Many of the operations you perform by hand every day belong to this category.

At the World Governments Summit in February 2024, Jensen Huang put it strikingly:

> “the programming language is human: everybody in the world is now a programmer”

In other words, human language becomes the programming language, and everyone can become a programmer. [Original: NVIDIA’s official coverage](https://blogs.nvidia.com/blog/world-governments-summit/)

The direction he describes is one in which ordinary people can instruct computers in natural language to do work. It also reinforces my belief that a person’s productive capacity increasingly depends on whether they can express their intent clearly.

Meanwhile, many people still treat “I have lots of steps to perform every day” as evidence that their work cannot be automated.

Being busy does not establish technical complexity.

Building a business system that actually enters service means dealing with requirements, data structures, business rules, interfaces, permissions, exceptions, testing, deployment, and the problems that arise once it is running. It has to work in the real world. Looking reasonable in a document is not enough.

Agents can now complete the entire workflow of coding, testing, and deploying even complex commercial software and websites. Such systems can involve hundreds of thousands of lines of code or more, with numerous modules, business rules, data, interfaces, permissions, and exception handling working together to run reliably in the real world. When agents can already deliver work at that level, I find it hard to accept someone insisting: “My office work is too complicated. Codex can’t do it.”

In my view, in terms of the workload and complexity of execution, engineering a complete commercial system is a hundred times more complex, or more, than the document work performed in all other office roles outside programming. Rating these everyday office tasks at one hundredth of that complexity would already be generous. This is my judgment about the difference in scale. But the question I am asking is very specific:

Can you actually explain where the complexity in your work lies?

Is the information unavailable? Are the rules undecided? Do several sources contradict one another? Is a business decision still outstanding? Or must someone with the appropriate qualifications and authority sign off on the result and take responsibility?

All of these can be discussed, and all of them need to be discussed.

A blanket statement that “my work is complicated” hides every one of these questions. It neither helps the agent complete the task nor proves that the agent cannot complete it.

The difficulty of professional judgment, the severity of the consequences of an error, and the tediousness of operating procedures need to be considered separately.

A contract may carry enormous liabilities. An accounting entry may affect an important decision. That is precisely why someone must judge whether the goal makes sense, the evidence is reliable, and the result meets the standard. It does not automatically establish that every act of reading, organizing, comparing, calculating, and drafting must continue to be performed manually by a person.

The change I really want to emphasize is that people must occupy a different position in the work.

**Make AI the workhorse. Don’t ask AI to teach you how to become a more capable workhorse yourself.**

Many people now use AI by asking a chatbot how to do something, getting a set of instructions, and then opening the software themselves and working through the steps.

They learn more complicated formulas, more proficient operations, and more efficient procedures. Then they continue doing the work by hand.

If the goal is to hand computer-based work over to an agent, this approach starts from the wrong position. The human still performs all the execution. AI merely helps that executor become more proficient. The executor improves, but the work remains tied to that person.

This misses what an agent means entirely.

Wanting to accomplish something does not require you to learn its specific method first and then hand each step you have learned to the agent.

What you need to understand is its fundamental purpose, which requirements must be met, where compromises are acceptable, where further refinement is worthwhile, and what kind of result you ultimately want.

Which tools to use, how to operate them, how many steps to take, and how to correct problems should be questions for the agent to resolve.

It can also actively find, organize, and ask about missing information. You need to understand how source information affects the result and help it obtain the necessary information and access. But you do not have to organize all the material alone first, much less train yourself to become a proficient operator of the task.

Back in November 2023, when discussing agents, Bill Gates wrote:

> “You’ll just tell your agent what you want.”

The instruction is simple: tell the agent the outcome you want. [Original: Bill Gates, “AI is about to completely change how you use computers”](https://www.gatesnotes.com/meet-bill/tech-thinking/reader/ai_agents)

The paragraph containing that sentence discusses a future way to create applications and services: people express their requirements, while agents handle coding, design, and publishing. This was his vision of the future at the time.

That direction is very close to my view. People need to learn to hand a goal to an executor and let the executor work out how to achieve it.

Take a payment system that calls a risk-control engine.

You do not need to know what each of the engine’s hundreds of parameters means or how to configure them. You do not even need to have seen its interface.

You need to use your common-sense understanding of the business to explain the broad risks it currently faces, the problems you want controlled, and the trade-offs you can accept.

For example, you may want stricter treatment of a particular risk while also accounting for the cost of wrongly blocking legitimate transactions. You may accept extra checks at certain points while wanting other parts of the experience to remain as smooth as possible.

Once the necessary business information, execution tools, and outcome feedback are connected, you can have the agent study the rules, configure the engine, organize tests, and then calibrate the configuration against actual execution results.

Were risky transactions missed? Were legitimate transactions blocked? Did the configuration take effect in the actual payment flow? Did its business impact match the original goal?

That feedback should inform the agent’s next round of judgment so it can keep adjusting, verifying, and refining. The whole process becomes a continuing cycle of execution and calibration around the goal.

People do not need to read hundreds of pages of operating manuals, learn every parameter, and then tweak them manually every day. They need to express their judgment clearly when goals change, trade-offs require a decision, or results deviate from what is required.

Specific execution and a great many technical details should progressively be handed over to agents.

That is the change in capabilities I mean.

In the past, a person’s competence was often expressed as: I know how to do this; I know that software; I can run this process faster than someone else.

Now, the more important capability is: I can see why this is worth doing, know which result is actually useful, and judge what is acceptable and what still needs improvement.

In his 2025 essay “Three Observations,” Sam Altman wrote:

> “Correctly deciding what to do and figuring out how to navigate an ever-changing world will have huge value;”

Put plainly, making the right choices about what to do and finding a way forward in a changing world will become immensely valuable. [Original: Sam Altman’s personal blog](https://blog.samaltman.com/three-observations)

My reading is that, as the capacity to execute becomes easier to obtain, the capacity to decide what should be done, why it should be done, and how far it should be taken becomes more important.

This is exactly where many people struggle.

They know the process they perform but cannot explain the goal it ultimately serves. They insist that a step must be done by a person but cannot explain why the step exists. They can submit a document that fits an old template but do not know whether it has actually solved the problem.

As agents take on more execution, these problems will be exposed directly.

Without the manual operations, what can you still contribute to the result?

There is another capability that I think is seriously underestimated: how broadly you understand the possible forms a deliverable can take.

The same problem could be addressed with a report, a spreadsheet, a searchable knowledge base, an interactive page, an automatically executing program, or a continuously running business system.

If all you have seen are documents and spreadsheets, you will keep asking agents to produce more documents and spreadsheets. You may never imagine that the result you actually need is a workflow that updates itself daily and alerts you when it finds an anomaly.

You do not need to know exactly how to program that workflow. But you do need to be able to imagine it, understand why it would be useful, and explain which information it should present, what feedback it should trigger, and which standards it should meet.

The form of the deliverable can, in turn, change how the work is organized.

This is also how I understand the “one-person company”: if a company is designed from the beginning to have agents do a great deal of execution, it may be able to bypass many traditional divisions of labor and layers of coordination between people.

Information gathering, organization, analysis, production, and checking that once had to be assigned to different people can be carried out under a single goal, with agents calling tools, advancing the work, and responding to feedback.

In such an organization, the demand for human coordination devoted to assigning tasks, chasing progress, and forwarding information may fall substantially. Real-world relationships, authorization, and responsibility still need to be handled, but not every deliverable requires assembling a human team first.

A person might not even be good at organizing people in the traditional sense. If they can see the goal clearly, understand the key trade-offs, know what form the result could take, express their intent clearly, and judge the quality of delivery, they may be able to drive work that previously required several people to collaborate.

In “The Gentle Singularity,” Sam Altman also made this prediction about individual productive capacity:

> “the ability for one person to get much more done in 2030 than they could in 2020 will be a striking change”

His point is that the amount one person can accomplish in 2030, compared with 2020, will change dramatically. [Original: Sam Altman’s personal blog](https://blog.samaltman.com/the-gentle-singularity)

That passage is about expanding individual productive capacity. Following that direction, I believe businesses will have an opportunity to redesign how many people they need, what kinds of people they need, and which work can be handed directly to agents.

It also means that general entry-level office skills, along with many intermediate professional skills that depend on proficient execution, may quickly lose the scarcity that once made them valuable.

Writing emails, making spreadsheets, organizing material, and applying templates used to be enough to sustain a position. Knowing a set of rules and producing a certain kind of professional document according to established practice used to command a higher salary.

As these execution tasks become easier for agents to perform, organizations will recalculate: how many people are actually needed to deliver the same result?

Experience may still be valuable, but that value must show up in judgment. Can you notice that the task’s fundamental purpose has been misunderstood? Can you identify a result that looks complete but cannot actually be used? Can you decide which conditions must be satisfied and which allow compromise?

Simply repeating “I’ve been doing this for years” does little to answer those questions.

So I want to give a clear warning to office workers whose main work is processing language in this broad sense:

**You may already be running out of time to change the structure of your capabilities.**

Others have expressed this urgency even more explicitly than I have.

In his January 2026 essay “The Adolescence of Technology,” Anthropic co-founder Dario Amodei reiterated an earlier prediction:

> “AI could displace half of all entry-level white collar jobs in the next 1–5 years”

He was warning that AI could replace half of entry-level white-collar positions within one to five years. [Original: Dario Amodei, “The Adolescence of Technology”](https://darioamodei.com/essay/the-adolescence-of-technology)

He also discussed a question office workers should take seriously: although professions such as finance, consulting, and law require different specialist knowledge, their entry-level work relies on similar general cognitive abilities. If all these fields are disrupted at once, moving into a neighboring profession may not be enough to escape the change.

He acknowledged that businesses take time to adopt new technology. But he immediately added:

> “But diffusion effects merely buy us time.”

The delay in the spread of technology, in other words, only gives us more time. [The same original essay](https://darioamodei.com/essay/the-adolescence-of-technology)

That sentence is well suited to anyone who assumes they can keep working as before simply because their own company has not changed yet.

My judgment is that, over the next year or two, people who still cannot move from proficient execution to defining goals, judging trade-offs, and accepting deliverables will begin to feel real pressure. My timeline is more urgent than Amodei’s range. It is my own judgment.

Change will not happen simultaneously across every industry and company. But you may first notice fewer new positions, work that once needed several people being assigned to one, and someone else delivering in far less time a result that takes you days.

Being displaced may not begin with a dismissal notice. It may begin with your work becoming worth less and less.

The market will not stop comparing delivery speed, quality, and cost because you believe your business is special. Nor will a company forever pay for steps that can already be performed more efficiently simply because you work hard every day.

You can test yourself against a real task right now: Can I explain its fundamental purpose? Can I judge which requirements must be maintained and where compromise is acceptable? Can I imagine a more suitable form of delivery? Can I use actual results to specify what should improve next?

If you cannot answer those questions, you should address that as soon as possible.

Because the next time you confidently declare, “My work is too complicated. AI can’t do it,” someone else may already have handed the goal to an agent, let it execute, verify, and adjust, and received the very result you are still producing by hand.