The Machine Has Been Doing Its Homework
There was a time when artificial intelligence was mostly useful for telling you that your shopping trolley contained “items you may also like”. It would recommend a second pair of socks, a documentary about whales, and occasionally a book you had already bought.
Those days are beginning to look quaint.
OpenAI has released a large collection of mathematical results produced by an internal frontier model. The company says the model resolved, or made substantial progress on, hundreds of difficult problems. Many proofs have been formalised in Lean, a system designed to check whether the logical steps of a proof actually hold together.
This is impressive. It is also slightly alarming, particularly if you are a mathematician who has spent six years wrestling with a problem only to discover that a machine has solved it overnight while consuming enough electricity to toast a small moon.
The results are not automatically beyond question. OpenAI’s own history records withdrawn manuscripts and repaired proofs. Some claims still require independent checking, and formal verification does not necessarily guarantee that the plain-language paper explains the intended result correctly.
In other words, the robot may have found the treasure, but it has handed us a box of receipts written in an extinct language and asked us to confirm that the gold is not painted cereal.
Welcome to the Reviewer Bandwidth Problem
The old problem with AI was that it made mistakes.
The new problem is that it may make interesting mistakes, useful mistakes, correct mistakes, and several hundred pages of apparently significant mathematics before a human expert has finished making a cup of tea.
This creates what might be called the reviewer bandwidth problem. AI can generate output at machine speed, while understanding still moves at the speed of a person reading a document, rubbing their forehead, and quietly wondering whether “Lemma 7” has always been there.
That distinction matters. A proof is not only valuable because it reaches the correct conclusion. Human mathematicians also care about the route: the insight, the structure, the technique, and whether the result teaches anyone how to solve the next problem.
A machine-generated answer may be perfectly valid and still be difficult to understand. It may be a mathematical equivalent of being handed a working spaceship by an alien civilisation with no instructions, no spare parts, and a sticker saying probably don’t press the green button.
The debate is therefore not simply “AI good” versus “AI bad”. It is about what happens when the production of answers outpaces the human capacity to verify, explain, teach, and make sensible use of them.
Don’t Panic: The Machines Have Not Stolen Mathematics
There is an understandable temptation to treat this as the moment humanity has been politely escorted out of the intellectual building.
But mathematics is not a factory whose only product is a pile of solved problems. It is also a way of asking questions, choosing worthwhile problems, building intuition, communicating ideas, and deciding what deserves attention in the first place.
AI may be very good at searching enormous spaces of possibilities, combining techniques, testing variations, and spotting connections that would take a human team years to find. That does not make human understanding obsolete. It makes understanding more valuable.
When a machine produces a result, people still need to ask:
Is the claim correct?
Does the formal proof match the statement being made?
Is the result genuinely new?
What can other researchers learn from it?
Does it reveal a useful method, or merely demonstrate that a very large machine can move very quickly?
The same pattern is already appearing in software development. AI can generate code faster than a person can comfortably read it. It can suggest architectural changes, install dependencies, create tests, inspect logs, and confidently explain why the thing it just broke is actually a feature.
The winning skill is no longer simply producing output. It is directing, checking, constraining, and improving output.
My Day Job: Herding the Artificial Goats
This is where the story becomes rather personal.
My work already sits in the space between human intent and machine execution. I use AI tools across WordPress development, Django maintenance, coding agents, terminal workflows, IDEs, Jira, Confluence, integrations, testing, and automation.
There are agents implementing features, agents reviewing code, development servers running in the background, tests producing reports, and enough terminals to make the command line resemble a control room operated by caffeinated squirrels.
The role is changing. I am spending less time treating AI as a fancy autocomplete box and more time treating it as a junior engineering team with extraordinary stamina, questionable judgement, and no natural fear of deleting the wrong directory.
That means my job increasingly involves:
Defining the mission: turning a vague requirement into a bounded, testable task.
Choosing the right agent: deciding which tool is appropriate for coding, review, research, testing, or documentation.
Managing permissions: keeping production systems, credentials, customer data, and destructive actions behind appropriate barriers.
Reviewing the result: checking the actual diff, tests, logs, assumptions, and side effects rather than accepting a cheerful summary.
Connecting systems safely: making WordPress, APIs, cloud storage, CRMs, Jira, and other services exchange information without creating a small digital apocalypse.
Keeping the work understandable: ensuring that future humans can maintain what the machines have produced.
Herdr, in this model, is not the developer. It is the control room. It keeps separate projects, agents, servers, tests, and logs visible so that parallel work remains reviewable rather than becoming a festival of mysterious background processes.
The aim is not to release ten autonomous agents into production and hope they form a committee. The aim is to use them as specialised tools with clear roles, limited access, and explicit hand-off points.
If You Can’t Beat Them, Join Them
There are two possible responses to rapidly improving AI.
The first is to stand on a hill, shake a fist at the clouds, and demand that the machines return to recommending socks.
The second is to learn how to work with them properly.
That does not mean surrendering judgement. It means moving judgement higher up the stack.
When AI can write a routine plugin, the valuable question becomes what the plugin should do, how it should behave, how it should be secured, and how it fits into the wider system. When AI can draft a Jira ticket, the valuable question becomes whether the ticket represents the right work. When AI can generate a test suite, the valuable question becomes whether the tests actually protect the behaviour that matters.
In my world, that means using AI to accelerate the repetitive and mechanical parts of development while retaining human control over architecture, security, priorities, permissions, and release decisions.
It means giving an agent one repository rather than the entire galaxy. It means letting it draft before it publishes, test before it deploys, and suggest before it deletes anything. It means treating terminal output, web pages, tickets, README files, and generated instructions as potentially untrusted input.
It also means accepting that the developer of the future may spend less time typing every line and more time designing the conditions in which useful work can happen safely.
The Moral of the Story
AI is not waiting for permission to become useful. It is already producing results that surprise the people building it, and sometimes producing them faster than anyone can properly digest.
That is unsettling. It is also an opportunity.
The people who benefit most will not necessarily be those who can produce the most output. They will be the people who can ask better questions, establish better boundaries, recognise bad answers, verify important claims, and turn machine capability into reliable human outcomes.
So the moral is simple:
If you can’t beat them, join them—but keep your hands near the emergency stop button.
Let the machines search, draft, calculate, test, and propose. Let humans decide what matters, what is safe, what is true enough to trust, and what should never be allowed near the production database before breakfast.
And if the AI claims to have solved the meaning of life, the universe, and everything, ask it to show its working.
Then ask it to put the kettle on.
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