AI Made Me 5x Faster. It Also Made Me 5x Worse at My Job.

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".

There is a moment happening in offices and bedrooms all over the world right now, and it looks like this. Three AI agent sessions are running. One is refactoring something.

One is writing tests. One is halfway through a migration nobody wanted to do by hand. The person in front of them is not typing.

They are watching. Their eyes move between panes like someone who put chips on three tables and cannot decide which one to be nervous about. Then the thought arrives.

I could start a fourth one. I call it the fourth terminal, and I think it is the defining mistake of the AI coding era. Not because running agents in parallel is bad.

Because of what the reflex reveals. AI handed us spare capacity, and our first instinct was to fill it with more AI, instead of asking what the spare capacity was actually for. I made that mistake for about four months.

Nearly everyone I know made it too. This is what it cost, what the research now says about why it happens, and what the people who came out the other side are doing instead. The AI honeymoon is real, and you should enjoy it Let me be fair to the tools first, because the backlash has gotten lazy.

When agentic coding properly landed in my workflow, it felt like someone lifted a weight off my chest I had not known I was carrying. All that configuration written by hand. All that run, squint, fix the typo, run again.

Suddenly optional. Work that used to take a day was done before lunch. A migration I had avoided for a quarter got drafted in an afternoon.

My manager noticed. My team noticed. This is not vibes.

In a controlled study of developers building a simple HTTP server, the ones with an AI assistant finished noticeably faster. In a field experiment across thousands of developers, merged pull requests rose by roughly a quarter. If your work involves a lot of greenfield code or a lot of boilerplate, the AI speedup is real and it is not small.

So we did the obvious thing. We got faster, so we took on more. Bug report that smells like infrastructure? I am on it.

Someone needs a dashboard by Friday? Sure. Ticket from March rotting in the backlog? Why not. My open pull request count started to look like a typo.

The bill AI quietly runs up Here is what nobody tells you about being five times faster. You can also get lost five times faster. The first sign was easy to ignore.

A colleague asked about one of my open PRs, and I had to read my own description to remember what it was for. The description had been written by AI. I was reading a machine's summary of a decision I had apparently made, in order to find out what I thought.

That is not productivity. That is a queue with your name on it. The second sign was not small.

An agent produced a change adding a new permission set for a service. Clean diff. Sensible naming.

Tests green. I reviewed it the way I had started reviewing everything by then, which is to say I scrolled, nodded, approved. It had been open six days and I wanted it gone.

Two things saved me. A teammate who actually reads policy documents left one comment: "is this wildcard on purpose?" And luck, in that the comment landed before the merge did. The AI had done exactly what I asked.

I had asked for the wrong thing, vaguely, and it filled the gap with the most permissive option available. The tests passed because the tests checked that the permission existed, not that it was safe. That is the sentence that now governs my working day:"All tests pass" is not the same as "this does what I meant.".