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A closer look

Software developer

AI can write the code. Someone still has to decide what's worth building, and fix it at 3am.

AI could take a fair chunk of six of the eight tasks, led by testing, working out requirements and documenting fixes. Only talking things through with engineers and customers is mostly out of its reach.

47 / 100

Changing significantly

AI could do a real share of the everyday tasks. The job is changing shape, not vanishing.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Exposed: Gradient 3

17 of the 20 jobs up close

Why this score?

The ILO rated each task in this job for how much of it AI could do. They average 52.7 out of 100, so the job holds up at 100 − 52.7 = 47.3.

ILO group “Exposed: Gradient 3”: Most of the tasks are affected.

AI helps JSA 2025
77
Usually good news: more of the work gets quicker without the job going away.
AI takes over JSA 2025
63
This is what most people mean by “exposed”. We keep it apart from helping on purpose.
Hands-on O*NET 31.0
14.2
Software can't do hands-on work, but robots might one day. How far they've got isn't on this site, because nobody measures it well enough yet.

from the source Working Paper 140, Generative AI and Jobs 2025 index O*NET 31.0 Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use

Man v Bot · compare

Who holds up better?

Software developer 47

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Writing routine implementation code from a clear specification.
  • Test scaffolding, boilerplate and migrations.
  • Reading unfamiliar code to work out what it does.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Choosing what to build, and what to refuse to build.
  • Owning a system at three in the morning when it is failing.
  • Judging which shortcut is survivable.

Both

What's arriving

New work that only exists because the AI does.

  • Reviewing far more generated code than any team used to write.
  • Designing systems that assume a model is in the loop.
  • Security and provenance work created by that assumption.

Written by a human, from the task data. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: High

63 could do without you

It could take over a lot of this job on its own. It could help with 77 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

14 how hands-on

Not much of this job needs hands, so the robot can sit this one out.

So could a robot do the physical part?

Less than half of this job is hands-on. Robots still matter, but for a smaller part of the work, so we don't sort it any further.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 12 out of 100. The higher it is, the longer the robot waits.

12 How fiddly
and cramped

Kind of hands-on work Less than half hands-on

The small print

It isn't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. This is the closest thing we have: how much fiddly handwork and cramped, awkward work the job takes, which is what Frey and Osborne found hardest for machines.

The numbers come straight from the US job database's entry for Software Developers. Its wider group, Software developers, lands in the same place.

finger skill 16 · hand skill 12.6 · cramped spaces 6.5 · hands-on 14.2 · caring 2.23 of 7 · rule: hands-on under 50

from the source O*NET 31.0 data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Out of every 100 conversations

39
61

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job. This job makes up 0.35% of the conversations.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should software developers be worried?

Short answer: not worried, but paying attention. The job is changing.

The job isn't disappearing, but the mix of work is changing quickly. Six of its eight tasks rate Medium. Writing routine code is becoming cheaper. Deciding what should be built, reviewing generated work and taking responsibility when systems fail are becoming more valuable.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing. Bank tellers survived the cash machine. The job just got more chatty.

Where this job could lead

Fancy a rematch?

Put software developers up against another job, then send the result to someone who needs a little humbling.

Where this page's numbers come from