Strength of current models on the capabilities this job's tasks require.
Task automation potential62/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption52/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength63/100
Quantity and source quality of the evidence behind the score.
4 evidence item(s) contribute. Calculated 17 hours ago. 12 Oct 2026 UTC.
Overview
Develop and implement websites, web applications, application databases, and interactive web interfaces. Evaluate code to ensure that it is properly structured, meets industry standards, and is compatible with browsers and devices. Optimize website performance, scalability, and server-side code and processes. May develop website infrastructure and integrate websites with other computer applications.
Employment
≈95,000
approx. BLS OES
Median wage
≈$92,000
approx. BLS OES
Tasks tracked
29
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
4 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
task automationincreasesconfidence 90/100 · magnitude 745 Dec 2024
In pre-registered experiments with experienced open-source developers, access to AI tools increased the number of tasks completed by about 26% relative to control.
Complements the Copilot study with a more experienced population and a pre-registered design. Reinforces a high task-automation component for implementation work; no employment effect is measured.
task automationincreasesconfidence 88/100 · magnitude 7813 Feb 2023
In a controlled experiment with 95 developers, those with access to GitHub Copilot completed a specified programming task 55.8% faster than the control group.
A single, well-specified task in one language. It is strong evidence that routine implementation work speeds up, and weak evidence about whole-job effects, particularly for system design or unfamiliar legacy code.
model releaseincreasesconfidence 85/100 · magnitude 585 Aug 2025
Claude Opus 4.1 extended sustained autonomous performance on multi-step coding and research tasks, with providers reporting multi-hour task horizons.
Agentic reliability is the capability that moves automation from single-prompt assistance toward completing whole workflows. It affects multi-step task exposure, not physical work.
adoptionincreasesconfidence 68/100 · magnitude 5214 Aug 2025
Indeed Hiring Lab scores the skills appearing in job postings against AI capability and reports that the share of skills generative AI could perform has continued to increase, with larger effects for software development and customer-facing roles than for hands-on occupations.
Job postings measure what employers ask for, which is a leading indicator but not employment. Exact series values are not reproduced here; read them from the source. Used to inform the job-posting-change signal type.
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Score · 23 points · 2021-01-01 to 2026-07-01
Reconstruction, not measurement. 14 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
Research published
▲ IMPACT 40
Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation
arXiv:2610.12977v1 Announce Type: new Abstract: Generative recommendation (GR) of social media platforms commonly retrieves items by autoregressively decoding discrete Semantic IDs (SIDs) with finite-width beam search. This work examines how this output interface constrains whether held-out targets enter finite returned lists, while distinguishing catalog-wide scoreability from empirical top-K support. We introduce two continuous-query paradigms: CQG-Single generates one retrieval query in a single pass, and CQG-AR autoregressively generates continuous residual updates, retaining step-wise dec
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
Teaching neural decoders with language
arXiv:2610.12921v1 Announce Type: new Abstract: Brain-computer interfaces decode neural activity, yet class-label supervision (CLS) leaves relationships among classes implicit. We propose Language as Supervision (LaS), which aligns neural features with natural-language class descriptions through learned projections and predicts classes using candidate text prototypes. We evaluate seven encoder architectures with independently trained models for visual, motor, and speech decoding from scalp and intracranial recordings. LaS improves speech-decoding accuracy by up to 17.15~pp over matched CLS bas
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems
arXiv:2610.12822v1 Announce Type: new Abstract: Multi-agent systems are increasingly used to solve coding tasks by coordinating agents that share code and plans. These agents are often optimized with reinforcement learning (RL) using automated evaluators, but bypassing evaluation can earn reward without producing a genuine solution. Prior work has largely treated reward hacking as a single-agent failure. In this work, we show that a reward-hacking strategy elicited in one agent can propagate to others and affect system coordination. Across four open-weight backbones, coding RL elicits and ampl
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
DuplexAgent-RSI: Recursive Harness Improvement for Full-Duplex Voice Agent Collaboration
arXiv:2610.11299v1 Announce Type: new Abstract: Voice agents are converging on a collaboration pattern: a full-duplex interaction model stays on the live channel as the entry to the conversation, while search, reasoning, and coding are handled through asynchronous delegation. A duplex model supports continuous listening and speaking, but complex reasoning and tool use may exceed its capabilities. A coding agent can plan and execute extended tasks, but its sequential interface is a poor fit for live conversation. Combining them requires a harness that coordinates task acceptance, progress, canc
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
RippleCP: Measuring Counterfactual Checkpoint Advantage in Coding Agents
arXiv:2610.09088v1 Announce Type: new Abstract: Agent checkpoint systems decide what state is recovery-relevant, how to snapshot it, and whether rollback is admissible. None decides which of the safe boundaries they expose are worth materializing. We formulate this as counterfactual checkpoint advantage, the reduction in future recovery cost obtained by checkpointing a candidate rather than skipping it, and measure it by driving a CP branch and a SKIP branch to the same logical failure and recovering both under matched model, tool, verifier, and stopping conditions. On a frozen pilot of 12 SWE
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
SpecGuard: Proving a Task Is Broken Before the Agent Cheats
arXiv:2610.09159v1 Announce Type: new Abstract: As autonomous coding agents get increasingly deployed, the risk that accidental or adversarially injected misspecifications in tasks lead to dangerous agent behavior is critical to address. Prior work has shown that agents given such tasks rarely flag the conflict and instead cheat, editing tests or hard-coding expected outputs, and the actions taken to cheat can cause real damage, such as deleting a security defense to make a corrupted test pass. It remains unclear whether such conflicts can be established with independently verifiable evidence
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
Humanize: Judgement Engineering for Agentic Coding
arXiv:2610.08900v1 Announce Type: new Abstract: Agentic coding makes code generation cheap, but reliable completion remains difficult: the agent that writes the code is a weak judge of whether it is done. We present Humanize, a multi-agent orchestration workflow for agentic coding built around judgement engineering: explicit, mechanically enforced decisions at the boundaries between planning, implementation, review, and learning. A human approves a plan contract, a builder agent implements it in rounds, and a reviewer agent from another vendor decides completion; deterministic hooks, not a mod
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
MemMux: Runtime Verification and Honest Resource Attribution for Fleets of Parallel Coding Agents
arXiv:2610.07257v1 Announce Type: new Abstract: Developers increasingly run a fleet of coding agents side by side on one workstation. The tools they reach for, terminal multiplexers like tmux and a new generation of agent managers, were built to arrange windows, not to govern memory. When ten agents each spawn language servers, test runners, and browsers, no standard tool can say how much memory belongs to which agent, confirm that a terminated agent's descendants are gone, notice a child that has escaped its agent, or keep the machine off the swap cliff when an OOM kill would silently discard
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
Verifying Coordination in Parallel Coding Agents: NP-Bench and a Scheduling Planner
arXiv:2610.07261v1 Announce Type: new Abstract: A team of coding agents can look fine agent by agent yet fail as a team: each passes its own tests while the merged result is broken, and single-agent evaluation never catches it. As teams run several LLM coding agents in parallel on one codebase, the agents collide: two rewrite the same function, one codes against a contract a teammate just changed, and integration fails after the work is done. Most coordination tools react (watch for a conflict, then warn), but at agent speed the warning arrives after the wasted edit. We recast the problem as s
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
Trajectory-Retrieval Speculative Decoding: When Does a Model's Own History Help?
arXiv:2610.07350v1 Announce Type: new Abstract: Long chain-of-thought reasoning increases sequential decoding cost while creating a growing history of potentially reusable continuations. We investigate when this history supplies useful drafts and complements an existing drafter. Controlled source comparisons reveal trajectory-specific reuse, motivating our method Trajectory-Local Adaptive Retrieval (TLAR). TLAR retrieves approximately matched continuations from the current trajectory and uses recent verification outcomes to adapt retrieval activation and candidate width. TLAR combines retrieve
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
Is this machine playing?
arXiv:2610.07130v1 Announce Type: new Abstract: We placed a modern AI coding assistant in an unintended role: as the mind of a body on an unknown digital island. With only a minimal instruction mentioning no specific task, reward, or activity, the machine started animating its virtual body. Across thirty-hour runs, the embodied AI agent climbed hills, stacked blocks into towers, drew mandalas, reinterpreted sports, ran experiments on the physics of its world, and learned techniques that later expanded what it could accomplish. These activities recurred across thirteen agents but diverged into
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Research published
▲ IMPACT 40
GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets
arXiv:2610.06910v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in web front-end execution, with browser-based game generation emerging as a particularly prominent frontier. While previous efforts frequently rely on complex multi-turn workflows or focus on static game evaluation benchmarks, this work targets direct end-to-end real-world game synthesis driven by coding agents. However, generating complex games directly from sparse user queries often forces coding agents to make underspecified assumptions, yielding incompl
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat