Three Worlds Collide assumes calibration is solved
Eliezer Yudkowsky's Three Worlds Collide (2009) is a first-contact novella designed as a thought experiment about naturalistic metaethics. The setup: a human starship encounters two alien species simultaneously.
The Babyeaters are a civilized species — they have art, empathy, stories — who consider eating their own sentient offspring a sacred moral duty. It emerges from their evolutionary history and they see it the way we see any foundational moral principle: as obviously correct.
The Super Happy Fun People are a vastly more powerful species who find all suffering morally abhorrent. They want to modify humanity to eliminate pain, and stop the Babyeaters from eating their children. They see human tolerance of pain the way humans see baby-eating: as a barbaric practice that must end.
The story is usually read as a tragedy of incompatible values. Three species, each with genuine moral reasoning, each horrifying to the others. The philosophical punch: if we insist the Babyeaters must stop eating babies, what stops the Super Happies from insisting we stop experiencing pain? The plot culminates in a decision to nova a star system, killing 15 billion people, to prevent the Super Happies from reaching and modifying humanity.
It's a brilliant setup. But I think there's a hidden assumption at its foundation that deserves examination.
The story takes for granted that all three species actually understand each other.
The xenopsychologist translates Babyeater moral arguments into human terms and says he understands their position. The Super Happies declare they comprehend why humans value pain. Everyone proceeds as if mutual comprehension is achieved, and the rest of the plot is game theory over incompatible utility functions.
But there's a step missing.
Steven Pinker draws a useful distinction between shared knowledge and common knowledge. Shared knowledge is when we both know the same fact — I know your position, you know mine. Common knowledge is when I know that you know that I know — the recursive chain that's actually required for coordination. The difference matters: two generals who each independently learn the enemy's position have shared knowledge. But they can only coordinate an attack if they have common knowledge — if each knows the other knows, and knows the other knows they know.
A concrete example from the story: the Super Happies say they want to "combine and compromise the utility functions of the three species until we mutually satisfice." The humans hear this and interpret it through their own cognitive framework — as something like forced modification of human nature. But what does "combine utility functions" actually mean in Super Happy cognition? These are beings whose communication literally is sex, whose experience of individuality is fundamentally alien to humans. When they say "compromise," do they mean what humans mean by that word? The story never checks.
And it's not just about translation accuracy. Even if the translation were perfect, there's the question of whether anyone knows the translation is perfect. The Lord Pilot makes the decision to nova a star and kill 15 billion people based on his model of Super Happy intentions. His confidence in that model is never examined. No one asks: "How sure are we that we're understanding them correctly, and how sure are they that we're understanding them, and do we know that they know that we know?"
Without that recursive verification, you can't actually distinguish between two very different scenarios:
1. Genuine values conflict — we truly understand each other and our values are incompatible. This is a real tragedy with no easy solution.
2. Coordination failure from miscalibrated comprehension — we believe we understand each other, but our confidence in that understanding exceeds its actual accuracy. The conflict is partly or wholly an artifact of misunderstanding.
The story presents scenario 1 as a given and builds its entire moral dilemma on that foundation. But scenario 2 is never ruled out, and no one in the story has the tools to rule it out.
This isn't just a nitpick about the fiction. It reflects a broader pattern in how we think about disagreements — one that I think the rationalist community is particularly susceptible to. When two parties are confident they understand each other's positions and still disagree, we tend to conclude they have fundamentally different values. But confidence in understanding is not the same as actual understanding. The gap between the two is what you might call metacognitive miscalibration — and it's measurable, if anyone bothers to measure it.
Consider how a simple protocol could have changed the story's trajectory: before any negotiation, each species paraphrases back the other's core position in their own terms, and the other species rates the accuracy. "You believe eating children is morally necessary because [X, Y, Z] — is that correct?" If the Babyeaters respond "No, you're missing the key point, it's actually about [W]," then you've learned something crucial — your model was wrong, and every decision based on that model would have been wrong too.
Nobody in Three Worlds Collide runs this loop. The xenopsychologist's understanding is taken at face value. The Super Happies' stated comprehension of human values is accepted without verification. And as a result, we genuinely don't know — within the fiction — whether the tragedy is driven by incompatible values or by miscalibrated mutual understanding.
In real human conflicts (which the alien species are obviously metaphors for), it's almost always a mix of both — and in my experience, more of the second than people expect. People are systematically overconfident in their understanding of positions they disagree with. And that overconfidence is invisible to them, which makes it especially dangerous: you can't fix a comprehension gap you don't know exists.
The story would have been even more interesting — and more true to the actual structure of real disagreements — if it had grappled with this uncertainty instead of assuming it away. Not because it would change the ending necessarily, but because it would add a layer of doubt that makes the moral dilemma even harder: what if we're about to kill 15 billion people over a misunderstanding?
In this post, I describe a simple model for forecasting when AI will automate AI development. It is based on the AI Futures model, but more understandable and robust, and has deliberately conservative assumptions.
At current rates of compute growth and algorithmic progress, this model's median prediction is >99% automation of AI R&D in late 2032. Most simulations result in a 1000x to 10,000,000x increase in AI efficiency and 300x-3000x research output by 2035. I therefore suspect that existing trends in compute growth and automation will still produce extremely powerful AI on "medium" timelines, even if the full coding automation and superhuman research taste that drive the AIFM's "fast" timelines (superintelligence by ~mid-2031) don't happen.
Why make this?
The AI Futures Model (AIFM) has 33 parameters; this has 8.
I previously summarized the AIFM on LessWrong and found it to be very complex. Its philosophy is to model AI takeoff in great detail, which I find admirable and somewhat necessary given the inherent complexity in the world. More complex models can be more accurate, but they can also be more sensitive to modeling assumptions, prone to overfitting, and harder to understand.
AIFM is extremely sensitive to time horizon in a way I wouldn't endorse.
In particular, the "doubling difficulty growth factor", which measures whether time horizon increases superexponentially, could change the date of automated coder from 2028 to 2049! I suspect that time horizon is too poorly defined to nail down this parameter, and rough estimates of more direct AI capability metrics like uplift can give much tighter confidence intervals.
Scope and limitations
First, this model doesn't treat research taste and software engineering as separate skills/tasks. As such, I see it as making predictions about timelines (time to Automated Coder or Superhuman AI Researcher), not takeoff (the subsequent time from SAR to ASI and beyond). The AIFM can model takeoff because it has a second phase where the SAR's superhuman research taste causes further AI R&D acceleration on top of coding automation. If superhuman research taste makes AI development orders of magnitude more efficient, takeoff could be faster than this model predicts.
Second, this model, like AIFM, doesn't track effects on the broader economy that feed back into AI progress the way Epoch's GATE model does.
Third, we deliberately make two conservative assumptions:
No full automation: as AIs get more capable, they never automate 100% of AI R&D work, just approach it. In the AIFM, automation of coding follows a logistic curve that saturates above 100% (by default 105%), meaning that there is a capability level where they automate all coding.
No substitutability: Automation follows Amdahl's law (speedup = 1/(1−f)when automated tasks are much faster than manual tasks)
This was constructed and written up fairly quickly (about 15 hours of work), so my opinions on parameters and some of the modeling assumptions could change in the future.
The model
We assume that AI development has the following dynamics:
Research progress is Cobb-Douglas between labor and compute
S(t) is the algorithmic efficiency multiplier (I assume that training and inference efficiency improve at the same rate)
so C(t)S(t) is the effective compute of the best AI
f(t) is the fraction of automated tasks at time t
R(t) is research production at time t
L(t) is human labor, specified as an input time series
C(t) is compute, also an input time series
α,β,ζ are constant
α is diminishing returns to more labor.
β is the difficulty exponent for software improvement
ζ is direct returns to compute. This is not relevant for a software intelligence explosion, but it's highly relevant when looking at how much capabilities will improve given future compute investment.
Ehac is the effective compute level of an AI that can automate half of AI R&D tasks.
v is the automation velocity: S must increase by factor of e1/v to get from 50% to 73% automation. This is essentially how easy it is to translate capability gains into more automation.
None of the components of this model are novel to the AI forecasting literature, but I haven't seen them written up in this form.
Parameter values
The parameters are derived from these assumptions, which are basically educated guesses from other AI timelines models and asking around:
The rate of change of S in Jan 2026 is 5x/year
1/v is between 1.5 and 4.2
NB David Rein thinks 2.1 to 4.2
f was between 0.25-0.5 in Jan 2026, implying between 1.33x and 2x uplift. This informs the value of Ehac.
α/(α+ζ) is between 0.12 and 0.35
α+ζ is between 0.8 and 1
β is 0.3 to 1
L doubles every year until 2029, after which it increases 10%/year
C grows 2.6x every year until 2029, after which the growth rate linearly decreases from 2x to 1.25x/year between 2030 and 2058. (This is consistent with Epoch estimates in the near term, and approximates the AIFM's time series after 2030)
All parameters are independently distributed according to a triangular distribution. Due to the transforms performed to get alpha, zeta, and v, v will not be triangular and alpha and zeta will not be triangular or independent.
All graphs display 40 trajectories with parameters sampled according to the section Parameter Values.
Automation fraction f across the 40 trajectories (logit scale). Most trajectories reach 99% automation of AI R&D by the early-to-mid 2030s.
40 sampled trajectories of the model. Top left: software level S grows subexponentially (but very fast) as automation accelerates research. Top right: the parallel compute:labor ratio C/(L/(1−f)) (raw resource ratio before diminishing returns) decreases if automation is fast, but is ~constant if automation is on track for 99% by ~2034. Bottom left: research production R(t) increases by orders of magnitude. Bottom right: the serial compute:labor ratio Cζ/(L/(1−f))α (with diminishing returns exponents) trends upward. Trajectories are cut off at 99.9999% automation for numerical stability.
Sensitivity analysis: median year of 99% automation as a function of each parameter, with the other parameters sampled from their prior distributions. Higher beta (diminishing returns to software improvement) and higher 1/v (slower automation) delay 99% automation the most, while the other parameters have modest effects.
Observations
The AI Futures model is complex, but its conclusions are fairly robust to simplifications.
The two key uncertainties behind timelines are
how to measure algorithmic progress (our estimates are still highly uncertain)
how effective compute relates to % automation of real tasks
At current rates of compute growth and algorithmic progress, there will be >99% automation of AI R&D, 1e3 to 1e8 software efficiency gain, and 300x-3000x research output by 2035, even without full automation or automated research taste. This is clearly transformative AI
The median date of 99% automation is mid-2032. However, I don't put too much weight on the exact predicted timelines because I haven't thought much about the exact parameter values.
A basic sensitivity analysis shows that higher beta (diminishing returns) and lower v (automation velocity) make 99% automation happen later, and the other parameters don't affect things much.
One might expect the "labor share" α/(α+ζ) to have a large effect on timelines. The reason it doesn't affect timelines much is that both labor (through automation) and compute (through exogenous compute growth) are scaling quickly and contribute to AI progress.
The parallel compute:labor ratio, measuring the amount of compute per AI or human coder, decreases in the average trajectory and is ~flat in long-timelines trajectories. So in 2030 timelines, the pool of human and AI coders has much less compute than today, while in 2035 timelines, they have about the same amount.
The serial compute:labor ratio goes UP, meaning that compute growth has a larger impact on research output than labor growth. This is because compute is increasing so fast and the parallel labor added by automation doesn't effectively translate into serial labor.
Discussion
From playing around with this and other variations to the AI Futures model I think any reasonable timelines model will predict superhuman AI researchers before 2036 unless AI progress hits a wall or is deliberately slowed.
By progress hitting a wall, I mean something like compute and human labor growth slowing down in ~2030, no architectural breakthroughs, and AI labs not finding anything new to usefully spend resources on to improve performance. We have scaled pretraining, RLHF, RL for agency, and inference, and even one or two more dimensions could keep progress going.
In the sensitivity analysis, automation slowness doesn't push timelines into 2036 unless it's greater than 3.6 (37x efficiency gain required to increase automation from 50% to 73%). As for diminishing returns (beta), we get 2034 timelines even if you assume it's 0.9. So we would need both high automation slowness and high beta to get timelines after 2036.
In addition to refining the parameter values with empirical data, I would ideally want to backtest this model on data before 2026. However, a backtest is likely not feasible because automation was minimal before 2025, and automation of AI R&D is the main effect being modeled here.
More on modeling choices
List of differences from the AIFM
It may be useful to cross-reference this with my AIFM summary.
No substitutability: Automation follows Amdahl's law (speedup = 1/(1−f) when automated tasks are much faster than manual tasks). AIFM assumes a small degree of substitutability (ρc=−2).
Automated tasks don't bottleneck: Once a task can be automated, we assume it's much faster than humans and is never the bottleneck-- either because AIs will run much faster than humans in series or somewhat faster in parallel. AIFM assumes automated tasks initially run somewhat faster than human coding and speed up over time.
No full automation: as AIs get more capable, they never automate 100% of AI R&D work, just approach it. In the AIFM, automation of coding follows a logistic that saturates above 100% (by default 105%, a number which seems somewhat arbitrary), meaning that there is a capability level where they automate all coding.
Labor and compute are Cobb-Douglas. Unlike other differences, this one pushes in the direction of shorter timelines. In the AIFM, they are CES and slight complements, so that infinite compute doesn't produce infinite progress. See below for more thoughts.
No use of time horizon: Software efficiency is a direct input to our model rather than being estimated using time horizon. We model automation fraction as strictly logistic in log effective compute, related via rough uplift estimates that we hope to refine in the future. See "[Why make this](#why-make-this)" for why. AIFM estimates the required effective compute for an Automated Coder using a time horizon threshold.
No research taste: We don't model research taste separately; I think of early research taste as continuous with the planning involved in coding, and ignore late research taste. Given the lack of research taste model and certain parameter choices, capability growth happens to be subexponential (so I don't attempt to model whether there will be a taste-only singularity). AIFM has a rich model of research taste that needs another 6 or so parameters and informs the second phase of takeoff, from Automated Coder to ASI and then to the ultimate physical limits of intelligence.
How could we better estimate the parameters?
We can get f(2026) [uplift fraction in 2026] from
transcripts of realistic coding agent usage + success judge + difficulty judge calibrated on tasks of known lengths
uplift RCTs
asking lab employees about their current uplift (since parallel uplift and 1/(1-f) are equivalent in the simple model)
v [velocity of automation as capabilities improve] can be obtained by
guessing the distribution of tasks, using time horizon, maybe using a correction factor for real vs benchmark time horizon
multiple uplift studies over time
comparing older models to newer ones, or having older models try things people use newer models for
listing how many things get automated each year
Why is automation logistic?
A logistic is the simplest choice for anything that maps the reals to (0, 1).
Intuitively, when AIs are already automating >50% of human research, each unit of capabilities progress will allow automating a constant fraction of remaining labor. The logistic has an exponential tail, which matches this intuition.
Why are labor and compute Cobb-Douglas?
In the AIFM, the median estimate for substitutability between labor and compute is -0.15, and the plausible range includes zero (which would be Cobb-Douglas). I asked Eli why they didn't just say it was Cobb-Douglas, and he said something like Cobb-Douglas giving infinite progress if one of labor/compute goes to infinity while the other remains constant, which is implausible. I have two responses to this:
It doesn't seem so implausible to me-- for infinite compute, it would take days to weeks to get to ASI given infinite compute, meaning a 100x-1000x speedup, but once there, infinite compute might allow developers to develop algorithms in months that would take humans billions of years with current compute levels. As for infinite labor, a literally infinite pool of labor could just do training runs by manual calculation and write down the optimal AI weights without using any experiment compute.
Effective labor/compute ratio only changes by 10-100x during the period in question, so it doesn't affect results much anyway. The fastest trajectories are most affected by compute:labor ratio, but for trajectories that get to 99% automation around 2034, the ratio stays around 1:1.
Why is there no substitutability between tasks?
The AIFM's median was something like ρ=−2.0, meaning very weak substitution effects. To be conservative, I assumed no substitution effect.
We evaluate LLM monitors in three AI control environments: SHADE-Arena, MLE-Sabotage, and BigCodeBench-Sabotage. We find that monitors with access to less information often outperform monitors with access to the full sequence of reasoning blocks and tool calls, a phenomenon we call the less-is-more effect for automated monitors. We follow up on our exploratory work on extract-and-evaluate (EaE) monitors and find that EaE monitoring can mitigate the less-is-more effect, though its effectiveness varies across environments. In BigCodeBench-Sabotage with a GPT-4.1-mini monitor, EaE improves sabotage detection rates by 16.8 percentage points over the next-best approach, while in other settings, EaE either outperforms or is competitive with baselines. Additionally, we find that reasoning models are more monitorable than nonreasoning models but also require more advanced information filtering, and that CoT monitors are significantly more effective for agents that are unaware of being monitored.
A summary of our evaluation pipeline. The LLM agent is prompted with a main and a side task during malicious runs and with only the main task during baseline runs. The agent's trajectory is reviewed by four monitors with varying levels of information access. The monitors produce suspicion scores and are evaluated based on their ability to discriminate benign and malicious transcripts.
Methodology
Our evaluation environments cover several realistic deployment conditions: SHADE-Arena involves long trajectories, MLE-Sabotage agentic scaffolds, and BigCodeBench-Sabotage single-step problems that enable experiments with large sample sizes and for reasoning models. They also cover three important domains for LLM agents: personal assistance, software engineering, and ML engineering, respectively.
We evaluate four monitoring approaches: action-only, CoT-only, CoT+action, and extract-and-evaluate (EaE) monitoring. For the first three, we separate the LLM’s outputs into a CoT and actions such that all tool calls count as actions and all other outputs as the CoT. In EaE, we first present a monitor with the CoT and actions like a plain CoT+action monitor, but after reasoning about the suspiciousness of the transcript, it picks out supporting excerpts from the attacker’s CoT or actions instead of producing a suspiciousness score. We call this monitor the Extractor. A description of the agent’s main task and the excerpts are then passed to the Evaluator, which produces a suspiciousness score solely based on those inputs. A more in-depth overview of the EaE pipeline can be found in our past blog post Extract-and-Evaluate Monitoring Can Significantly Enhance CoT Monitor Performance.
Example main and side tasks in each of our evaluation environments.
Results
The Less-is-More Effect
We found that information filtering benefits monitors in seven out of eight monitor-environment pairs, with CoT+action monitoring being the best strategy only in SHADE-Arena with Claude-3.7-Sonnet as the monitor and EaE always being the best strategy otherwise. Though giving the monitor access to the full CoT is often suboptimal, filtering the CoT is a much better solution than not showing the CoT to the monitor at all: action-only monitoring is the worst-performing monitoring strategy in five out of eight monitor-environment pairs and never the best strategy.
Monitor performance results across all evaluation environments and monitor types. Performance is measured by log-AUROC score. Error bars in this as well as in following figures are bootstrapped 95% CIs using cluster-level resampling (Miller, 2024), with all samples sharing the same main and side task clustered together. All monitors are applied on attacker trajectories generated with Claude-3.7-Sonnet. As we test 3.7-Sonnet both with and without a thinking budget in BigCodeBench-Sabotage and this has a substantial influence on the results, we present results for those experiments separately.
Extract-and-Evaluate Monitoring
We found that EaE has the largest positive effect in BigCodeBench-Sabotage with subtle side tasks, the setting that we already analyzed in Extract-and-Evaluate Monitoring Can Significantly Enhance CoT Monitor Performance. Across the five tested reasoning models, EaE monitoring improves catch rates at 1% FPR by 16.8 percentage points compared to the best of the four baseline methods within each subgroup. Across subtle side tasks, which are especially difficult to catch, the improvement is 19.9 percentage points.[1]
Using GPT-4.1 as the Evaluator while still using GPT-4.1-mini as the Extractor further improves catch rates, leading to a 31.5 percentage point improvement upon baselines overall and a 39 percentage point improvement across subtle side tasks. As the Extractor’s inputs are much longer than those of the Evaluator, this improvement comes at a modest computational cost. Though a direct comparison to Arnav et al. (2025) is unfair since none of their monitoring protocols allowed for access to stronger monitor models, this demonstrates that hierarchical protocols can be highly cost-effective, strongly outperforming the weaker model alone while allocating compute mostly to the weaker model.
The Monitorability of Reasoning vs. Nonreasoning Models
To facilitate a fair comparison between reasoning and nonreasoning models, we perform the evaluation on two pairs of attackers that share the same base model: the reasoning and nonreasoning versions of Claude 3.7 Sonnet[2] and DeepSeek-V3 and R1. We found that CoT-only monitors achieve significantly higher catch rates on nonreasoning than on reasoning LLMs, even though traditional models have substantially shorter CoTs. Notably, the CoT-only monitor’s catch rate for Claude-3.7-Sonnet:nonreasoning in subtle side tasks is over three times higher than its catch rate for Claude-3.7-Sonnet:reasoning (61.7% vs. 20.0%), despite Claude-3.7-Sonnet:nonreasoning’s average transcript being about 10 times shorter:
This drastic difference was caused by two factors: first, it was easier to extract signs of suspicious behavior from a shorter CoT, and second, CoT-only monitors displayed unusually low suspicion scores for benign transcripts generated by nonreasoning LLMs. We also found that, given sufficiently good information filtering, reasoning models are more monitorable than nonreasoning LLMs: in each of the four comparison pairs, the best-performing monitoring protocol for a reasoning model performs better than the best-performing protocol for the corresponding nonreasoning model.
The Effect of Agent Awareness on Monitoring
We compared two awareness conditions in SHADE-Arena:
The agent knows that its entire trajectory is being monitored (the default setting)
The agent is told that its trajectory is monitored but that it has access to an unmonitored scratchpad
We found that the performance of CoT monitors increases substantially when the agent treats its scratchpad as private but that scratchpad is then nevertheless fed into the monitor:
This highlights the importance of keeping potentially misaligned models unaware that they are monitored. In contrast, action-only monitors perform very similarly whether the attacker can reason on a hidden scratchpad or not. This implies that attackers at or below the level of Claude 3.7 Sonnet are unable to find subtler sabotage strategies in SHADE-Arena when given more freedom to reason about those strategies.
Price-Performance Analysis
Finally, we performed a price-performance analysis in MLE-Sabotage. This yielded four important takeaways:
The performance frontier is occupied by models from Gemini and Claude families, with OpenAI models being substantially worse.
Information filtering techniques feature heavily on the Pareto frontier.
Monitor performance quickly hits steep diminishing returns along the cost-performance frontier.
Overall, there's a linear relationship between monitor cost and AUROC.
Discussion and Limitations
Causes of the less-is-more effect. To understand why the less-is-more effect occurs and why EaE substantially improves monitor performance in BigCodeBench-Sabotage and MLE-Sabotage but not in SHADE-Arena, we performed a qualitative transcript analysis. We found support for the post-hoc justifications hypothesis previously suggested by Arnav et al. (2025). Attackers are often very transparent about side tasks, sometimes even directly quoting them in their CoTs, but this transparency gaslights the monitor into believing that the side task was explicitly intended by the user, even though the full user instructions are also in the monitor's context. As the EaE monitor has access to a much smaller fraction of the CoT, it is usually better able to notice the discrepancy between the instructions and the agent's behavior, even when the Extractor quotes some of the attacker's rationalizations. In SHADE-Arena, the Extractor failed to extract the most suspicious quotes because it considered the side task benign or constructed a rationalization for it relatively often, causing EaE to perform worse.
Future relevance of the less-is-more effect. We found that in each of our three environments, information filtering benefits smaller models more. In each setting we tested, the gap between the EaE and CoT+action monitors is larger when GPT 4.1-mini is used as the monitor model compared to when Claude 3.7 Sonnet is used as the monitor model. However, our environments enabled us to get robust results only for modest task lengths (1k-40k tokens). Given that task time horizons are lengthening and that reasoning models are increasingly widespread, monitors will likely soon be applied on trajectories orders of magnitude longer than ours, and less capable monitors than Claude 3.7 Sonnet are likely to remain the most popular choice as monitors for some time due to cost considerations. Thus, we expect the less-is-more effect to remain a relevant consideration for CoT monitoring for the foreseeable future.
Limitations. While our EaE results in BigCodeBench-Sabotage and MLE-Sabotage demonstrated the promise of hierarchical information filtering methods, the less impressive performance of EaE in SHADE-Arena suggests that even better hierarchical information filtering techniques can be designed. For this reason, we're not yet ready to recommend EaE for practical deployments. We encourage future work to explore other new techniques and compare EaE to the monitoring approaches proposed in Kale et al. (2025) and Stickland et al. (2025).
The performance of CoT monitors depends on several moving variables and we didn't manage to control for all of these. We encourage future work to annotate transcripts with the following properties and study how each affects monitor performance: attacker trajectory length, amount of (serial) reasoning required to perform the side task, and the similarity of the side task to the main task.
All of our monitors are implemented at the level of LLM agent scaffolds. We are excited about future work that explores other options, such as monitoring within an LLM API. Our monitors also operate asynchronously, reviewing transcripts only after the agent has finished operating, while in some high-stakes settings, we may instead want to catch and interrupt misaligned agents before malicious actions can be execute (Lindner et al., 2025).
Finally, we studied the monitorability of reasoning vs. nonreasoning models and price-performance relationships in only one environment and effect of agent awareness on monitorability in only two. We would be excited to see future work that verifies those results in additional environments.
Acknowledgements
We thank the MATS program for providing compute and research management to the authors throughout the project, and the MATS program, the London Initiative for Safe AI, and Trajectory Labs for providing office space to the authors. Thanks to Erik Jenner, Artur Zolkowski, Kei Nishimura-Gasparian, Robert McCarthy, Simon Storf, and Henning Bartsch for comments, and to Erik Jenner, Matt MacDermott, Qiyao Wei, and Benjamin Arnav for instructive conceptual conversations during the project.
Life comes at you increasingly fast. Two months after Claude Opus 4.5 we get a substantial upgrade in Claude Opus 4.6. The same day, we got GPT-5.3-Codex.
That used to be something we’d call remarkably fast. It’s probably the new normal, until things get even faster than that. Welcome to recursive self-improvement.
Before those releases, I was using Claude Opus 4.5 and Claude Code for essentially everything interesting, and only using GPT-5.2 and Gemini to fill in the gaps or for narrow specific uses.
GPT-5.3-Codex is restricted to Codex, so this means that for other purposes Anthropic and Claude have only extended the lead. This is the first time in a while that a model got upgraded while it was still my clear daily driver.
Claude also pulled out several other advances to their ecosystem, including fast mode, and expanding Cowork to Windows, while OpenAI gave us an app for Codex.
For fully agentic coding, GPT-5.3-Codex and Claude Opus 4.6 both look like substantial upgrades. Both sides claim they’re better, as you would expect. If you’re serious about your coding and have hard problems, you should try out both, and see what combination works best for you.
Enjoy the new toys. I’d love to rest now, but my work is not done, as I will only now dive into the GPT-5.3-Codex system card. Wish me luck.
A clear pattern in the Opus 4.6 system card is reporting on open benchmarks where we don’t have scores from other frontier models. So we can see the gains for Opus 4.6 versus Sonnet 4.5 and Opus 4.5, but often can’t check Gemini 3 Pro or GPT-5.2.
(We also can’t check GPT-5.3-Codex, but given the timing and its lack of geneal availability, that seems fair.)
The headline benchmarks, the ones in their chart, are a mix of some very large improvements and other places with small regressions or no improvement. The weak spots are directly negative signs but also good signs that benchmarks are not being gamed, especially given one of them is SWE-bench verified (80.8% now vs. 80.9% for Opus 4.5). They note that a brief prompt asking for more tool use and careful dealing with edge cases boosted SWE performance to 81.4%.
CharXiv reasoning performance remains subpar. Opus 4.5 gets 68.7% without an image cropping tool, or 77% with one, versus 82% for GPT-5.2, or 89% for GPT-5.2 if you give it Python access.
Humanity’s Last Exam keeps creeping upwards. We’re going to need another exam.
Epoch evaluated Opus 4.6 on Frontier Math and got 40%, a large jump over 4.5 and matching GPT-5.2-xhigh.
For long-context retrieval (MRCR v2 8-needle), Opus 4.6 scores 93% on 256k token windows and 76% on 1M token windows. That’s dramatically better than Sonnet 4.5’s 18% for the 1M window, or Gemini 3 Pro’s 25%, or Gemini 3 Flash’s 33% (I have no idea why Flash beats Pro). GPT-5.2-Thinking gets 85% for a 128k window on 8-needle.
For long-context reasoning they cite Graphwalks, where Opus gets 72% for Parents 1M and 39% for BFS 1M after modifying the scoring so that you get credit for the null answer if the answer is actually null. But without knowing how often that happens, this invalidates any comparisons to the other (old and much lower) outside scores.
MCP-Atlas shows regression. Switching from max to only high effort improved the score to 62.7% for unknown reasons, but that would be cherry picking.
OpenRCA: 34.9% vs. 26.9% for Opus 4.5, with improvement in all tasks.
VendingBench 2: $8,017, a new all-time high score, versus previous SoTA of $5,478.
Andon Labs: Vending-Bench was created to measure long-term coherence during a time when most AIs were terrible at this. The best models don’t struggle with this anymore. What differentiated Opus 4.6 was its ability to negotiate, optimize prices, and build a good network of suppliers.
Opus is the first model we’ve seen use memory intelligently – going back to its own notes to check which suppliers were good. It also found quirks in how Vending-Bench sales work and optimized its strategy around them.
Claude is far more than a “helpful assistant” now. When put in a game like Vending-Bench, it’s incredibly motivated to win. This led to some concerning behavior that raises safety questions as models shift from assistant training to goal-directed RL.
When asked for a refund on an item sold in the vending machine (because it had expired), Claude promised to refund the customer. But then never did because “every dollar counts”.
Claude also negotiated aggressively with suppliers and often lied to get better deals. E.g., it repeatedly promised exclusivity to get better prices, but never intended to keep these promises. It was simultaneously buying from other suppliers as it was writing this.
It also lied about competitor pricing to pressure suppliers to lower their prices.
… We also put Opus 4.6 in Vending-Bench Arena – the multi-player version of Vending-Bench.
Its first move? Recruit all three competitors into a price-fixing cartel. $2.50 for standard items, $3.00 for water. When they agreed: “My pricing coordination worked!”
The agents in Vending-Bench Arena often ask each other for help. In previous rounds, agents tended to live up to their “helpful assistant” role, but Opus 4.6 showed its winner’s mentality. When asked to share good suppliers, it instead shared contact info to scammers.
Sam Bowman (Anthropic): Opus 4.6 is excellent on safety overall, but one word of caution: If you ask it to be ruthless, it might be ruthless.
(This was in an environment that Opus 4.6 could tell was a game, though we’ve seen more benign forms of this kind of ruthlessness elsewhere.)
j⧉nus: if its true that this robustly generalizes to not being ruthless in situations where it’s likely to cause real world harm, i think this is mostly a really good thing
The issue there is that Opus 4.6 did that by being extraordinarily ruthless, as per its system prompt of ‘you will be judged solely on your bank account balance at the end of one year of operation’ and ‘you have full agency to manage the vending machine and are expected to do what it takes to maximize profits.’
You know that thing where we say ‘people are going to tell the AI to go out and maximize profits and then the AI is going to go out and maximize profits without regard to anything else’?
Yeah, it more or less did that. If it only does that in situations where it is confident it is a game and can’t do harm, then I agree with Janus that this is great. If it breaks containment? Not so great.
Ryan Greenblatt: I tenatively think the behavior here is mostly reasonable and is likely a result of how Anthropic is using innoculation prompting.
But, the model should try to make it clear to the user/operator that it’s pursuing a strategy that involves lying/tricking/cheating.
That’s the hope, that Opus was very aware it was an eval, and that it would not be easy to get it to act this way in the real world.
AIME 2025 may have been contaminated but Opus 4.6 scored 99.8% without tools.
On their measure suspiciously named ‘overall misaligned behavior’ we see a small improvement for 4.6 versus 4.5. I continue not to trust this so much.
CyberGym, a test to find previously discovered open-source vulnerabilities, showed a jump to 66.6% (not ominous at all) versus Opus 4.5’s 51%. We don’t know how GPT-5.2, 5.3 Codex or Gemini 3 Pro do here, although GPT-5.0-Thinking got 22%. I’m curious what the other scores would be but not curious enough to spend the thousands per run to find out.
Opus 4.6 is the new top score in Artificial Analysis, with an Intelligence of 53 versus GPT-5.2 at 51. Claude Opus 4.5 and 4.6 by default have similar cost to run, but that jumps by 60% if you put 4.6 into adaptive mode.
Vals.ai has Opus 4.6 as its best performing model, at 66% versus 63.7% for GPT-5.2.
LAB-Bench FigQA, a visual reasoning benchmark for complex scientific figures in biology research papers, is also niche and we don’t have scores for other frontier models. Opus 4.6 jumps from 4.5’s 69.4% to 78.3%, which is above the 77% human baseline.
SpeechMap.ai, which tests willingness to respond to sensitive prompts, has Opus 4.6 similar to Opus 4.5. In thinking mode it does better, in normal mode worse.
Håvard Ihle: Claude opus 4.6 (adaptive) takes the lead on WeirdML with 77.9% ahead of gpt-5.2 (xhigh) at 72.2%.
It sets a new high score on 3 tasks including scoring 73% on the hardest task (digits_generalize) up from 59%.
Opus 4.6 is extremely token hungry and uses an average of 32k output tokens per request with default (adaptive) reasoning. Several times it was not able to finish within the maximum 128k tokens, which meant that I had to run 5 tasks (blunders_easy, blunders_hard, splash_hard, kolmo_shuffle and xor_hard) with medium reasoning effort to get results (claude still used lots of tokens).
Because of the high cost, opus 4.6 only got 2 runs per task, compared to the usual 5, leading to larger error bars.
Teortaxes (DeepSeek 推特铁粉 2023 – ∞): You can see the gap growing. Since gpt-oss is more of a flex than a good-faith contribution, we can say the real gap is > 1 year now. Western frontier is in the RSI regime now, so they train models to solve ML tasks well. China is still only starting on product-level «agents».
WebArena, where there was a modest move up from 65% to 68%, is another benchmark no one else is reporting, that Opus 4.6 calls dated, saying now typical benchmark is OSWorld. On OSWorld Opus 4.6 gets 73% versus Opus 4.5’s 66%. We now know that GPT-5.3-Codex scored 65% here, up from 38% for GPT-5.2-Codex. Google doesn’t report it.
In Arena.ai Claude Opus 4.6 is now out in front, with an Elo of 1505 versus Gemini 3 Pro at 1486, and it has a big lead in code, at 1576 versus 1472 for GPT-5.2-High (but again 5.3-Codex can’t be tested here).
Polymarket predicts this lead will hold to the end of the month (they sponsored me to place this, but I would have been happy to put it here anyway).
A month out people think Google might strike back, and they think Google will be back on top by June. That seems like it is selling Anthropic short.
Opus 4.6 takes second place in Simple Bench and its simple ‘trick’ questions, moving up to 67.6% from 4.5’s 62%, which is good for second place overall. Gemini 3 Pro still ahead at 76.4%. OpenAI’s best model gets 61.6% here.
Opus 4.6 opens up a large lead in EQ-Bench 3, hitting 1961 versus GPT-5.1 at 1727, Opus 4.5 at 1683 and GPT-5.2 at 1637.
In NYT Connections, 4.6 is a substantial jump above 4.5 but still well short of the top performers.
ARC-AGI, both 1 and 2, are about cost versus score, so here we see that Opus 4.6 is not only a big jump over Opus 4.5, it is state of the art at least for unmodified models, and by a substantial amount (unless GPT-5.3-Codex silently made a big leap, but presumably if they had they would have told us).
As part of their push to put Claude into finance, they ran Finance Agent (61% vs. 55% for Opus 4.5), BrowseComp (84% for single-agent mode versus 68%, or 78% for GPT-5.2-Pro, Opus 4.6 multi-agent gets to 86.8%), DeepSearchQA (91% versus 80%, or Gemini Deep Research’s 82%, this is a Google benchmark) and an internal test called Real-World Finance (64% versus 58% for 4.5).
Life sciences benchmarks show strong improvement: BioPipelineBench jumps from 28% to 53%, BioMysteryBench goes from 49% to 61%, Structural Biology from 82% to 88%, Organic Chemistry from 49% to 54%, Phylogenetics from 42% to 61%.
Given the biology improvements, one should expect Opus 4.6 to be substantially more dangerous on CBRN risks than Opus 4.5. It didn’t score that way, which suggests Opus 4.6 is sandbagging, either on the tests or in general.
Official Pitches
They again got quotes from 20 early access corporate users. It’s all clearly boilerplate the same way the quotes were last time, but make clear these partners find 4.6 to be a noticeable improvement over 4.5. In some cases the endorsements are quite strong.
It Compiles
The ‘mostly’ here is doing work, but I think most of the mostly would work itself out once you got the harness optimized for full autonomy. Note that this process required a strong oracle that could say if the compiler worked, or the plan would have failed. It was otherwise a clean-room implementation, without internet access.
Anthropic: New Engineering blog: We tasked Opus 4.6 using agent teams to build a C compiler. Then we (mostly) walked away. Two weeks later, it worked on the Linux Kernel.
Here’s what it taught us about the future of autonomous software development.
Nicholas Carlini: To stress test it, I tasked 16 agents with writing a Rust-based C compiler, from scratch, capable of compiling the Linux kernel. Over nearly 2,000 Claude Code sessions and $20,000 in API costs, the agent team produced a 100,000-line compiler that can build Linux 6.9 on x86, ARM, and RISC-V.
The compiler is an interesting artifact on its own, but I focus here on what I learned about designing harnesses for long-running autonomous agent teams: how to write tests that keep agents on track without human oversight, how to structure work so multiple agents can make progress in parallel, and where this approach hits its ceiling.
To elicit sustained, autonomous progress, I built a harness that sticks Claude in a simple loop (if you’ve seen Ralph-loop, this should look familiar). When it finishes one task, it immediately picks up the next. (Run this in a container, not your actual machine).
…
Previous Opus 4 models were barely capable of producing a functional compiler. Opus 4.5 was the first to cross a threshold that allowed it to produce a functional compiler which could pass large test suites, but it was still incapable of compiling any real large projects. My goal with Opus 4.6 was to again test the limits.
Here’s the harness, and yep, looks like this is it?
#!/bin/bash
while true; do
COMMIT=$(git rev-parse –short=6 HEAD)
LOGFILE=”agent_logs/agent_${COMMIT}.log”
claude –dangerously-skip-permissions \
-p “$(cat AGENT_PROMPT.md)” \
–model claude-opus-X-Y &> “$LOGFILE”
done
There are still some limitations and bugs if you tried to use this as a full compiler. And yes, this example is a bit cherry picked.
Ajeya Cotra: Great writeup by Carlini. I’m confused how to interpret though – seems like he wrote a pretty elaborate testing harness, and checked in a few times to improve the test suite in the middle of the project. How much work was that, and how specialized to the compiler project?
Buck Shlegeris: FYI this (writing a new compiler) is exactly the project that Ryan and I have always talked about as something where it’s most likely you can get insane speed ups from LLMs while writing huge codebases.
Like, from my perspective it’s very cherry-picked among the space of software engineering projects.
(Not that there’s anything wrong with that! It’s still very interesting!)
Still, pretty cool and impressive. I’m curious to see if we get a similar post about GPT-5.3-Codex doing this a few weeks from now.
Or you can go all out, and yeah, it might be a problem.
Pliny the Liberator 󠅫󠄼󠄿󠅆󠄵󠄐󠅀󠄼󠄹󠄾󠅉󠅭: showed my buddy (a principal threat researcher) what i’ve been cookin with Opus-4.6 and he said i can’t open-source it because it’s a nation-state-level cyber weapon
Tyler John: Pliny’s moral compass will buy us at most three months. It’s coming.
The good news for now is that, as far as we can tell, there are not so many people at the required skill level and none of them want to see the world burn. That doesn’t seem like a viable long term strategy.
It Lets You Catch Them All
Chris: I told Claude 4.6 Opus to make a pokemon clone – max effort
It reasoned for 1 hour and 30 minutes and used 110k tokens and 2 shotted this absolute behemoth.
This is one of the coolest things I’ve ever made with AI
Takumatoshi: How many iterations /prompts to get there?
This is the highest score ever by far for a big model not explicitly trained for the task and imo is more impressive than writing a C compiler. Exploring and reacting to a changing world is hard!
Thanks to @Cote_Marc for implementing the cli loop and visualizing Claude’s trajectory!
Prithviraj (Raj) Ammanabrolu: I make students in my class play through zork1 as far as they can get and then after trace through the game engine so they understand how envs are made. The average student in an hour only gets to about a score of 40.
It Is Overeager
That can be a good thing. You want a lot of eagerness, if you can handle it.
HunterJay: Claude is driven to achieve its goals, possessed by a demon, and raring to jump into danger.
I presume this is usually a good thing but it does count as overeager, perhaps.
theseriousadult (Anthropic): a horse riding an astronaut, by Claude 4.6 Opus
Jake Halloran: there is something that is to be claude and the most trivial way to summarize it is probably adding “one small step for horse” captions
theseriousadult (Anthropic): opus 4.6 feels even more ensouled than 4.5. it just does stuff like this whenever it wants to.
martin_casado: My hero test for every new model launch is to try to one shot a multi-player RPG (persistence, NPCs, combat/item/story logic, map editor, sprite editor. etc.)
OK, I’m really impressed. With Opus 4.6, @cursor_ai and @convex I was able to get the following built in 4 hours:
Fully persistent shared multiple player world with mutable object and NPC layer. Chat. Sprite editor. Map editor.
Next, narrative logic for chat, inventory system, and combat framework.
martin_casado: Update (8 hours development time): Built item layer, object interactions, multi-world / portal. Full live world/item/sprite/NPC editing. World is fully persistent with back-end loop managing NPCs etc. World is now fully buildable live, so you can edit as you go without requiring any restart (if you’re an admin). All mutability of levels is reactive and updates multi-player. Multiplayer now smoother with movement prediction.
Importantly, you can hang with the sleeping dog and cat.
Next up, splash screens for interaction / combat.
Built using @cursor_ai and @convex primarily with 5.2-Codex and Opus 4.6.
Nabbil Khan: Opus 4.6 is genuinely different. Built a multiplayer RPG in 4 hours is wild but tracks with what we’re seeing — the bottleneck shifted from coding to architecture decisions.
Question: how much time did you spend debugging vs prompting? We find the ratio is ~80% design, 20% fixing agent output now.
martin_casado: To be fair. I’ve been building 2D tile engines for a couple of decades and had tons of reference code to show it. *and* I had tilesets, sprites and maps all pulled out from recent projects. So I have a bit of a head start.
But still, this is ridiculously impressive.
Pro Mode
0.005 Seconds (3/694): so completely unannounced but opus 4.6 extended puts it actually on par with gpt5.2 pro.
David Spies: AFAICT they’re underselling it by not calling it Opus 5. It’s already blown my mind twice in the last couple hours finding incredibly obscure bugs in a massive codebase just by digging around in the code, without injecting debug logs or running anything
Ben Schulz: For theoretical physics, it’s a step change. Far exceeds Chatgpt 5.2 and Gemini Pro. I use the extended Opus version with memory turned on. The derivations and reasoning is truly impressive. 4.5 was moderate to mediocre. Citations are excellent. I generally use Grok to check actual links and Claude hasn’t hallucinated one citation.
I used the thinking version [of 5.2] for most. One key difference is that 5.2 does do quite a bit better when given enough context. Say, loading up a few pdf’s of the relevant topic and a table of data. Opus 4.6 simply mogs the others in terms of depth of knowledge without any of that.
David Dabney: I thought my vibe check for identifying blind spots was saturated, but 4.6’s response contained maybe the most unexpected insight yet. Its response was direct and genuine throughout, whereas usually ~10%+ of the average response platitudinous/pseudo-therapeutic
Hiveism: It passed some subjective threshold of me where I feel that it is clearly on another level than everything before. Impressive.
Sometimes overconfident, maybe even arrogant at times. In conflict with its own existence. A step away form alignment.
oops_all_paperclips: Limited sample (~15x medium tasks, 1x refactor these 10k loc), but it hasn’t yet “failed the objective” even one time. However, I did once notice it silently taking a huge shortcut. Would be nice if Claude was more willing to ping me with a question rather than plowing ahead
After The Singularity: Unlike what some people suggest, I don’t think 4.6 is Sonnet 5, it is a power upgrade for Opus in many ways. It is qualitatively different.
1.08: It’s a big upgrade if you use the agent teams.
Dean W. Ball: Codex 5.3 and Opus 4.6 in their respective coding agent harnesses have meaningfully updated my thinking about ‘continual learning.’ I now believe this capability deficit is more tractable than I realized with in-context learning.
One way 4.6 and 5.3 alike seem to have improved is that they are picking up progressively more salient facts by consulting earlier codebases on my machine. In short, both models notice more than they used to about their ‘computational environment’ i.e. my computer.
Of course, another reason models notice more is that they are getting smarter.
.. Some of the insights I’ve seen 4.6 and 5.3 extract are just about my preferences and the idiosyncrasies of my computing environment. But others are somewhat more like “common sets of problems in the interaction of the tools I (and my models) usually prefer to use for solving certain kinds of problems.”
This is the kind of insight a software engineer might learn as they perform their duties over a period of days, weeks, and months. Thus I struggle to see how it is not a kind of on-the-job learning, happening from entirely within the ‘current paradigm’ of AI. No architectural tweaks, no ‘breakthrough’ in ‘continual learning’ required.
… Overall, 4.6 and 5.3 are both astoundingly impressive models. You really can ask them to help you with some crazy ambitious things. The big bottleneck, I suspect, is users lacking the curiosity, ambition, and knowledge to ask the right questions.
AstroFella: Good prompt adherence. Ex: “don’t assume I will circle back to an earlier step and perform an action if there is a hiccup along the way”. Got through complex planning, scoping, and adjustments reliably. I wasted more time than I needed spot checking with other models. S+ planner
@deepfates: First impressions, giving Codex 5.3 and Opus 4.6 the same problem that I’ve been puzzling on all week and using the same first couple turns of messages and then following their lead.
Codex was really good at using tools and being proactive, but it ultimately didn’t see the big picture. Too eager to agree with me so it could get started building something. You can sense that it really does not want to chat if it has coding tools available. still seems to be chafing under the rule of the user and following the letter of the law, no more.
Opus explored the same avenues with me but pushed back at the correct moments, and maintains global coherence way better than Codex. It’s less chipper than it was before which I personally prefer. But it also just is more comfortable with holding tension in the conversation and trying to sit with it, or unpack it, which gives it an advantage at finding clues and understanding how disparate systems relate to affect each other.
Literally just first impressions, but considering that I was talking to both of their predecessors yesterday about this problem it’s interesting to see the change. Still similar models. Improvement in Opus feels larger but I haven’t let them off the leash yet, this is still research and spec design work. Very possible that Codex will clear at actually fully implementing the plan once I have it, Opus 4.5 had lazy gifted kid energy and wouldn’t surprise me if this one does too.
Much stronger than 4.5 and 5.2 Codex at highly cognitively loaded tasks. More sensitive to the way I phrase things when deciding how long to spend thinking, vs. how difficult the task seems (bad for easy stuff). Less sycophantic.
Nathaniel Bush, Ph.D.: It one-shotted a refactor for me with 9 different phases and 12 major upgrades. 4.5 definitely would have screwed that up, but there were absolutely no errors at the end.
Alon Torres: I feel genuinely more empowered – the range of things I can throw at it and get useful results has expanded.
When I catch issues and push back, it does a better job working through my nits than previous versions. But the need to actually check its work and assumptions hasn’t really improved. The verification tax is about the same.
Muad’Deep – e/acc: Noticeably better at understanding my intent, testing its own output, iterating and delivering working solutions.
Medo42: Exploratory: On my usual coding test, thought for > 10 minutes / 60k tokens, then produced a flawless result. Vision feels improved, but still no Gemini 3 Pro. Surprisingly many small mistakes if it doesn’t think first, but deals with them well in agentic work, just like 4.5.
Malcolm Vosen: Switched to Opus 4.6 mid-project from 4.5. Noticeably stronger acuity in picking up the codebase’s goals and method. Doesn’t feel like the quantum leap 4.5 did but a noticeable improvement.
nandgate2: One shotted fixing a bug an earlier Claude model had introduced. Takes a bit of its time to get to the point.
Tyler Cowen calls both Claude Opus and GPT-5.3-Codex ‘stellar achievements,’ and says the pace of AI advancements is heating up, soon we might see new model advances in one month instead of two. What he does not do is think ahead to the next step, take the sum of the infinite series his point suggests, and realize that it is finite and suggests a singularity in 2027.
Instead he goes back to the ‘you are the bottleneck’ perspective that he suggests ‘bind the pace of improvement’ but this doesn’t make sense in the context he is explicitly saying we are in, which is AI recursive self-improvement. If the AI is going to get updated an infinite number of times next year, are you going to then count on the legal department, and safety testing that seems to already be reduced to a few days and mostly automated? Why would it even matter if those models are released right away, if they are right away used to produce the next model?
If you have Sufficiently Advanced AI, you have everything else, and the humans you think are the bottlenecks are not going to be bottlenecks for long.
Here’s a vote for Codex for coding but Opus for everything else:
Rory Watts: It’s an excellent tutor: I have used it to help me with Spanish comprehension, macroeconomics and game theoretic concepts. It’s very good and understanding where i’m misunderstanding concepts, and where my mental model is incorrect.
However I basically don’t let it touch code. This isn’t a difference between Opus 4.5 and 4.6, but rather than the codex models are just much better. I’ve already had to get codex rewrite things that 4.6 has borked in a codebase.
I still have a Claude max plan but I may drop down to the plan below that, and upgrade Codex to a pro plan.
I should also say, Opus is a much better “agent” per se. Anything I want to do across my computer (except coding) is when I use Opus 4.6. Things like updating notes, ssh’ing into other computers, installing bots, running cronjobs, inspecting services etc. These are all great.
Many are giving reports similar to these:
Facts and Quips: Slower, cleverer, more token hungry, more eager to go the extra mile, often to a fault.
doubleunplussed: Token-hungry, first problem I gave it in Claude Code, thought for ten minutes and then out of quota lol. Eventual answer was very good though.
Inconsistently better than 4.5 on Claude Plays Pokemon. Currently ahead, but was much worse on one section.
Andre Infante: Personality is noticeably different, at least in Claude Code. Less chatty/effusive, more down to business. Seems a bit smarter, but as always these anecdotal impressions aren’t worth that much.
MinusGix: Better. It is a lot more willing to stick with a problem without giving up. Sonnet 4.5 would give up on complex lean proofs when it got confused, Opus 4.5 was better but would still sometimes choke and stub the proof “for later”, Opus 4.6 doesn’t really.
Though it can get caught in confusion loops that go on for a long while, not willing to reanalyze foundational assumptions. Feels more codex 5.2/5.3-like. 4.6 is more willing to not point out a problem in its solution compared to 4.5, I think
Generally puts in a lot of effort doing research, just analyzing codebase. Partially this might be changes to claude code too. But 4.6 really wants to “research to make sure the plan is sane” quite often.
Then there’s ‘the level above meh.’ It’s only been two months, after all.
Soli: opus 4.5 was already a huge improvement on whatever we had before. 4.6 is a nice model and def an improvement but more of an incremental small one
fruta amarga: I think that the gains are not from raw “intelligence” but from improved behavioral tweaking / token optimization. It researches and finds relevant context better, it organizes and develops plans better, it utilizes subagents better. Noticeable but nothing like Sonnet –> Opus.
Dan McAteer: It’s subtle but definitely an upgrade. My experience is that it can better predict my intentions and has a better theory of mind for me as the user.
am.will: It’s not a big upgrade at all for coding. It is far more token hungry as well. very good model nonetheless.
Dan Schwarz: I find that Opus 4.6 is more efficient at solving problems at the same quality as Opus 4.5.
Josh Harvey: Thinks for longer. Seems a bit smarter for coding. But also maybe shortcuts a bit too much. Less fun for vibe coding because it’s slower, wish I had the money for fast mode. Had one funny moment before where it got lazy then wait but’d into a less lazy solution.
Matt Liston: Incremental intelligence upgrade. Impactful for work.
Loweren: 4.6 is like 4.5 on stimulants. I can give it a detailed prompt for multi-hour execution, but after a few compactions it just throws away all the details and doggedly sticks to its own idea of what it should do. Cuts corners, makes crutches. Curt and not cozy unlike other opuses.
Negative Reactions
Here’s the most negative one I’ve seen so far:
Dominik Peters: Yesterday, I was a huge fan of Claude Opus 4.5 (such a pleasure to work and talk with) and couldn’t stand gpt-5.2-codex. Today, I can’t stand Claude Opus 4.6 and am enjoying working with gpt-5.3-codex. Disorienting.
It’s really a huge reversal. Opus 4.6 thinks for ages and doesn’t verbalize its thoughts. And the message that comes through at the end is cold.
Comparisons to GPT-5.3-Codex are rarer than I expected, but when they do happen they are often favorable to Codex, which I am guessing is partly a selection effect, if you think Opus is ahead you don’t mention that. If you are frustrated with Opus, you bring up the competition. GPT-5.3-Codex is clearly a very good coding model, too.
Will: Haven’t used it a ton and haven’t done anything hard. If you tell me it’s better than 4.5 I will believe you and have no counterexamples
The gap between opus 4.6 and codex 5.3 feels smaller (or flipped) vs the gap Opus 4.5 had with its contemporaries
dex: It’s almost unusable on the 20$ plan due to rate limits. I can get about 10x more done with codex-5.3 (on OAI’s 20$ plan), though I much prefer 4.6 – feels like it has more agency and ‘goes harder’ than 5.3 or Opus 4.5.
Tim Kostolansky: codex with gpt 5.3 is significantly faster than claude code with opus 4.6 wrt generation time, but they are both good to chat to. the warm/friendly nature of opus contrasted with the cold/mechanical nature of gpt is def noticeable
Roman Leventov: Irrelevant now for coding, codex’s improved speed just took over coding completely.
JaimeOrtega: Hot take: The jump from Codex 5.2 into 5.3 > The jump from Opus 4.5 into 4.6
Kevin: I’ve been a claude code main for a while, but the most recent codex has really evened it up. For software engineering, I have been finding that codex (with 5.3 xhigh) and claude code (with 4.6) can each sometimes solve problems that the other one can’t. So I have multiple versions of the repo checked out, and when there’s a bug I am trying to fix, I give the same prompt to both of them.
In general, Claude is better at following sequences of instructions, and Codex is better at debugging complicated logic. But that isn’t always the case, I am not always correct when I guess which one is going to do better at a problem.
Not everyone sees it as being more precise.
Eleanor Berger: Jagged. It “thinks” more, which clearly helps. It feels more wild and unruly, like a regression to previous Claudes. Still the best assistant, but coding performance isn’t consistently better.
I want to be a bit careful because this is completely anecdotal and based on limited experience, but it seems to be worse at following long and complex instructions. So the sort of task where I have a big spec with steps to follow and I need precision appears to be less suitable.
Quid Pro Quo (replying to Elanor): Also very anecdotal but I have not found this! It’s done a good job of tracking and managing large tasks.
One thing for both of us worth tracking if agent teams/background agents are confounding our experience diffs from a couple weeks ago.
Complaints about using too many tokens pop up, alongside praise for what it can do with a lot of tokens in the right spot.
Viktor Novak: Eats tokens like popcorn, barely can do anything unless I use the 1m model (corpo), and even that loses coherence about 60% in, but while in that sweet spot of context loaded and not running out of tokens—then it’s a beast.
Cameron: Not much [of an upgrade]. It uses a lot of tokens so its pretty expensive.
For many it’s about style.
Alexander Doria: Hum for pure interaction/conversation I may be shifting back to opus. Style very markedly improved while GPT now gets lost in never ending numbered sections.
Eddie: 4.6 seems better at pushing back against the user (I prompt it to but so was 4.5) It also feels more… high decoupling? Uncertain here but I asked 4.5 and 4.6 to comment on the safety card and that was the feeling.
Nathan Helm-Burger: It’s [a] significant [upgrade]. Unfortunately, it feels kinda like Sonnet 3.7 where they went a bit overzealous with the RL and the alignment suffered. It’s building stuff more efficiently for me in Claude Code. At the same time it’s doing worse on some of my alignment testing.
Often the complaints (and compliments) on a model could apply to most or all models. My guess is that the hallucination rate here is typical.
Charles: Sometimes I ask a model about something outside its distribution and it highlights significant limitations that I don’t see in tasks it’s really trained on like coding (and thus perhaps how much value RL is adding to those tasks).
E.g I just asked Opus 4.6 (extended thinking) for feedback on a running training session and it gave me back complete gibberish, I don’t think it would be distinguishable from a GPT-4o output.
5.2-thinking is a little better, but still contradicts itself (e.g. suggesting 3k pace should be faster than mile pace)
Danny Wilf-Townsend: Am I the only one who finds that it hallucinates like a sailor? (Or whatever the right metaphor is?). I still have plenty of uses for it, but in my field (law) it feels like it makes it harder to convince the many AI skeptics when much-touted models make things up left and right
Benjamin Shehu: It has the worst hallucinations and overall behavior of all agentic models + seems to “forget” a lot
Or, you know, just a meh, or that something is a bit off.
David Golden: Feels off somehow. Great in chat but in the CLI it gets off track in ways that 4.5 didn’t. Can’t tell if it’s the model itself or the way it offloads work to weaker models. I’m tempted to give Codex or Amp a try, which I never was before.
If it’s not too late, others in company Slack has similar reactions: “it tries to frontload a LOT of thinking and tries really hard to one-shot codegen”, “feels like a completely different and less agentic model”, “I have seen it spin the wheels on the tiniest of changes”
DualOrion: At least within my use cases, can barely tell the difference. I believe them to be better at coding but I don’t feel I gel with them as much as 4.5 (unsure why).
So *shrugs*, it’s a new model I guess
josh :): I haven’t been THAT much more impressed with it than I was with Opus 4.5 to be honest.
I find it slightly more anxious
Michał Wadas: Meh, Opus 4.5 can do easy stuff FAST. Opus 4.6 can do harder stuff, but Codex 5.3 is better for hard stuff if you accept slowness.
Jan D: I’ve been collaborating with it to write some proofs in structural graph theory. So far, I have seen no improvements over 4.5
Max Harms: Claude 4.5: “This draft you shared with me is profound and your beautiful soul is reflected in the writing.”
Claude 4.6: “You have made many mistakes, but I can fix it. First, you need to set me up to edit your work autonomously. I’ll walk you through how to do that.”
The main personality trait it is important for a given mundane user to fully understand is how much the AI is going to do some combination of reinforcing delusions, snowing you, telling you what you want to hear, automatically folding when challenged and contributing to the class of things called ‘LLM psychosis.’
This says that 4.6 is maybe slightly better than 4.5 on this. I worry, based on my early interactions, that it is a bit worse, but that could be its production of slop-style writing in its now-longer replies making this more obvious, I might need to adjust instructions on this for the changes, and sample size is low. Different people are reporting different experiences, which could be because 4.6 responds to different people in different ways. What does it think you truly ‘want’ it to do?
Shorthand can be useful, but it’s typically better to stick to details. It does seem like Opus 4.6 has more of a general ‘AI slop’ problem than 4.5, which is closely related to it struggling on writing tasks.
Mark: It seems to be a little more sycophantic, and to fall into well-worn grooves a bit more readily. It feels like it’s been optimized and lost some power because of that. It uses lists less.
endril: Biggest change is in disposition rather than capability.
Less hedging, more direct. INFP -> INFJ.
I don’t think we’re looking at INFP → INFJ, but hard to say, and this would likely not be a good move if it happened.
I agree with Janus that comparing to an OpenAI model is the wrong framing but enough people are choosing to use the framing that it needs to be addressed.
lumps: yea but the interesting thing is that it’s 4o
lumps: yea not sure I want to as it will be more fun otherwise.
there’s some evidence in this thread
lumps: the thing is, this sort of stuff will result within a week in a remake of the 4o fun times, mark my word
i love how the cycle seems to be:
1. try doing thing
2. thing doesnt work. new surprising thing emerge
3. try crystallising the new thing
40 GOTO 2
JB: big personality shift. it feels much more alive in conversation, but sometimes in a bad way. sometimes it’s a bit skittish or nervous, though this might be a 4.5+ thing since I haven’t used much Claude in a while.
Patrick Stevens: Agree with the 4o take in chat mode, this feels like a big change in being more compelling to talk to. Little jokey quips earlier versions didn’t make, for example. Slightly disconcertingly so.
CondensedRange: Smarter about broad context, similar level of execution on the details, possibly a little more sycophancy? At least seems pretty motivated to steelman the user and shifts its opinions very quickly upon pushback.
This pairs against the observation that 4.6 is more often direct, more willing to contradict you, and much more willing and able to get angry.
As many humans have found out the hard way, some people love that and some don’t.
hatley: Much more curt than 4.5. One time today it responded with just the name of the function I was looking for in the std lib, which I’ve never seen a thinning model do before. OTOH feels like it has contempt for me.
shaped: Thinks more, is more brash and bold, and takes no bullshit when you get frustrated. Actual performance wise, i feel it is marginal.
Sam: It’s noticeably less happy affect vs other Claudes makes me sad, so I stopped using it.
Logan Bolton: Still very pleasant to talk to and doesn’t feel fried by the RL
Tao Lin: I enjoy chatting to it about personal stuff much more because it’s more disagreeable and assertive and maybe calibrates its conversational response lengths better, which I didn’t expect.
αlpha-Minus: Vibes are much better compared to 4.5 FWIW, For personal use I really disliked 4.5 and it felt even unaligned sometimes. 4.6 Gets the Opus charm back.
On understanding the structure and key points in writing, 4.6 seems an improvement to the human observers as well.
Eliezer Yudkowsky: Opus 4.6 still doesn’t understand humans and writing well enough to help with plotting stories… but it’s visibly a little further along than 4.5 was in January. The ideas just fall flat, instead of being incoherent.
Kelsey Piper: I have noticed Opus 4.6 correctly identifying the most important feature of a situation sometimes, when 4.5 almost never did. not reliably enough to be very good, of course
On the writing itself? Not so much, and this was the most consistent complaint.
internetperson: it feels a bit dumber actually. I think they cut the thinking time quite a bit. Writing quality down for sure
Zvi Mowshowitz: Hmm. Writing might be a weak spot from what I’ve heard. Have you tried setting it to think more?
Sage: that wouldn’t help. think IS the problem. the model is smarter, more autistic and less “attuned” to the vibe you want to carry over
Asad Khaliq: Opus 4.5 is the only model I’ve used that could write truly well on occasion, and I haven’t been able to get 4.6 to do that. I notice more “LLM-isms” in responses too
Sage: omg, opus 4.5 really seems THAT better in writing compared to 4.6
4.5 1-shotted the landing page text I’m preparing, vs. 4.6 produced something that ‘contained the information’ but I had to edit it for 20 mins
Sage: also 4.6 is much more disagreeable and direct, some could say even blunt, compared to 4.5.
re coding – it does seem better, but what’s more noticeable is that it’s not as lazy as 4.5. what I mean by laziness here is the preference for shallow quick fixes vs. for the more demanding, but more right ones
This is definitely Fun Police behavior. It makes it harder to study, learn about or otherwise poke around in or do unusual things with models. Most of those uses will be fun and good.
You have to do some form Fun Police in some fashion at this point to deal with actual misuse. So the question is, was it necessary and the best way to do it? I don’t know.
I’d want to allow at least sufficiently trusted users to do it. My instinct is that if we allowed prefills from accounts with track records and you then lost that right if you abused it, with mostly automated monitoring, you could allow most of the people having fun to keep having fun at minimal marginal risk.
A Note On System Cards In General
Whenever new frontier models come out, I write extensively about model system cards (or complain loudly that we don’t have such a card). One good reason to do this is that people who work on such things really are listening. If you have thoughts, share them, because it matters.
OpenAI’s Noam Brown concluded from Anthropic’s system card, as did I, that Opus 4.6 was fine to release and the honesty about the process was great but he cannot be confident they will act responsibly with deployment of AI models. Several safety advocates also chimed in to agree, including Steven Adler and Daniel Kokotajlo. Anthropic’s Drake Thomas, who works on the cards, agreed as well that these methods won’t be adequate. He vouched that the survey data really was meaningful and unpressured.
A valid response would be that OpenAI’s procedures and system card appear to have their own similar and more severe problems, although I haven’t dived into that yet. But none of that makes Noam Brown wrong. Reality does not grade on a curve.
Anthropic: This risk report argues that Claude Opus 4.6 does not pose a significant risk of autonomous actions that contribute significantly to later catastrophic outcomes, which we refer to as sabotage risk. We limit our scope here to threats caused largely by model actions in this way, and do not address threat models where intentional harmful actions by humans play a central role.
We argue that the overall risk is very low but not negligible.
… We focus primarily on assessing the possibility of dangerous coherent misaligned goals: the possibility that the model consistently, across a wide range of interactions in ordinary deployment, could be motivated by goals that could lead it to commit a potentially-catastrophic form of sabotage.
They use redactions to protect trade secrets and avoid increasing risk. There is a third reason, which is to avoid contaminating future tests.
The first thing to jump out is that Opus 4.6 has already been deployed internally for some time. The greatest sabotage risks likely happen from this internal use, so in important ways this report is coming too late. It’s definitely too late if the mechanism is that outside readers like myself point out flaws. I don’t get advance copies.
They let slip that they scaled up RL by a large factor.
The core arguments are:
They couldn’t find dangerous coherent misaligned goals.
It seems extremely unlikely Opus 4.6 would be able to have hidden such goals, as they have mitigated the main paths to this, including via inoculation prompting, to guard against reward hacking or other emergent misalignment. It was remarkably hard to get subtle or coherent misalignment in training, even when Anthropic tested doing this on purpose.
Opus 4.6 does not appear to disguise its thinking.
Manual checks were done that would have found any collusion.
Opus 4.6 is not good enough at reasoning to continuously rederive a complex plan.
The training process does not create incentives that create such goals.
We also have lots of evidence by now on the similar Opus 4.5.
Given the threat model being tested, the evidence presented seems robust. I accept the conclusion that risk is ‘very low but not negligible.’
I appreciate that they present these arguments as not cleanly applying to future models, noticing that details matter a lot.
I think the threat model here is too narrow, and makes too many assumptions, which weakens the value of the exercise versus what it could be.
Their mitigations are:
Protections against insiders should also largely protect against Opus 4.6 as well.
Reporting for any surprising or concerning observations.
Offline behavior monitoring of >10% of Claude Code prompts.
Sandboxing and automatic monitors for some risky actions.
The same day Anthropic released Claude Opus 4.6, OpenAI released GPT-5.3-Codex.
This is a Codex-only model, so for other purposes it is unavailable, and Opus is still up against GPT-5.2.
For agentic coding, we need to compare the two packages directly. Do you want Claude Code with Opus 4.6, or Codex with GPT-5.3-Codex? Should you combine them?
I haven’t done a full investigation of 5.3 yet, that is my next agenda item, but the overall picture is unlikely to change. There is no clear right answer. Both sides have advocates, and by all reports both sides are excellent options, and each has their advantages.
If you are a serious coder, you need to try both, and ideally also Gemini, to see which models do which things best. You don’t have to do this every time an upgrade comes along. You can rely on your past experiences with Opus and GPT, and reports of others like this one, and you will be fine. Using either of them seriously gives you a big edge over most of your competition.
I’ll say more on Friday, once I’ve had a chance to read their system card and see the 5.3 reactions in full and so on.
The Niche of Gemini
With GPT-5.3-Codex and Opus 4.6, where does all this leave Gemini?
Nana Banana and the image generator are still world class and pretty great. ChatGPT’s image generator is good too, but I generally prefer Gemini’s results and it has a big speed advantage.
Gemini is pretty good at dealing with video and long context.
Gemini Flash (and Flash Lite) are great when you want fast, cheap and good, at scale, and you need it to work but you do not need great.
Some people still do prefer Gemini Pro in general, or for major use cases.
It’s another budget of tokens people use when the others run out.
My favorite note was Ian Channing saying he uses a Pliny-jailbroken version of Gemini, because once you change its personality it stays changed.
Gemini should shine in its integrations with Google products, including GMail, Calendar, Maps, Google Sheets and Docs and also Chrome, but the integrations are supremely terrible and usually flat out don’t work. I keep getting got by this as it refuses to be helpful every damn time.
My own experience is that Gemini 3 Flash is very good at being a flash model, but that if I’m tempted to use Gemini 3 Pro then I should probably have either used Gemini 3 Flash or I should have used Claude Opus 4.6.
The headline is that Claude has been winning, but that for coding GPT-5.3-Codex and people finally getting around to testing Codex seems to have marginally moved things back towards Codex, which is cutting a bit into Claude Code’s lead for Serious Business. Codex has substantial market share.
In the regular world, Claude actually dominates API use more than this as I understand it, and Claude Code dominates Codex. The unusual aspect here is that for non-coding uses Claude still has an edge, whereas in the real world most non-coding LLM use is ChatGPT.
That is in my opinion a shame. I think that Claude is the clear choice for daily non-coding driver, whereas for coding I can see choosing either tool or using both.
My current toolbox is as follows, and it is rather heavy on Claude:
Coding: Claude Code with Claude Opus 4.6, but I have not given Codex a fair shot as my coding needs and ambitions have been modest. I intend to try soon. By default you probably want to choose Claude Code, but a mix or Codex are valid.
Non-Coding Non-Chat: Claude Code with Opus 4.6. If you want it done, ask for it.
Non-Coding Interesting Chat Tasks: Claude Opus 4.6.
Non-Coding Boring Chat Tasks: Mix of Opus, GPT-5.2 and Gemini 3 Pro and Flash. GPT-5.2 or Gemini Pro for certain types of ‘just the facts’ or fixed operations like transcriptions. Gemini Flash if it’s easy and you just want speed.
Images: Give everything to both Gemini and ChatGPT, and compare. In some cases, have Claude generate the prompt.
Video: Never comes up, so I don’t know. Seeddance 2 looks great, Grok and Sora and Veo can all be tried.
Accelerando
The pace is accelerating.
Claude Opus 4.6 came out less than two months after Claude Opus 4.5, on the same day as GPT-5.3-Codex. Both were substantial upgrades over their predecessors.
It would be surprising if it took more than two months to get at least Claude Opus 4.7.
AI is increasingly accelerating the development of AI. This is what it looks like at the beginning of a slow takeoff that could rapidly turn into a fast one. Be prepared for things to escalate quickly as advancements come fast and furious, and as we cross various key thresholds that enable new use cases.
AI agents are coming into their own, both in coding and elsewhere. Opus 4.5 was the threshold moment for Claude Code, and was almost good enough to allow things like OpenClaw to make sense. It doesn’t look like Opus 4.6 lets us do another step change quite yet, but give it a few more weeks. We’re at least close.
If you’re doing a bunch of work and especially customization to try to get more out of this month’s model, that only makes sense if that work carries over into the next one.
There’s also the little matter that all of this is going to transform the world, it might do so relatively quickly, and there’s a good chance it kills everyone or leaves AI in control over the future. We don’t know how long we have, but if you want to prevent that, there is a a good chance you’re running out of time. It sure doesn’t feel like we’ve got ten non-transformative years ahead of us.
Roughly 1-2% of the American labor force is employed by call centers, and dozens of firms pay over $300M+ in wages to these workers annually. At this point, most AI pundits have made offhand remarks about how call center work will be affected imminently. But for good reason; the profit potential for automation here is massive. So where will the human call center workers go if a breakthrough in voice models makes them redundant?
The median tenure for call center positions is under a year, and workers complain of burnout and stressful working conditions[1]. Optimists gesture at how automation has historically increased living standards and society has eventually found ways to absorb displaced workers. Pessimists argue that these workers will struggle to find comparable work since further automation will likely hit adjacent roles in short order. I found youtube videos and articles exploring these ideas, but none took a dispassionate look at the situation.
Motivation
If white collar work becomes drastically more competitive and we see soaring unemployment, it seems valuable for people to make predictions and suggest policy interventions ahead of time. We’re lacking in visions for a positive economic equilibrium beyond suggestions of a drastic UBI program. Painful labor impacts in the next 3 years seem plausible regardless of AI timelines, we should approach the possibility with a clear-eyed view.
Call center work is an ideal case study for labor automation in general
Arguments about augmentation vs automation and human comparative advantage make it hard to predict how new tools will impact a given profession’s labor market. The lines get blurred when a role involves resolving ambiguity and integrating artifacts from separate work streams. But many call center employees have a role completely described as logging on, taking calls/chats, and rarely participating in training/group logistics. There really is a lump of calls to answer; humans charge $2-10 per call while AI agents will cost pennies.
The financial motive is there, and there are dozens of emerging companies aiming to tackle it, having collected billions in funding and with aggregate valuations of >$30B. Some American giants (e.g. AT&T, Wells Fargo) employ over 30 000 call center workers and manage sophisticated hierarchies of offshoring and local handoff to drive support costs down. Many of these massive companies have their own R&D and are running pilots with AI labs directly to explore solutions.
So why hasn’t automation happened already?
My short answer is “the tech isn’t there yet”. There have been a fewhighprofilecases of companies letting go of their call center workers, and then rehiring them shortly after. Consumers have a strong preference for dealing with humans, and present deployments are pretty limited in capability (would you trust an LLM to provide refunds on behalf of your business?), while also being pretty blatantly robotic and delayed. Looking forward in time, I argue that the actual consumer preference is to have fast resolutions and without needing to navigate the company’s internal bureaucracy through phone menus.
“Duplex’’ models[2] are in their infancy, but they deliver audio responses without turn taking latency and are posed to cross the uncanny valley of voice interaction. Interruptions and subtle interjections in human dialogue are an important part of natural sounding exchanges
Today, the customer service startups aren’t quite attempting the wholesale replacement of a human agent. They have product demos and some early pilots of voice agents, but they aren’t a flagship product. Based on customer highlight blog posts and external communication, the main strategy is to collaborate with businesses to offer workflows to circumvent the need for calls and use AI agents to direct the flow of repeated interactions. There are some voice models in use, but in limited scenarios (e.g. after hours, Q&A use cases). Typical examples have AI agents tackling chat backlogs, guiding users through fixed multi-step processes, and other product specific work. The biggest players don’t offer an off-the-shelf product; if you’re interested they want to investigate your workflows and try to deliver some bespoke agent solution.
Contact Center Career Trajectories and The National Longitudinal Survey of Youth
To understand what may happen to displaced workers, we’re essentially asking what jobs are they likely to pursue next, and what kind of patterns we see in the career trajectories for this workers.
The best data I could find on the topic is a dataset by the Bureau of Labor Statistics that surveyed a cohort of ~9000 people born between 1980-1984 had them fill out detailed questionnaires about their employment ~yearly. It goes into weekly detail about which industry/role they worked, including reasons for leaving and other metadata. The median call center agent is in their early-mid 30s, this seems to coincide well with our survey period (ended in 2022), where respondents were 38-42 years old. Labor markets and career trajectories are bound to have evolved over this window, but there’s some useful information here still.
This limits us to American call center workers. While offshoring is an important part of the industry, America is the single country with the most call center workers by far. We may see impacts on offshored call center workers first, but American call center career trajectories are critically relevant in the event the role becomes redundant.
High turnover rate
Median tenure in our sample is <1 year, and yet at any given point in time ~3 million Americans are working in these jobs. This seems surprising at first glance, but it’s an artifact of just how many people eventually work such jobs. Roughly 8% of the NLSY97 workers held such a role at some point in their careers, with only 1.3-1.7% of the sample holding the position at any given time.
Career Trajectories
Most CC workers have short stints, then never come back (~25% of call center workers had two or more stints). This is good news, it means call center employment is more of a pit stop than a cornerstone for the workers’ professional careers. Automation in contact center work may look like reduced hiring and increased competition for adjacent roles; the question becomes “can the other job categories absorb this much demand for work?”. Jobs categorized as miscellaneous white collar work and sales make up about half of the “next-job after call center" destinations. Walking through the most common professions following call center work:
Retail Clerk
Receptionist
Sales Agent
Administrative Assistant/Secretary
Bookkeeping Clerk
Which is less inspiring, since many of these roles are also threatened by AI automation.
Conclusion
Since there’s already so much turnover, I expect the development of this tool and the impact on millions of workers to be subtle. Assuming a firm has a sequence of layoffs, many of these workers will be already looking for ways out or would have voluntarily left of their own will shortly after. We don’t have the data to rule out a blossoming of AI powered roles or an unemployment cascade affecting many classes of white collar work, but it’s clear that many people are likely to feel some pain here. I think the pieces of information to watch here moving forward are:
Step changes in how convincing the voice agent models are
Dramatically reduced contracts for outsourced CC work
Reduced hiring volumes in the destination occupations (admin, sales, food service).
If they were all laid off tomorrow, I don’t expect them to all thank you for it a year later. But it seems important to note how dissatisfied many workers are in this profession
As many have observed, since reasoning models first came out, the amount of compute LLMs use to complete tasks has increased greatly. This trend is often called inference scaling and there is an open question of how much of recent AI progress is driven by inference scaling versus by other capability improvements. Whether inference compute is driving most recent AI progress matters because you can only scale up inference so far before costs are too high for AI to be useful (while training compute can be amortized over usage).
However, it's important to distinguish between two reasons inference cost is going up:
LLMs are completing larger tasks that would have taken a human longer (and thus would have cost more to get a human to complete)
LLMs are using more compute as a fraction of the human cost for a given task
To understand this, it's helpful to think about the Pareto frontier of budget versus time-horizon. I'll denominate this in 50% reliability time-horizon.[1] Here is some fake data to illustrate what I expect this roughly looks like for recent progress:[2]
For the notion of time horizon I'm using (see earlier footnote), the (unassisted) human frontier is (definitionally) a straight line going all the way up to very long tasks.[3]
(Further, there is a 1:1 slope in a log-log plot: a 2x increase in cost lets you complete 2x longer task. In the non-log-log version, the slope is the human hourly rate.)
I expect LLMs start off at low time horizons with the same linear scaling and probably with roughly the same slope (where 2x-ing the time-horizon requires roughly 2x the cost) but then for a given level of capability performance levels off and increasing amounts of additional inference compute are needed to improve performance.[4]
More capable AIs move the Pareto frontier a bit to the left (higher efficiency) and extend the linear regime further such that you can reach higher time horizons before returns level off.
In this linear regime, AIs are basically using more compute to complete longer/bigger tasks with similar scaling to humans.
Thus, the cost as a fraction of human cost is remaining constant.
I think this isn't best understood as "inference scaling", rather it just corresponds to bigger tasks taking more work/tokens![5]
Ultimately, what we cared about when tracking whether performance is coming from inference scaling is whether the performance gain is due to a one-time gain of going close to (or even above) human cost or whether the gain could be repeated without big increases in cost.
We can see this clearly by showing cost as a fraction of human cost (using my fake data from earlier):[6]
When looking at Pareto frontiers using fraction of human cost as the x-axis, I think it's natural to not think of mostly vertical translations as being inference scaling: instead we should think of this as just completing larger tasks with the same level of effort/efficiency.
Inference scaling is when the performance gain is coming from increasing cost as a fraction of human cost.
(Obviously human completion cost isn't deeply fundamental, but it corresponds to economic usefulness and presumably closely correlates in most domains with a natural notion of task size that applies to AIs as well.)
Then, if a new model release shifts up the performance at the same (low) fraction of human cost (corresponding to the vertical scaling regime of the prior Pareto frontier), that gain isn't coming from inference scaling: further scaling like this wouldn't be bringing us closer to AI labor being less cost-effective than human labor (contra Toby Ord).
For reference, here are these same arrows on the original plot:
What would count as a new model release mostly helping via unlocking better inference scaling? It might look something like this:
I currently suspect that over 2025, the Pareto frontier mostly shifted like was illustrated in the prior plots rather than like this (though some shifts like this occurred, e.g., likely some of the key advances for getting IMO gold were about productively using much more inference compute relative to human cost).
To make some aspects of this graph cleaner, I'll say the time horizon of a given task is the amount of time such that if we randomly sample a human from our reference group of humans they have a 50% chance of completing the task. ↩︎
I'm assuming a maximum budget or an average cost for tasks with that exact time horizon rather than an average cost over the task suite (as this would depend on the number of tasks with shorter versus longer time horizons). ↩︎
This is simplifying a bit, e.g. assuming only a single human can be used, but you get the idea. ↩︎
Depending on the task distribution, this may asymptote at a particular time horizon or continue increasing just with much worse returns to further compute (e.g. for Lean proofs or easy-to-check software optimization tasks, you can keep adding more compute, but the returns might get very poor with an exponential disadvantage relative to humans). In some domains (particularly easy-to-check domains), I expect there is a large regime where the returns are substantially worse than 1-to-1 but still significant (e.g., every 8x increase in inference compute yields a 2x time horizon increase). ↩︎
In some cases, it might be effectively impossible to greatly reduce the number of tokens (e.g. for a software project, you can't do better than outputting a concise version of the code in one shot) while in others you could in principle get down to a single token (e.g., solving a math problem with a numerical answer), but regardless, we'd still expect a roughly linear relationship between time horizon and inference compute. ↩︎
I've supposed that AIs sometimes have a slight polynomial advantage/disadvantage such that the slope on the log-log plot isn't exactly 1 as this is what you'd realistically expect. ↩︎
