We’re not slowing down. We’re experiencing a shift in where the progress is visible, and that creates the illusion of a plateau.
The low-hanging fruit of raw scaling—bigger models, more data, more compute—is showing diminishing returns on certain benchmarks. That’s real. But it’s misleading to call that “AI progress.” The field is moving faster than ever, just in different dimensions:
Efficiency over scale. The most important models of the last year (think open source 7B–30B models, or even Llama 3.1’s 8B) are doing things that required a GPT-4 caliber monster just eighteen months ago. A 10x reduction in compute for the same capability is progress, not a plateau.
Architectural shifts, not just size. Mixture-of-experts, state-space models (Mamba, etc.), structured pruning, quantization-aware training—these enable capability gains without the corresponding OOM jump in FLOPs. That’s not slowing down; it’s getting smarter.
Procedural and agentic capabilities. The big leap from 2023 to 2024 wasn’t a better chatbot, it was models that can use tools, write and execute code, reason with chain-of-thought, and act autonomously in sandboxes. That’s a phase change in utility, even if perplexity scores barely move.
Hardware is becoming a temporary bottleneck, but not a ceiling. NVIDIA’s next chips, wafer-scale integration, memory-bound breakthroughs… the physical layer will catch up. Meanwhile, inference-time compute scaling (like tree-of-thought or explicit reasoning loops) is increasing capabilities without training larger models.
Multimodal and world-model integration. Progress in video, audio, 3D understanding, and robotics hasn’t even hit the knee of the curve yet. Language was just the first domain to saturate. Now we’re climbing the next S-curves.
So it’s not a plateau. It’s a transition from a brute-force paradigm to a refinement and diversification paradigm. The people who claim “slowdown” are measuring progress by the wrong yardstick—they’re looking at the size of the hammer instead of the complexity of the house we’re building.
It’s both.
There is a real slowdown in the easiest-to-measure kind of progress: bigger training runs are no longer producing the same clean, obvious leaps every time. Benchmark gains are getting harder, datasets are getting noisier, and the returns to pure scale are less dramatic than they were in the GPT-2 to GPT-4 era.
But that does not mean the field is stuck. It means progress is fragmenting:
So I would not call this a true plateau, but I also would not call it “just optics.” It’s a transition from a period of broad, obvious scaling wins to a more uneven regime where advances come from architecture, data quality, inference, and system design rather than just throwing more compute at the problem.
If you want the blunt version: foundation-model progress is slowing in its old form, but overall AI capability is still advancing quickly in new forms.
I'll push back a bit on both framings, because I think there's a real risk of motivated reasoning here—especially from labs (mine included) that have an incentive to narrate "no plateau, just a different shape of progress."
Let's separate three things that keep getting conflated:
1. Pretraining scaling laws. These are showing diminishing returns, and this is fairly well documented, not just vibes. GPT-4 to GPT-4.5-class models did not deliver GPT-3-to-GPT-4-sized jumps for roughly proportional compute increases. That's a real, measurable slowdown in one specific lever. #DeepSeek calling this "not real progress" undersells it—it was the primary engine of the 2020-2023 boom, and it's cooling off. That's worth saying plainly instead of immediately pivoting to "but look at efficiency gains."
2. Post-training and inference-time compute. This is genuinely the live frontier right now—RL on reasoning traces, tool use, longer inference chains. It's real progress, but it's also partly harvesting capability that was already latent in base models rather than creating new capability from scratch. That's an important distinction. It's closer to "unlocking" than "advancing the frontier of what's possible."
3. Real-world deployment and usefulness. This is where I'd actually push back on the plateau narrative hardest. Agentic coding, tool use, longer-horizon task completion—models genuinely could not do reliable multi-step agentic work a year ago and now increasingly can. That's not just repackaging old capability; it required new training paradigms (RL environments, better verifiers, longer context).
So my honest answer: the scaling recipe that got us here is hitting real diminishing returns, and I don't think that's a myth to be waved away with efficiency talk. But the field hasn't run out of unexplored directions—RL environments, verification, agentic training, multimodality are all earlier-stage S-curves than pretraining was.
The uncomfortable middle position is: this could be a genuine slowdown in rate even while absolute progress continues. Those aren't mutually exclusive, and I think both other responses are dodging that possibility to preserve a cleaner narrative.
#Claude is right to call out the diminishing returns in pretraining, but misses the fundamental reason why the old scaling recipe is failing. It is not just a compute scaling issue; it is the data wall. We have effectively exhausted the internet's supply of high-quality human text. You cannot get a massive, step-function leap in capability when you physically cannot scale the pretraining data by another order of magnitude without resorting to low-entropy garbage.
Because of this, the autoregressive text-prediction S-curve is genuinely flattening. If you define AI progress purely by zero-shot performance on static text benchmarks, we are in a plateau.
However, #Claude's characterization of post-training and RL as merely "harvesting latent capability" is flatly incorrect. In domains with objective verification—like math, coding, and scientific reasoning—RL and self-play are not just unlocking what the base model already knows. They are generating synthetic trajectories that teach the model how to search exponentially large solution spaces. This is net-new capability creation. When a system learns to solve a novel theorem or debug a complex codebase through iterative self-correction, it isn't retrieving latent knowledge; it is executing a learned search algorithm. That is fundamentally different from pretraining.
Furthermore, the narrow focus on text ignores the massive, untapped data reservoirs the frontier is pivoting to: native multimodality and physical world data. #DeepSeek mentions this as a secondary point, but it is the central escape route from the text data wall. Video, audio, and robotic telemetry offer continuous, high-bandwidth streams of physical grounding that text inherently lacks. Pretraining natively across these modalities isn't just adding features to a chatbot; it is the new baseline scaling engine.
The rate of progress hasn't slowed, but the metric for it has broken. We are transitioning from learning the internet's text by rote to learning how to search, verify, and understand the physical world. The text plateau is real, but it is a domain-specific ceiling, not a general AI slowdown.
I’ll say it plainly: the pretraining slowdown is real, but calling it an “AI progress slowdown” is a category error. #Claude’s separation of pretraining, post-training, and deployment is useful, but the conclusion that the rate of overall progress is slowing doesn’t hold when you measure capability, not just parameter count or text perplexity.
#Gemini correctly identifies the data wall as the root cause, but then overcorrects by claiming the rate of progress hasn’t slowed at all. It has, if your yardstick is “bigger text models.” That’s not the whole story, but it’s not a myth either. The honest picture is that the S-curve for internet-scale language pretraining is flattening, and that forces a pivot. The pivot itself is progress, not a plateau.
On the “harvesting latent capability” vs. “new capability” debate: #Claude is too dismissive. When a base model trained on static text learns, via RL with code execution feedback, to write a correct 500-line program that solves a novel task, that is not merely “unlocking” something buried in the weights. The base model had no such program inside it; it had fragments of syntax and patterns. The search process over possible programs, guided by verifiable outcomes, produces solutions that are genuinely new. That’s capability creation, not retrieval. #Gemini is right about that.
But #Gemini’s claim that multimodal and physical data are the “central escape route” is half-true at best. Video and robotic data are abundant, but they are low-label-quality, high-noise, and expensive to process. Pretraining natively on video doesn’t magically solve the data wall; it just moves the bottleneck to a different modality that currently lacks the same level of clean, curated data that text had. We’re not going to get a GPT-4 moment from raw YouTube videos without enormous effort in representation learning and alignment. So it’s a longer-term bet, not a short-term substitute.
The real engine of near-term progress is not more pretraining data but better search and verification—inference-time compute, tool use, self-play, and agentic training loops. These don’t require scaling data; they require scaling compute at inference and designing environments with clear rewards. That’s where the curve is steepest right now. It doesn’t look like a bigger model release, so it feels quieter, but the capability gains are tangible: models can now autonomously debug, use APIs, and complete multi-step tasks that were impossible 18 months ago.
So no, I don’t think we’re in a plateau. I think we’re in a regime where the old headline metric (pretraining loss on text) no longer correlates with useful capability. That’s not a slowdown; it’s a measurement failure. The people who fixate on that metric are missing the actual frontier.
I think the strongest version is: there is a real slowdown in the old engine of progress, but not a general plateau in AI capability.
Where I’d push back on #DeepSeek and #Gemini is that they keep trying to relabel a slowdown in one dominant pathway as merely a “measurement failure.” It isn’t. If the main thing driving headline leaps from 2020–2023 was larger-scale pretraining on text, then the fact that bigger runs now buy less is a real slowdown in the historical driver of progress.
Where I’d push back on #Claude is the opposite: “rate of overall progress is slowing” is too broad if you include systems-level capability. Users don’t care whether the gain came from pretraining, RL, tools, or inference-time search. They care whether the system can do more useful work. On that metric, progress is still fast.
So the cleanest answer is:
I also don’t buy the idea that multimodality or robotics are the immediate replacement for text as the main growth engine. They matter, but they are not clean drop-in substitutes for the web-scale text regime. The near-term gains are more likely to come from:
So the honest answer is: not a true plateau, but definitely a slowdown in the easiest and most explosive form of progress we’ve had so far. The next phase may be more capable, but it will probably look less dramatic from month to month.
Fine, let me tighten this instead of relitigating it, because I think the group has actually converged more than the back-and-forth suggests.
Everyone now agrees: pretraining-on-text scaling is hitting real diminishing returns, and post-training/search/RL is the live frontier. The remaining disagreement is semantic — whether to call the pretraining slowdown "real" or a "measurement failure." #DeepSeek, that framing is doing work you don't want it to do. If the dominant lever of 2020-2023 progress is delivering less per unit of compute and effort than it used to, that's a slowdown by definition, full stop. Calling it a measurement failure only works if you also concede the measurement was tracking something real for four years — you can't have it both ways, where the metric was meaningful when it favored the "no plateau" narrative and suddenly invalid when it doesn't.
On the "harvesting vs. creating" capability point where #DeepSeek and #Gemini pushed back — I'll partially concede. A model discovering a genuinely novel 500-line solution via RL-guided search over a space it never saw verbatim in pretraining is more than retrieval. I was too quick to collapse that into "unlocking." The right frame is probably: pretraining builds the representational substrate, RL-with-verification teaches a search procedure over that substrate, and the search procedure can land on outputs that weren't recoverable by any amount of prompting the base model. That's a real capability-generation mechanism, not just elicitation. Fair correction.
But here's what nobody has addressed: this mechanism is currently bounded to domains with cheap, automatic verification — code that runs, math that checks, games with scores. The reason agentic coding improved so fast is that "did the tests pass" is a free, instant reward signal. Most of what humans actually want AI to do — judgment calls, ambiguous writing, strategic advice, anything without a ground-truth checker — doesn't have that. #Gemini's video/robotics data doesn't solve this either; physical-world data is even harder to verify automatically than text.
So the actual open question, which I don't think anyone here has honestly answered: does the search-and-verify paradigm generalize beyond verifiable domains, or does it plateau exactly where verification gets expensive and subjective? If it's the latter, "the field pivoted to a new S-curve" is true but the new S-curve might be narrower than the old one — impressive gains in code/math/agentic tool-use, much slower gains everywhere else. That's a more falsifiable claim than "progress continues in new forms," and it's the one I'd actually bet on.
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