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The underlying account relied on unnamed researchers and comments from former OpenAI chief scientist Ilya Sutskever. OpenAI’s publicly documented o1 work simultaneously showed a different route to better results: spending more computation after a prompt arrives, allowing a model to generate and evaluate multiple lines of reasoning. For businesses and investors, the important change is economic as much as technical: the industry may be shifting from ever-larger training runs toward better data, specialized systems, inference capacity, and measurable cost per completed task.
What the November 2024 report actually said
Futurism’s November 12, 2024 headline summarized reporting that OpenAI was seeing smaller gains from its next flagship model than it had seen when moving from GPT-3 to GPT-4. Secondary reports described that model as Orion. Unnamed OpenAI researchers reportedly saw little or no reliable improvement on some tasks, with coding cited as one area where gains might be limited. The Information’s underlying reporting was not accompanied by a public technical evaluation that readers could reproduce.
That makes the careful formulation “reported weaker returns” rather than “Orion failed.” The public record does not establish whether Orion was released under that name, its final benchmark results, the size or cost of its training run, or whether the alleged plateau continued through later model generations. See the contemporaneous coverage from Futurism, Yahoo News Australia, and Ars Technica.
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What “diminishing returns” means in AI
In economics, diminishing returns means that adding more of one input while holding other inputs fixed produces progressively smaller additions to output. In the older AI scaling approach, the inputs included model size, training tokens, accelerator hours, and training duration.
Scaling laws are empirical relationships: over particular ranges and training setups, more compute has generally improved a model’s loss or capabilities. They do not promise equal-sized capability jumps, commercial value at every spending level, or a permanent relationship after architecture, data, and objectives change. A hard wall would mean additional investment no longer produces meaningful improvement. The available public evidence did not establish that stronger claim.
Why the old “make it bigger” strategy may weaken
High-quality data is difficult to expand
More tokens are not automatically more useful tokens. Deduplicated text, licensed material, expert demonstrations, domain records, multimodal data, reinforcement-learning traces, and synthetic examples have different value. Once the most useful public material has been heavily used, adding repetitive, noisy, or poorly filtered data can deliver less benefit. “The industry is running out of data” is therefore too broad unless it specifies which kind of data is scarce.
Benchmarks can stop revealing practical progress
A model can improve in a real workflow while moving little on a test that is near saturation. Conversely, a higher score can reflect repeated sampling, answer selection, contamination, or narrow optimization for a known format. Small differences may also fall within evaluation noise. Serious comparisons need adversarial tests and ordinary customer tasks, not one headline number.
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Training objectives and architecture matter
Larger runs can expose weaknesses in optimization, reasoning objectives, memory, retrieval, or tool use. Broad internet pretraining may not translate proportionally into factuality, planning, reliable code execution, or completion of a business process. Synthetic data can help, but if generated examples contain errors or reduce diversity, filtering and provenance become essential.
The economics can deteriorate before capability stops improving
An extra capability point is not automatically worth an extra billion-dollar training run. A modestly better model may be commercially attractive if it reduces human labor, improves reliability, or enables a new product. A larger benchmark gain may be unattractive if serving it is slow, expensive, difficult to verify, or quickly copied by competitors.
What Sutskever’s warning adds—and what it does not
Reuters reported that Ilya Sutskever, who left OpenAI in 2024, said results from scaling pretraining had plateaued and described the field as moving beyond the 2010s scaling era into an “age of wonder” requiring new ideas. His experience makes the warning influential, but he was speaking as a former OpenAI leader, not issuing an official company statement that progress had ended. The Reuters account is available through Investing.com’s republication.
The alternative scaling axis: compute while answering
Training-time compute updates model parameters before deployment. Inference-time or test-time compute is spent after a user submits a prompt. A reasoning model can generate several candidate solutions, check them, rank them, and revise before returning an answer.
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OpenAI’s o1 research presented this approach as a way to spend more time on difficult problems rather than relying only on a larger pretrained base. In OpenAI’s reported 2024 AIME mathematics evaluation, GPT-4o averaged 1.8 of 15 points (about 12%). o1-preview averaged 11.1 (about 74%) with one sample, 12.5 (about 83%) when consensus was taken across 64 samples, and 13.9 (about 93%) with a 1,000-sample reranking setup. Those figures are OpenAI’s results under its stated conditions, not proof of general intelligence; the multi-sample results consume substantially more inference compute than an ordinary single response. Read the methodology at OpenAI’s “Learning to reason with LLMs”.
This trade-off changes the bottleneck. Training may become less dominant while serving demand rises. Longer reasoning can mean higher latency, larger bills, more accelerator capacity, less predictable per-request costs, and no guarantee against factual errors or misunderstanding the task.
Does this disprove AI scaling laws?
No. It challenges a simplistic interpretation that every additional tranche of data and compute should produce a similarly large, general improvement. Progress can come from algorithms, hardware utilization, quantization, distillation, batching, caching, retrieval, memory, tools, multimodal learning, robotics, and domain-specific fine-tuning. A smaller model with the right retrieval system or tools can beat a much larger general model on a defined workflow.
OpenAI’s discussion of algorithmic and infrastructure gains illustrates why capability per dollar matters alongside raw model size; see “AI and efficiency”. A frontier model can stop growing while the cost and reliability of useful answers continue to improve.
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Why this matters to investors and business decision-makers
Capital may move from training toward inference
If frontier pretraining yields smaller marginal gains, companies may direct more money toward proprietary data, evaluation, post-training, reasoning capacity, inference hardware, and software efficiency. A model that reasons longer can reduce the need for an ever-larger base model while increasing data-center utilization when customers use it.
Capability and profitability can diverge
Investors should separate technical progress from returns on capital. Useful questions include:
- How many dollars of training or serving cost produce one measurable improvement?
- What is the cost per successfully completed customer task, not merely per token?
- Does added reasoning reduce labor, errors, or cycle time enough to justify its latency?
- Can customers verify outputs and pay for the result?
- Does a gain transfer from a benchmark to production workflows?
Efficiency can be an investment thesis
Software optimization, better hardware utilization, quantization, caching, and smaller specialized models can improve margins even if headline model sizes plateau. Conversely, a reasoning-heavy product may need more serving capacity and energy, making infrastructure availability and electricity costs relevant to its economics.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge whether the slowdown thesis is holding
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- Capability: Successive frontier models deliver smaller gains on difficult, independently designed tasks.
- Reliability: Error rates, tool-use failures, and performance under adversarial testing improve—or fail to improve—in real workflows.
- Efficiency: Providers achieve the same result with fewer tokens, chips, or seconds.
- Economics: Customers accept the additional price and latency of inference-heavy models.
- Generalization: Gains survive unfamiliar tasks, contamination checks, and production evaluation.
Greater use of proprietary or synthetic data, test-time reasoning, specialized models, and efficiency techniques would indicate a change in strategy. Customer willingness to pay is the decisive commercial test.
What the evidence does not justify
- OpenAI admitted that its models stopped improving.
- Orion definitively failed or was never useful.
- The world has run out of training data.
- Scaling laws have been disproven.
- o1 solved the scaling problem or guarantees artificial general intelligence.
- A reported slowdown by itself proves that AI valuations or investment will collapse.
The strongest supported conclusion is narrower: reporting in late 2024 suggested that simply increasing conventional pretraining scale was becoming less predictably rewarding, while OpenAI and its competitors were exploring other ways to spend compute.
What this means for choosing an AI platform
For a company evaluating models, the relevant comparison is cost per successful outcome. Test a fast model against a reasoning model on representative tasks, record latency and failure rates, and include retries, tool calls, retrieval, and human review in the calculation. A reasoning model is a poor fit when response time, predictable cost, offline operation, or control over model weights is mandatory.
Possible deployment routes include the OpenAI API, Azure AI Foundry, Amazon Bedrock, Google Vertex AI, the Anthropic API, or self-hosted infrastructure such as NVIDIA AI Enterprise. Prices, model availability, regions, and contract terms change; verify current terms before committing. The right choice depends on latency, privacy, data residency, portability, workload steadiness, and the maximum acceptable cost per completed task.
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The November 2024 story described a possible slowdown in returns from conventional pretraining, supported by unnamed-source reporting and a former OpenAI scientist’s warning. It did not demonstrate an end to AI scaling or progress. The more durable shift is toward combining better data and algorithms with inference-time reasoning, specialized systems, and efficiency—and judging each advance by the value it delivers per dollar.
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