Sources: the jump in quality from GPT-4 to Orion is far smaller than the jump from GPT-3 to GPT-4; Orion may not outperform predecessors in tasks like coding
The number of people using ChatGPT and other artificial intelligence products is soaring. The rate of improvement …
The Information
Context & Ripple Effects
GPT-4 had established a high bar with greater precision, accuracy and image input, even as it continued to hallucinate after its advance over GPT-3. This report frames Orion as a much less decisive successor rather than another discontinuity.
Later coverage identifies Orion as GPT-4.5, offered through ChatGPT Pro and characterized as compute-intensive in its limited Pro rollout. That makes the reported performance gap material: a larger model does not automatically translate into a clear task-level upgrade.
First-order effects
Orion’s perceived value proposition weakens if users cannot count on it to beat prior models on coding, a marquee evaluation and adoption use case.
Teams comparing GPT-4-era models gain reason to validate performance on their own tasks rather than treating a new model generation as a default replacement.
Second-order effects
Model providers face more pressure to differentiate through reliability, task-specific performance and deployment economics, not generation labels alone.
High-compute products become harder to justify on capability claims alone; the later $200-per-month Pro positioning makes the cost-to-usefulness comparison more salient for a compute-intensive model tier.
Third-order effects
If successive frontier releases deliver smaller broad gains, AI competition can shift from single-model leaps toward systems that combine models and route work by difficulty, as later GPT-5 coverage describes a router between efficient and reasoning models.
Benchmark leadership may become less durable as a market signal, with practical workflow performance—especially in coding—carrying more weight; later reporting of GPT-5’s software-engineering gains illustrates that narrower contest in practical coding work.
The trend: This is one data point in the shift from headline model-generation jumps toward optimization of cost, routing and task-specific usefulness.
This is quietly one of the more worrying/bearish things I've read about generative AI. They're running out of data. They're getting more expensive but not much better and not really anymore powerful. It's like I warned you! [embedded post]
@danshipper
Dan Shipper on x
The message that this headline conveys is at odds with what people inside the big labs are actually feeling / saying. It's technically correct, but the takeaway for the casual reader (AI progress is slowing) is exactly the opposite of what I am hearing
@noahpinion
Noah Smith on x
I remain skeptical of the idea that LLMs will “scale to AGI”.
@hyhieu226
Hieu Pham on x
Simply cannot resist uttering the sticky phrase in my mind for the last few days: “Skill issue” 🤣 Okay, jokes aside. I find this hard to believe. I kind of get why they are called The [Mis]information. Saying that the scaling law for LLMs has slowed down is like saying that
@yampeleg
Yam Peleg on x
so I think it is safe to assume that all major players have reached the limits of training longer and collecting more data already.. It is all about data quality now.. which takes time..
@yampeleg
Yam Peleg on x
Heard a leak from one of the frontier labs (not oai tbh), they reached an unexpected HUGE wall of diminishing returns trying to brute-force better results by training longer & using more and more data.. (more severe than what is published publicly)
@amir
Amir Efrati on x
@steph_palazzolo @erinkwoo To put a finer point on it, the future seems to be LLMs combined with reasoning models that do better with more inference power. The sky isn't falling.
@amir
Amir Efrati on x
With all due respect, the article talks about a new AI scaling law that could replace the old one. Sky isn't falling.
@garymarcus
Gary Marcus on x
Folks, game over. I won. GPT is hitting a period of diminishing returns, just like I said it would.
@modestproposal1
@modestproposal1 on x
“While Orion's performance ended up exceeding that of prior models, the increase in quality was far smaller compared with the jump between GPT-3 and GPT-4” [image]
@0xkarmatic
Karma on x
“The increase in quality of OpenAl's next flagship model was less than the quality jump between the last two flagship models” Gary Marcus might be right. Heartbreaking. I was promised AGI next year though? [image]
@amir
Amir Efrati on x
news: OpenAI's upcomning Orion model shows how GPT improvements are slowing down It's prompting OpenAI to bake in reasoning and other tweaks after the initial model training phase. [image]
r/OpenAI
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r/Futurology
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r/singularity
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