AI companies pivot to post-training tweaks as bigger models hit limits

OpenAI released GPT-5 last week after more than two years of development, but early reviews suggest the model represents only incremental improvements rather than the dramatic leap many expected. The lukewarm reception has intensified questions about whether the AI industry’s foundational belief in “scaling laws”—the idea that larger models trained on more data inevitably produce better results—may be breaking down, forcing companies to reconsider their path toward artificial general intelligence.

The big picture: The AI industry’s confidence in scaling laws stems from a 2020 OpenAI paper predicting that language models would improve dramatically as they grew larger, a theory that seemed validated by GPT-3’s success.

• However, progress appeared to stall after GPT-4’s March 2023 release, with OpenAI not unveiling a major new model for over two years.
• Industry insiders began questioning whether traditional scaling approaches were yielding diminishing returns, leading companies to pivot toward “post-training improvements” rather than simply building bigger models.

What early reviewers found: GPT-5 showed mixed results in initial testing, with some improvements but notable limitations that disappointed users.

• Tech YouTuber Mrwhosetheboss found GPT-5 superior for creating chess games and writing YouTube scripts compared to previous models.
• However, GPT-4o outperformed GPT-5 in generating thumbnails and invitations, while GPT-5 could still be induced to fabricate facts.
• Reddit users on r/ChatGPT quickly expressed disappointment, with one calling it the “biggest piece of garbage even as a paid user.”

The scaling law breakdown: Internal reports suggest OpenAI’s struggles began well before GPT-5’s public release.

• According to The Information, OpenAI’s code-named “Orion” model showed “far smaller” quality improvements compared to the dramatic jump between GPT-3 and GPT-4.
• AI researcher Gary Marcus, who was “excommunicated” from the machine learning community in 2022 for questioning scaling laws, summarized GPT-5 as “overdue, overhyped and underwhelming.”

Industry pivot to post-training: Major AI companies have shifted from pre-training larger models to refining existing ones through specialized techniques.

• OpenAI released multiple variants like o1, o3-mini, and o4-mini-high, each using different post-training approaches for specific tasks.
• Anthropic, an AI safety company, experimented with post-training in Claude 3.7 Sonnet, while xAI’s Grok 3 trained on 100,000 H100 GPU chips failed to significantly outperform competitors.
• Microsoft CEO Satya Nadella claimed these approaches represent “the emergence of a new scaling law,” though results suggest more modest improvements.

In plain English: Post-training is like taking a car that’s already been built and adding performance upgrades—better tires, a turbo engine, or improved suspension—rather than designing an entirely new vehicle from scratch. AI companies are now focusing on tweaking their existing models to perform better at specific tasks instead of building fundamentally larger, more powerful systems.

Benchmark concerns: While GPT-5 scored higher on various technical benchmarks, researchers question whether these improvements translate to real-world utility.

• Apple researchers found in their paper “The Illusion of Thinking” that reasoning models show “performance collapsing to zero” when puzzle complexity exceeds modest thresholds.
• Arizona State University researchers concluded that AI “reasoning” is “a brittle mirage that vanishes when it is pushed beyond training distributions.”
• Gary Marcus noted: “I don’t hear a lot of companies using A.I. saying that 2025 models are a lot more useful to them than 2024 models, even though the 2025 models perform better on benchmarks.”

Economic reality check: AI skeptics predict more modest market impacts than the trillion-dollar projections from tech leaders.

• Technology analyst Ed Zitron estimates AI represents “a fifty-billion-dollar market, not a trillion-dollar market,” with Marcus agreeing on “maybe a hundred” billion.
• Zitron noted that the “Magnificent Seven” tech companies spent $560 billion on AI capital expenditures in 18 months while generating only $35 billion in AI revenues.
• About 35% of U.S. stock market value is currently tied to these seven companies, raising concerns about market stability if AI fails to deliver expected returns.

What this means going forward: Even moderate AI critics acknowledge the technology’s importance while tempering expectations about dramatic transformation.

• Marcus believes AI tools will make “steady but gradual advances” with regular but limited use for tasks like summarization and drafting.
• Programming and academia may change dramatically, while some professions like voice acting could disappear, but massive job market disruption appears less likely.
• The original 2020 scaling law paper included caveats often overlooked: “At present we do not have a solid theoretical understanding for any of our proposed scaling laws.”