The Stable Era: The Modularisation Of Post-Training
Stable Interfaces -> Modular Innovation
The Stable Era
In Liu Cixin’s Three Body Problem, the planet Trisolaris alternates between chaotic eras, when three suns move unpredictably and civilisation is impossible, and stable eras, when the orbits settle and everything gets built. The inhabitants cannot control which era they are in. They can only recognise the transition and act on it.
Together AI’s CEO Vipul Ved Prakash wrote a compelling essay arguing that we’re in a stable era in AI, identifying three interfaces that have standardised: the transformer architecture below, the OpenAI-compatible inference API above it, and the agentic harness (with MCP as its tool protocol) on top.
Carliss Baldwin and Kim Clark argued that the most important economic event in technology industries is often not the invention of a new product, but the creation of a modular architecture with stable interfaces.
But what is perhaps less appreciated is that, as with previous platform markets, the appearance of stable interfaces is enabling specialization, independent innovation, and a modular re-organization of the AI ecosystem.
AI has already converged on three powerful, standardized interfaces:
The transformer architecture
Inference API (OpenAI compatible)
Agentic harnesses that operate over these APIs
In much the same way that steam engines, interchangeable parts, and shipping containers transformed fragmented crafts into industrial systems, the transformer has become the common industrial substrate for intelligence.
Every improvement to attention algorithms, optimizers, kernel libraries, inference engines, and training frameworks, advances the frontier for nearly every model at once, and an entire supplier ecosystem has emerged around it, spanning software frameworks, silicon, and models.
Invoking Baldwin and Clark’s theory of modularity, he argues that the most important economic event in a technology industry is rarely a product. It is the arrival of stable interfaces, because stable interfaces are what allow an ecosystem to specialise, disaggregate, and improve along every axis at once.
The PC did not out-innovate vertically integrated computer companies because any single component was better. It won because Intel, Microsoft, and thousands of peripheral vendors could innovate independently against a shared contract. Sun Microsystems, was undone by Linux plus commodity x86 not a better integrated competitor.
Stabilisation’s consequences
Together’s API volume grew nearly 10,000x in nine months, from 30B to 400T tokens a month, most of it open-weights models driving agentic workloads.
More importantly, the open frontier is no longer a distillation artefact of the closed one: the modern transformer block is an assembly of freely diffusing parts (RoPE, RMSNorm, SwiGLU, GQA, MoE), and training methods spread the same way, with DeepSeek’s GRPO propagating across the ecosystem within weeks of publication.
Open tokens now price an order of magnitude below the closed frontier tokens they substitute for, and the moment an open model crosses “good enough” for a workload, the closed premium on that workload collapses toward zero.
Cheap, capable, frictionlessly substitutable open weights are the substrate. What gets interesting is the layer that sits on top of them.
The economics of shaping intelligence
The marginal cost of shaping intelligence continues to fall.
Bridgewater’s AIA Labs recently released results from from their collaboration withThinking Machines Lab The task: filtering and triaging financial documents the way an experienced investor would. Frontier models managed roughly 50% accuracy with naive prompts and plateaued below 80% even after expert prompt engineering, short of the threshold investors would trust in a daily workflow. Notably, the frontier curve was flattening: GPT 5.4 cost 43% more than 5.2 for marginal accuracy gains. The binding constraint isn’t raw frontier intelligence.
Bridgewater trained Qwen3-235B on expert-labeled data via Tinker, Thinking Machines’ training API, and reached 84.7% average accuracy against the best frontier model’s 78.2%, a 29.8% reduction in errors, at 13.8x lower inference cost per task.
The result generalised across many internal tasks beyond the six published. They characterised this as “differentiated intelligence”: custom models tuned to specific organisational needs that outperform frontier models on those needs (Applied Compute calls this ‘specific intelligence’).
The strongest argument against post-training is the lack of transferability when a new SOTA model is dropping every 35 days.
A fine-tune, full or LoRA, is locked to one base model’s weight space. New models now arrive so fast that the state of the art holds the top of the public leaderboard for roughly 35 days on average. Every release forces a choice: forgo the newer, smarter base, or pay to re-tune from scratch. The cost of maintaining a portfolio of fine-tuned capabilities scales inversely with the time between releases. Under that math, adaptation is perishable capex, and “just wait for the next frontier model” is a rational procurement strategy.
PorTAL, published by Ramp Labs, is the first credible attack on the structure of that cost rather than its level. The idea: learn the task adaptation once, in a base-agnostic form, and port it to each new frozen model by refitting only a thin per-base converter on a small calibration set.
The results are impressive.
A task representation learned on Qwen3-1.7B and 4B recovered ~98% of a from-scratch LoRA’s accuracy lift on an unseen Qwen3-8B, and ~94% on Gemma-3-4B, across model families. The comparable prior method, Cross-LoRA, recovered only ~14%. The refit needed roughly half the calibration data of training from scratch.
If task adaptations become portable across bases, a fine-tune stops being an expense you re-incur per model release and becomes a durable asset that rides the frontier. The organisation’s accumulated judgment, encoded once, appreciates as the substrate improves underneath it. The cost-benefit case for owning your adaptation stops depending on frontier progress slowing down, which was always the weakest link in the argument.
A base-agnostic task latent is to fine-tuning what the container was to cloud workloads: the abstraction that decouples the thing you own from the substrate it runs on, and thereby makes the substrate a commodity you ride rather than a dependency you fear.
Recipe expertise, today's platform differentiator, diffuses like everything else in this ecosystem: Bridgewater published its entire recipe, ablations included. Methods are a wasting moat. Proprietary data and captured judgment are not.
Post-training has not had its modularisation event yet, but this might be it.