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What's Left to Research When Models Are Commoditized

5 min read
Ml EngAi Research

Ai Research

Efficiency, alignment, and new modalities. Commodity models are the base layer.

Ml Eng

Research ships as features. Your job: productionize the latest that actually works.

What's Left to Research When Models Are Commoditized

TL;DR

  • Training giant models from scratch is mostly done by well-funded labs. Everyone else builds on top.
  • Research that matters: efficiency (smaller, faster), alignment (safer, more controllable), and new modalities.
  • Academic and industry research diverge. Industry ships. Academia explores. Both matter.

2026: You can call GPT-5 or Claude 4 or Gemini. Open weights (Llama, Mistral, etc.) run locally. "Build a language model" is no longer the research question. The questions are: How do we make it smaller? Safer? Cheaper? How do we extend it to new domains? And what do we do when it fails?

Where Research Is Active

Efficiency:

  • Smaller models that match larger ones. Quantization, pruning, distillation.
  • Inference at the edge. On-device AI. Cost per token.
  • If you can get 90% of the quality at 10% of the cost, that's a win. Research pushes that frontier.

Alignment and safety:

  • Models that follow instructions. Don't hallucinate (or do less). Resist jailbreaks.
  • Interpretability: Why did the model say that? Can we tell?
  • Red-teaming. Evaluations. Adversarial robustness. Active research. Production impact.

New modalities and tasks:

  • Video. Audio. Multimodal reasoning. Agentic systems (models that use tools, act in the world).
  • Foundation models for code, for science, for specific industries. Customization at scale.

Data and training:

  • Better data. Cleaner data. Synthetic data. Data efficiency (learn from less).
  • Training dynamics. Why do models learn what they learn? Still open questions.

What's Commoditized

  • Basic LLM training (architectures, scaling laws) — Lab territory. Not where most researchers add value.
  • Standard fine-tuning — Tools exist. Engineering, not research.
  • Standard NLP/CV tasks — Use a model. Research is incremental.

Industry vs. Academia

Industry:

  • Ships products. Research that doesn't ship is a cost. Focus: efficiency, robustness, product fit.
  • ML engineers productionize. Researchers prototype. The handoff is critical.

Academia:

  • Explores. No product deadline. Focus: novel methods, benchmarks, understanding.
  • Papers matter. Replication matters. Industry often lags; academia explores first.

The bridge:

  • Researchers who understand production. Engineers who read papers. Collaboration accelerates both.

'Build a language model' was the research question. Labs and academics raced to scale.

Click "AI Research 2026" to see the difference →

Quick Check

Training giant models is mostly done by well-funded labs. Where can most researchers add value?

Do This Next

  1. Map your research interests — Efficiency? Alignment? New modality? Where does it connect to what ships?
  2. Pick one paper from the last year. Read it. Could you productionize the idea? What would that take?
  3. Define your "research vs. engineering" boundary — What do you explore? What do you ship? How do they inform each other?