Twitter/X

Rahul G argues that fast-moving AI advantages in 2026 are concentrated in…

2026-03-25 · 17:28 UTC ·@rahulgs ·1 min read

Brief

Rahul G frames AI strategy around what is changing quickly versus slowly. He argues model capabilities and pricing are improving too fast for small benchmark or retrieval wins to remain durable, while humans, workflows, integrations, and infrastructure change much more slowly. His conclusion is that defensibility will come from product, distribution, operational tooling for agents, and deep integration with customer data and systems rather than model-specific optimizations.

Why it matters

Rahul G argues that fast-moving AI advantages in 2026 are concentrated in model-layer variables like context windows, reasoning ability within context, benchmark performance, and cost per token, making narrow optimizations easy to obsolete.

Key details

  • He claims startups are 'ngmi' if their moat is a 10-15% technical gain such as cutting context by 15%, boosting retrieval 10% with hybrid search, beating Opus on a benchmark, building a memory system, using context graphs, or training a specialized RL model for benchmark performance at lower cost.
  • He argues teams are 'wagmi' if they focus on slower-changing layers: product/UI, customer acquisition, integrations, fast linting/CI/skills and feedback for agents, background-agent infrastructure for parallel work, faster verification loops, and user training tied to customer systems and data.
Source evidence

title: @rahulgs: seems obvious but:

things that are changing rapidly:
1. context windows
2. intelligence / ability ...
author: @rahulgs
contenttype: tweet
publication: Twitter/X
published: 2026-03-25T17:28:53+00:00
sourceurl: https://x.com/rahulgs/status/2036857870042411438

word_count: 175

seems obvious but:

things that are changing rapidly:
1. context windows
2. intelligence / ability to reason within context
3. performance on any given benchmark
4. cost per token

things that are not changing much:
1. humans
2. human behavior, preferences, affinities
3. tools, integrations, infrastructure
4. single core cpu performance

therefore,

ngmi:
1. "i found this method to cut 15% context"
2. "our method improves retrieval performance 10% by using hybrid search"
3. "our finetuned model is cheaper than opus at this benchmark"
4. "our harness does this better because we invented this multi agent system"
5. "we're building a memory system"
6. "context graphs"
7. "we trained an in house specialized rl model to improve task performance in X benchmark at Y% cost reduction"

wagmi:
1. product/ui
3. customer acquisition
4. integrations
5. fast linting, ci, skills, feedback for agents
6. background agent infra to parallelize more work
7. speed up your agent verification loops
8. training your users, connecting to their systems and working with their data, meeting them where they are