According to @soumithchintala, Anthropic may scale account support via Claude or humans, while firms adopt multi AI with open harnesses for flexibility.

According to Ethan Mollick on Twitter, Claude Opus 4.7’s adaptive thinking requirement often misclassifies non‑math and non‑code prompts as low effort, yielding worse results compared to tasks it deems high effort, and lacks a manual override similar to ChatGPT’s controls (as reported by Ethan Mollick, Apr 16, 2026). According to Mollick’s post, the absence of a user-selectable effort mode limits control over reasoning depth, potentially degrading outputs for writing, strategy, and qualitative analysis. From an AI product perspective, this suggests opportunities for providers to add explicit effort controls, per‑task reasoning budgets, and transparent routing indicators; vendors serving enterprise content, marketing, and consulting workflows could differentiate with tunable reasoning settings and audit logs for model routing decisions, according to the same source.

According to Ethan Mollick (@emollick), large language models exhibit jagged intelligence where weaknesses are non‑intuitive, broadly shared across models, and shift as capabilities advance; this raises operational risk because failure modes cluster and evolve together across vendors (as reported by X/Twitter, Apr 10, 2026). According to Alex Imas (@alexolegimas), humans are also jagged, but organizations are accustomed to human variability, whereas LLM jaggedness is harder to anticipate due to emergent behaviors in advanced systems (as reported by X/Twitter). For AI deployment, this implies portfolio risk when relying on multiple similar LLMs, increased validation costs, and the need for systematic red teaming and evaluation suites. Business opportunities include specialized model evaluation tooling, multi‑model routing with capability probing, domain‑specific guardrails, and insurance‑like risk products for AI reliability, according to the discussion threads on X/Twitter by Mollick and Imas.

According to Priyaa on X (God of Prompt citing @pritopian), Lica is launching foundational infrastructure that converts AI-generated images into structured, editable layers to avoid prompt-regeneration churn and the so-called token tax (source: X post by @godofprompt referencing @pritopian). As reported by the same X thread, Lica reads an image, segments it into layers, and lets teams modify specific elements (e.g., font color) without full regeneration, preserving prior design state and routing each layer to the best model or a human editor (source: X post by @godofprompt). According to the thread, this approach addresses model concentration risk by enabling enterprises to own the editing layer in-house rather than depend solely on external model APIs for creative output (source: X post by @godofprompt). Business impact: reduced inference costs from fewer prompt loops, faster creative iteration, higher brand consistency through stateful edits, and better compliance via auditable, layer-level changes (source: X post by @godofprompt). Market opportunity: creative operations at scale across web, social, and email where teams need deterministic edits, model routing, and vendor-agnostic control of outputs (source: X post by @godofprompt).