Retrain small open-source language models for long-horizon agentic workloads and sell efficient inference and on-device deployments
Build a company that takes capable open-source models, post-trains them to reason across long-running tasks without losing context, serves them on efficient infrastructure, and offers them to businesses through cloud providers or embedded in hardware. The models are intended to handle deep research, coding, browser interaction, and other workloads that usually require frontier models, at lower cost and potentially on company-owned devices.
online B2B Deep Tech / Infrastructure
From Inside the Silicon Mind with Firas Sozan — Ex-Google Insider Reveals The Future Of AI in 2026... at 07:36
Problem: Existing AI tooling was built primarily for chatbots rather than agentic systems that must reason over long contexts and complete complex tasks. Frontier models can perform these workloads but require more expensive infrastructure and may require company data to leave the business.
For: Businesses that need reliable long-running AI agents, cloud providers and hardware companies seeking efficient model deployments, and eventually consumers who want locally owned AI on computers or phones.
Examples
- Subconscious: retrains open-source models and serves them on efficient infrastructure for long-running tasks; it is offering inference to a number of customers and working with hardware companies on embedded deployments.
Behind this: 14 build steps · 4 tools and how each is used · how to validate demand · 4 more real examples · 10 things the video never answers.
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