Startup discovery
Understand the product, user, market, and strongest customer-learning questions before proposing experiments.
Fall 2026 operating model
Three tracks. Real output.
Startup operators
AI-native growth work with early-stage startups.
Growth is AINU's startup-facing operator team: a 6-10 person group helping founders test acquisition, messaging, content, analytics, and customer-learning systems.
Growth experiments
Run focused tests across content, social, email, SEO, outreach, and lightweight acquisition workflows.
Analytics and reporting
Use retention and engagement data to turn results into dashboards, memos, and recommendations a founder can use.
Who it is for
Students interested in startups, marketing, analytics, social-native content, customer research, and practical AI workflows.
Output
- Startup growth memo
- Content or acquisition experiment
- Performance dashboard
- Founder-ready recommendations
AI public markets
Source-backed research on AI public markets.
Investment is AINU's AI Public Markets Lab. Eight analysts study public companies across the AI value chain, connect filings to demand drivers, and publish educational student research.
Sector framework
Map Energy, Hardware, Infrastructure, and AI Products to understand where AI demand creates company-level economics.
Company ownership
Own one public company deeply, read filings and transcripts, pressure-test the bull case, and build a real bear case.
Packet and pitch night
Produce a polished 20-30 page research packet and present the work in a pitch-night format without price targets or advice.
Who it is for
Students interested in AI markets, equity research, diligence, valuation, strategy, disciplined skepticism, and technical business writing.
Output
- 20-30 page research packet
- AI value-chain map
- Company deep dive
- Pitch night presentation
Technical R&D
Focused robotics, reinforcement learning, and applied AI.
Research is AINU's technical R&D team. The fall model moves away from scattered side projects toward one focused semester project, starting with robotics, reinforcement learning, and practical applied AI.
Single project focus
Build around one semester-long project with clear milestones, candidate profiles, and a visible technical output.
Robotics and RL
Explore Unitree robot directions, MuJoCo-aligned simulation, stability, speed, and complex movement demos.
Documented progress
Package the work as technical demos, writeups, open-source contributions, and visual documentation for AINU channels.
Who it is for
Students who want technical depth in robotics, reinforcement learning, simulation, ML systems, applied AI tools, and research communication.
Output
- Robotics or RL demo
- Open-source contribution
- Technical project writeup
- Showcase-ready documentation