Time: 4:20 PM
Speaker Bio: Managing Partner of Tenex. Previously founded multiple venture-backed AI companies, scaled Google Cloud AI to millions of developers, adjunct professor at Carnegie Mellon.
Speaker Profile: Full Speaker Profile
Company: Tenex compensates engineers based on “story points” (completed output) rather than hours. Anticipates multiple engineers earning $1M+ annually.
Focus: Revolutionary compensation model for the AI era. How output-based compensation directly incentivizes AI tool adoption and maximizes throughput.
References:
Notes
- Pay engineers like sales guys
- We pay engineers based upon the story points they complete
- Paid on output
- Uncapped upside
- Incentivized to work smarter faster harder
- History of compensation
- Hourly labor
- Project based
- Salary
- Salary + bonus
- Salary + bonus + equity
- Two products
- Product reqs -> strategy (external facing)
- Outputs a roadmap
- Arch design -> AI engineer (external facing)
- A lot of what we do is customer builds, that where story point models
- Most of our time goes in architect design document
- Graded on some number of story points
- When that story point is accepted the engineer gets paid based upon story points
- Client story:
- Billboard company
- 2 weeks of build to automate the moderator
- Retail technology
- Can run a model on device which does heat mapping
- Billboard company
- Risks
- Inflate story points -> strategies defines code
- Rushes and quality drops -> 3 rounds of internal and external QA
- Get sharp employees -> Hire the right people
- “Give your team a reason to go faster”
Slides
Slide: 2025-11-20-16-30
Key Point: A real-world retail AI implementation achieved measurable business impact (5%+ revenue increase) by deploying edge AI solutions that compress large models for sensor hardware, prioritize security, and integrate seamlessly with existing infrastructure.
Literal Content:
- Client story about “THE RETAIL TECHNOLOGY COMPANY”
- Three sections:
- THE COMPANY: Describes a retail tech company connecting physical and digital stores through store analytics and customer engagement
- THE WORK: Details building a Store Intelligence (SaaS) platform with edge AI, compressed teacher/student models, encrypted data streaming, and a single reporting API
- Result highlighted: “5%+ ANNUALIZED Y1 IN-STORE REVENUE UPLIFT”
- Footer shows TENEX.CO and IX logo
Slide: 2025-11-20-16-34
Key Point: Promoting a strategic resource (likely a whitepaper or guide) aimed at C-suite executives for developing AI strategy - the basketball court-like diagram suggests a “playbook” approach to AI implementation.
Literal Content:
- Title: “The AI strategy playbook for senior executives”
- Red square containing a simple geometric diagram (resembling a basketball court or strategic planning diagram with a circle and lines)
- QR code on the right side
Slide: 2025-11-20-16-38
Key Point: Shows statistical analysis of different AI adoption strategies and their effectiveness/impact, with the Bayesian posterior distributions indicating uncertainty ranges and likely outcomes for each approach.
Literal Content:
- Title: “Bayesian Posterior Distributions of AI Adoption Strategies”
- URL: https://dora.dev/research/ai-gen-ai-report/
- Chart with ridge plots (violin plots) showing distributions for various AI adoption strategies:
- time to learn
- clear AI policies
- device deployment fillers
- transparent about AI risks
- mandatory trainings
- invest in employee development
- AI adoption goals
- guidelines to integrate privacy risks
- safeguard against sec and privacy breaches
- resources to learn about AI
- Each has purple and orange overlapping distributions centered around 0