Why One AI Company Pays Engineers Like Salespeople (And Why It's Working)
When Tenex told distinguished engineers they pay per story point, people rolled their eyes. But this controversial model has helped them hire NASA rocket scientists and ship production AI systems in 2 weeks with 96% accuracy.
AI agents
To production
Accuracy
Model
The AI Adoption Gap: Two Worlds, One Room
The moment that changed everything happened at WeWork. Arman was sitting among 12 engineers, but he might as well have been on a different planet.
"On my computer, I had 45 agents. Three were ordering me lunch. Two were writing code. One was doing research. Just different worlds were happening on my computer versus this person's computer."
— Arman Hezarkhani (00:03:21)
3:21The Person Next to Him
"They were typing like a caveman... this poor person was typing like with their little chopstick fingers individual characters."
— Arman (00:03:07)
The Realization
"I thought maybe we should do a GoFundMe or something. But regardless, my belief is that this is an incentive issue."
— Arman (00:03:31)
"For me, I was a founder and I wanted to squeak out every bit of incremental value and efficiency that I could. And so I would sit on Twitter and LinkedIn and read blog posts and try to understand what is the cutting edge in software engineering and what's going to give me the ability to output more code, higher quality, faster. And because of that, I was using all these different agents."
— Arman (00:03:56)
The insight: Founders adopt AI aggressively because they benefit directly from productivity gains. Salaried employees don't have the same incentive structure.
Why Current Compensation Models Fail
Engineering compensation has evolved through three models. Each worked for its time, but all break in the age of AI.
Stage 1: Hourly Pay
"There's no upside. There's no reason to work faster, right? And in fact, there's a disincentive to work faster."
— Arman (00:05:26)
Why it fails: Engineers pad estimates. "I'm not going to say it's going to take five hours. I'm going to say it's going to take 15 hours, 20 hours, so that I have no downside." (00:05:53)
Stage 2: Salary + Bonus
"People punch in at nine, leave at five."
— Arman (00:05:44)
Why it fails: No incentive for incremental effort or AI adoption. Annual bonuses are too disconnected from daily output to motivate behavior change.
Stage 3: Equity (The Google Model)
"This is the foundation of the startup community. But not every company is Google. In fact, for every one Google, there are many, many failures. And software engineers know this, right?"
— Arman (00:06:53)
Why it breaks: Engineers prefer cash over risky equity. "Cash is non-negotiable. Equity? Yeah, sure, I'll take some upside." (00:07:12)
And so my contention is that this model needs to be reinvented in the age of AI. We need to directly incentivize people to use these tools and to use them well and to still maintain really high quality standards of code.
— Arman Hezarkhani (00:07:22)
The Tenex Model: How Story Point Pay Works
Tenex isn't just paying engineers differently—they've restructured the entire development process around output-based compensation with quality safeguards.
Roadmapping
Strategists (former PMs/engineers) distill client requirements
Execution
AI Engineers design architecture and build systems
"Our engineers have a flat base that they're paid and then every quarter we round up based on the story points that they've completed."
— Arman (00:09:13)
Key: When ticket is accepted → Engineer paid fee per story point
1. Client Request
AI needs identified
2. Strategist Scope
Requirements distilled
3. Architecture
Design + story points
4. Implementation
Engineer builds
5. Payment
Per accepted point
Proof It Works: Real Case Studies
Theory is nice. Results are better. Here's what happens when you align incentives with AI adoption.
"We did it in two weeks and we got to 96% accuracy when compared to the human moderator."
— Arman (00:10:43)
"We built them five models that can run in parallel. It does everything from heat mapping to Q detection to theft detection and more."
— Arman (00:11:45)
The Three Big Risks (And How Tenex Mitigates Them)
Paying engineers per story point sounds dangerous. Tenex knows this—they've built safeguards into the model.
Risk 1: Engineers Inflate Story Points
"One is what if an engineer inflates the story points, right? What if an engineer says, 'Okay, you want me to add a button? 45 story points.'"
— Arman (00:12:16)
Mitigation: Separation of Duties
Strategists (not engineers) scope the work. All scoping requires review and approval.
Risk 2: Quality Drops When Rushing
"What if an engineer rushes and quality drops? You're saying that it took two weeks. Well, was it good? Did it work?"
— Arman (00:12:26)
Mitigation: Multi-Stage QA
Multiple rounds of QA with strategist involvement. Every ticket must be approved by both internal team AND the client.
Risk 3: Toxic Competition / 'Sharp Elbows'
"I started this by saying that we compensate engineers like salespeople. It's not a culture that we necessarily want to emulate in software engineering... What if engineers get sharp elbowed?"
— Arman (00:12:35)
Mitigation: Hire the Right People
"We make hiring incredibly difficult for ourselves so that everything else is easy. And this is incredibly important with AI."
The Core Philosophy: AI as an Amplifier
Why is hiring so critical? Because AI doesn't change who you are—it amplifies it.
My co-founder, Alex, always says, "AI makes people look like one of those crazy mirrors where any one of your attributes, it makes it 10 times larger."
— Arman Hezarkhani (00:13:50)
If you're a great engineer...
AI makes you greater
If you're not...
AI makes you sloppier
"You have to hire the right people, and this is what I tell everybody. We make hiring incredibly difficult for ourselves so that everything else is easy. And this is incredibly important with AI."
— Arman (00:13:40)
Who They've Hired
The Vision: Compensation Reimagined
This isn't just about paying engineers differently. It's about unlocking human potential in the AI age.
AI Gives Superpowers
"Our belief is that AI gives people superpowers and it makes all of us smarter, faster, and better at what we do."
— Arman (00:14:12)
Current Model Holds People Back
"But my belief is that the current way that we compensate people is actually holding them back."
— Arman (00:14:18)
Need Direct Incentives
"We need to directly incentivize people to use these tools and to use them well and to still maintain really high quality standards of code."
— Arman (00:07:31)
I would invite you to think about how can you compensate people on your team differently, whether it's software engineering or anything else. If you want to unlock your employees' potential...
— Arman Hezarkhani (00:14:24)
9 Key Takeaways for AI Engineering Leaders
Founders use AI because they benefit directly from efficiency gains. Salaried employees don't have the same motivation.
Salary + bonus + equity rewards time, not output. No upside for working faster or smarter.
Engineers prefer cash over risky equity. "Cash is non-negotiable. Equity? Yeah, sure."
Strategists (NRR-based) balance engineers (story point-based) to prevent gaming the system.
Most engineering time spent on design and planning, not just cranking out code quickly.
Multi-stage QA with client approval prevents rushing and ensures maintainable code.
"AI amplifies who you already are" — hire great people and make hiring incredibly difficult.
The mirror effect: "If you're a great engineer, AI makes you great. If you're not, it makes you sloppier."
We need models that directly incentivize AI tool usage while maintaining high quality standards.
Source Video
Video ID: 4mRekpZpBZs
Duration: ~15 minutes
Research Note: All quotes in this report are timestamped and link to exact moments in the video for validation. This analysis was conducted using multi-agent transcript analysis with transcript-analyzer, highlight-extractor, fact-checker, and content-strategist agents.
Company Background: Tenex (pronounced "ten-ex") helps companies with AI transformation globally, working with both off-the-shelf tools and custom AI builds.