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Tenex scales AI engineering pay-for-output model to 75 employees

By PulseAugur Editorial · [4 sources] ·

Tenex, an AI-powered development shop, is scaling its model of paying AI engineers based on delivered work rather than hours worked. The company, which now employs 75 people and serves Fortune 2000 clients, faces challenges in compensating engineers for work that doesn't fit neatly into traditional tickets, such as understanding customer problems or designing architectures. To manage this, Tenex uses a system called MetaHarness, which stores project plans and decisions in Git to provide context for both human and AI agents. The discussion also touched upon the 'Kill My SaaS' experiment, highlighting the trade-offs between building custom software and maintaining vendor solutions. AI

IMPACT Explores novel compensation models for AI engineers and the challenges of integrating AI into the software development lifecycle.

RANK_REASON Discussion of an AI company's business model and operational challenges, rather than a new release or product launch.

Read on Latent Space (swyx) →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Tenex scales AI engineering pay-for-output model to 75 employees

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Commentary
Discussion of an AI company's business model and operational challenges, rather than a new release or product launch.
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4 independent sources
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product, other
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2 days old
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COVERAGE [4]

  1. Latent Space (swyx) TIER_1 English(EN) ·

    AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess

    Listen now (44 mins) | Paying for outcomes, building with agents, and the last mile of enterprise AI adoption

  2. Latent Space (swyx) TIER_1 English(EN) · Latent.Space ·

    AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess

    Paying for outcomes, building with agents, and the last mile of enterprise AI adoption

  3. Latent Space (podcast video) TIER_1 English(EN) · Latent Space ·

    AI in NYC: How Tenex Builds an AI-Native Engineering Team — Alex Lieberman & CJ Hess

    How do you organize an engineering team when AI agents can do more of the implementation? Alex Lieberman and CJ Hess of Tenex join swyx at AI Engineer New York to discuss paying for outcomes, the MetaHarness process, enterprise AI adoption, and where AI-built software can—and can…

  4. Medium — MLOps tag TIER_1 English(EN) · Random Droid ·

    An Engineering Map of the AI Infrastructure Stack

    <div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/@random.droid/an-engineering-map-of-the-ai-infrastructure-stack-6bc82543cb88?source=rss------mlops-5"><img src="https://cdn-images-1.medium.com/max/1358/1*N8Hgbr3bTTKrcgsWUqaIaw.png" width="135…

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