90% of AI prototypes never reach production (w/ Temporal's Samar Abbas) | AI Basics
September 15, 2026
AI Summary
5 min readSamar Abbas, CEO of Temporal, opens with a striking number: 90% of AI prototypes never reach production. The gap between a promising proof of concept and a system that runs reliably at scale is not about the AI model itself—it is about the infrastructure underneath. Abbas argues that as AI agents move from a developer’s laptop into distributed, long-running production environments, they hit the same reliability, durability, and observability problems that cloud-native applications faced a decade ago. The conversation focuses on why these failures happen, what a “harness” for agents actually means, and how enterprises can safely transition from experimentation to deployment.
The POC-to-production chasm
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What you'll learn
- 1 (00:20) **The 90% POC Graveyard** - Samar Abbas introduces the core problem: most AI prototypes die after the proof-of-concept stage.
- 2 (03:12) **Why Durability is the Make-or-Break Problem** - The conversation shifts to the specific technical failure that kills POCs: the lack of durable execution.
- 3 (06:59) **Visualizing the "Heads-Up Display" for AI** - Jason describes a visual demo on Temporal's website that shows the real-time execution of an agent.
- 4 (10:51) **The Power of Intervention and "Code Mode"** - The discussion moves from visibility to the ability to intercept and modify agent behavior.
- 5 (13:03) **The Harness vs. The Model** - Samar explains the critical difference between the AI model and the "harness" that controls it.
- 6 (15:20) **When to Move an Agent Off Your Laptop** - The host asks for the practical signal that tells a team it's time to take an agent from a personal experiment to a corporate process.
- 7 (18:43) **The Paradigm Shift** - The interview concludes with a summary of the current moment.
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Show Notes
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Today's show:
The demo works, the vibes are immaculate, but then the app falls apart the second real users touch it. On today's AI Basics, Temporal co-founder and CEO Samar Abbas says the problem isn't with the model at all. You're missing a harness, the layer that keeps an AI agent's work durable, secure, and recoverable if/when something breaks.
Temporal's durable execution platform is used by major companies from OpenAI to Stripe to Netflix, and helps to make their long-running software reliable and interruption-free.
On AI Basics, he explains why so many AI-coded prototypes die in the imagineering stage, before ever hitting production. Plus he walks Jason through Temporal's live dashboard, to show how it gives developers full visibility into what their agents are doing at every step.
Guest and Relevant Links:
Samar Abbas on X: https://x.com/SamarAtTemporal
Temporal: https://temporal.io/
Temporal raises $550M at a $12.55B valuation: https://temporal.io/blog/temporal-raises-usd550m-series-e-at-usd12-55b-valuation-ai
GeekWire: Samar Abbas on the "massive platform shift" in AI: https://www.geekwire.com/2026/temporal-ceo-samar-abbas-on-the-massive-platform-shift-in-ai-fueling-the-startups-5b-valuation/
Temporal expands Google Cloud partnership: https://temporal.io/blog/temporal-expands-google-cloud-partnership-with-pay-as-you-go-pricing
Timestamps:
0:00 Why every startup needs an AI harness
2:12 Why 90% of AI prototypes never reach production
4:16 What happens when an agent crashes mid-task
7:00 A look at Temporal's dashboard
13:02 OK but what is a "harness" exactly?
15:30 When to graduate an agent from laptop to production
17:44 Organizing agents into teams
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