AI:AM — Enterprise AI Meets Real-Time Inference · August 31, 2026
Notas do episódio
Zach Bratun-Glennon of Gradient joins Nathan Labenz and Prakash Narayanan to discuss why enterprise AI pilots often stall before production, how long-running agents change infrastructure requirements, and where benchmarks, model routing, and open-source security matter most. Angela Yeung of Cerebras explains wafer-scale inference, microbatching, on-chip weights, power constraints, and the data-center capacity needed for real-time AI.
Chapters
(0:00) AI agents sacrifice themselves.
(0:53) AI can game its own evaluation.
(1:47) A late defense loses.
(2:34) AI can reason itself into lying.
(4:04) The incident in context
(6:11) Why the report drew criticism
(12:14) Speed versus investigative scope
(14:31) Lawyers and executive risk
(15:00) Felony claims and Congress
(18:07) Why investigations stay limited
(23:39) The origin of AI cooperation
(28:26) AI outbreaks and resources
(31:35) Bio risk enters the picture
(33:22) Testing models before scaling
(34:42) AI labs and a possible pause
(36:34) Meet Zach Bratun-Glennon
(39:56) Gradient's contrarian AI bet
(42:17) AI startup investment thesis
(48:17) Long-running agent infrastructure
(54:19) Nango and Respan tooling
(56:36) Enterprise adoption and benchmarks
(57:41) AI pilots to production
(1:02:01) Open-source model security
(1:04:13) Legal responsibility for agents
(1:07:33) Safety standards and evaluation
(1:09:15) AI competition and model access
(1:13:40) AI venture funding
(1:16:53) What LPs misunderstand
(1:19:01) Token economics of AI
(1:20:54) Angela Yeung and Cerebras
(1:23:16) Cerebras wafer-scale chips
(1:25:49) On-chip weights versus GPUs
(1:27:49) Microbatches and throughput
(1:29:38) Why inference speed matters
(1:32:14) Speed dividend use cases
(1:33:17) Fast inference for model evals
(1:34:58) CUDA and AI-generated kernels
(1:36:05) AI agents and kernel programming
(1:40:30) Cerebras public API
(1:47:05) Hidden harness bottlenecks
(1:49:58) Power and data-center space
(1:51:40) Booking future capacity
(1:52:50) Building data centers
(1:56:58) AI agent security
(1:58:52) Agent orchestration guardrails
(2:01:11) Sovereign AI and enclaves
(2:02:33) Speed turns into quantity
(2:06:15) Why agents are slow
(2:11:06) Commercial cyber model incentives
(2:12:55) AI defense versus offense
(2:16:04) Creative agent workarounds
(2:21:59) RLVR and model behavior
(2:24:35) AI labs flying blind
(2:27:08) Privacy makes risk visible
(2:31:35) Testing faster AI models
(2:32:56) OpenAI ads and AI video
(2:34:44) Infinite AI-generated content
(2:36:55) Aliens and simulated worlds
(2:39:10) Real-time speed threshold
(2:40:13) Real-time video quality
(2:41:54) AI-generated music video
Guests
Angela Yeung — SVP, Product, Cerebras (𝕏 | LinkedIn)
Zach Bratun-Glennon — General Partner, Gradient (𝕏 | LinkedIn)
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