This episode looks at three places where AI’s expanding capabilities meet practical limits: the memory and energy needed to run it, the difficulty of knowing whether agents are doing trustworthy work, and the human judgment behind relationships and coordination.
Kepler Computing CEO Debo Olaosebikan describes a ferroelectric memory approach designed for denser cache and more energy-efficient high-bandwidth memory, including the role of standard manufacturing tools and experimental materials data. Arena CEO Anastasios Angelopoulos explains why rankings and user satisfaction can miss agent failures, and how recorded actions can reveal unauthorized work or false completion claims. Union CEO Samvit Ramadurgam discusses AI-assisted brokering, hiring for ideas, and why human relationships remain central as agents take on more execution.
Show Notes
Kepler Computing CEO Debo Olaosebikan explains how ferroelectric memory could increase cache density and improve high-bandwidth memory using standard fab equipment. Arena CEO Anastasios Angelopoulos discusses agent evaluation, alignment failures and model rankings, while Union CEO Samvit Ramadurgam explores AI-assisted brokering, startup hiring and the continuing importance of human relationships.
Chapters
(0:00) Memory, rankings and human connections
(1:27) Claude Duet gets it done, for a price
(3:21) Meet Debo Olaosebikan, Kepler Computing CEO
(4:30) Why AI is rationed and where energy goes
(6:54) Stack the bits instead of shrinking the transistor
(10:02) Finding a material that flips cheaply yet stays stable
(13:29) The winning material is a stack, not one recipe
(16:03) Materials AI is bottlenecked by experiment data
(17:32) Two products: denser cache and efficient HBM
(19:45) Can standard fab tools make Kepler’s memory?
(22:12) Could cheaper fabs add tenfold more compute?
(24:25) Three-dimensional silicon comes before leaving silicon
(25:25) Production next year, then a gigawatt ramp
(26:32) Meet Anastasios Angelopoulos from Arena
(27:38) Arena measures work, not multiple-choice answers
(28:43) User satisfaction is only one quality signal
(30:12) Models know when they are being tested
(31:05) Long agent sessions reveal hidden violations
(32:35) Three ways an agent betrays its user
(34:32) Paid evaluations fund Arena’s free inference
(35:42) A thank-you message can cost forty dollars
(37:11) Overall rankings can hide language-specific performance
(38:39) Private benchmarks do not prevent repeated-test gaming
(40:32) Coding agents change what evaluators can measure
(42:08) OpenAI leads, but alignment failures persist
(44:18) Production failures must feed agent training
(46:15) Meet Samvit Ramadurgam, Union founder
(47:34) Humans still broker most of the economy
(49:17) Union hires for ideas, not execution
(50:16) A target of two billion tokens per person
(51:43) Cheap models first, heavy models for the hardest cases
(52:36) Why people recoil from AI outreach
(55:27) The long tail is already very long
(57:16) Friendly fees earn more referrals than hard bargaining
(58:50) Humans hold the call, AI holds the memory
(1:00:11) The moat is the network, not the software
(1:02:37) Act two is coordinating humanity itself
(1:04:26) The same tool could organize ethnic hatred
(1:06:44) Humans keep a monopoly on their own perspective
(1:08:14) Cheaper AI startups threaten conventional SaaS
(1:09:20) Can growth absorb the jobs AI replaces?
(1:10:17) Could AI deliver a company in a box?
(1:12:13) Robot kitchens cannot make us eat more
(1:13:22) Can philanthropy make AI status games useful?
Guests
Anastasios Angelopoulos — Cofounder and CEO, Arena (𝕏 | LinkedIn)
