WaveSession

TensorWave
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WaveSession is a conversation series with the people setting the direction of an open ecosystem around AI. In this show we interview the founders, researchers, builders, and capital providers making the calls on what gets built, how it scales, and who are getting sneak peeks into the future. Each episode moves across the stack, from the chip to the grid, the software to the firmware, the language to the physics beneath it. These are the calls being made in real time, by the people making them. Each session is an honest account, where we explore beyond the superficial, not a highlight reel. The guests are testing approaches that haven't been proven yet, because building open AI infrastructure at this scale means solving problems that don't have a playbook: technical, financial, and human, all at once. WaveSession treats this moment as what it is: a technological revolution, built in the open, with societal consequences we get into as they unfold. It invites the audience to challenge, question, and shape where it goes.

Episodes

  1. 20h ago ·  Video

    Why Firmware Is Going Open Source, Fast

    What actually happens between the moment a server powers on and the instant your operating system boots? In this episode of WaveSession #3, Chris Walter, co-founder of firmware engineering firm Nine Elements and founder of the Open Source Firmware Foundation, pulls back the curtain on BIOS, BMC, and root-of-trust firmware, and explains why the once-closed, NDA-locked firmware industry is rapidly going open source. Chris walks through his unconventional path into firmware, starting with a master's thesis attempting to brute-force a cryptographic algorithm using FPGA clusters, and evolving through side-channel attacks, hardware security auditing, and embedded sensor development before landing in BIOS, BMC, and root-of-trust engineering. That security-first background shapes his central thesis: firmware is the invisible foundation underneath every claim a system makes about being secure. If the boot chain is compromised, everything built on top of it, confidential computing, secure enclaves, container isolation, falls apart with it. The conversation gets into why hyperscalers like Meta and Google, followed by mid-sized on-prem operators, are driving the shift from proprietary firmware toward open standards. Custom silicon, multi-host server designs, faster time-to-OS, and the ability to patch security vulnerabilities on your own timeline are pushing companies to demand transparency and unified firmware stacks instead of juggling incompatible vendor blobs. Chris and Ilya trace the parallels to past industry inflection points, the rise of Linux, software-defined networking, and software-defined radio, arguing that firmware is hitting its own tipping point now, compressed into years instead of decades. Efficiency and AI take over the back half of the conversation. Chris explains how open BMC access already lets operators fine-tune fan curves and power draw at scale, and why firmware's deterministic, well-documented nature makes it surprisingly well-suited to AI-assisted development. Nine Elements uses Claude extensively for automated testing and firmware workflows, even as legal NDAs around proprietary silicon limit what can be fed into LLMs in the first place. They close on the "second life" of decommissioned hyperscaler hardware, the acquisition upheaval hitting legacy firmware vendors like Phoenix and AMI, and Chris's prediction for a far more fragmented, competitive firmware ecosystem over the next decade, with AMD's OpenSIL initiative as an early proof point. This episode covers: Why firmware is the invisible root of system security, and what happens when that layer is compromisedWhy Nine Elements had to go open source to survive, reverse-engineering silicon behavior just to support early customersHow open firmware gives decommissioned hyperscaler hardware a "second life" instead of becoming an unusable brickWhy firmware's deterministic nature makes it a surprisingly strong fit for AI-assisted development, and where NDAs still limit itParallels between today's firmware shift and past tipping points like Linux and software-defined networkingWhy longer hardware lifecycles, 7 to 12+ years in production, are forcing companies toward open, self-serviceable firmwareWhere the firmware industry is headed by 2030, including AMD's OpenSIL and the fallout hitting legacy vendorsAnd more!Takeaway: Firmware has quietly become the thing every other security claim depends on, and the industry that used to lock it behind NDAs is now racing, at Linux-and-SDN speed, to open it up before longer hardware lifecycles and hyperscale demands make the old model unworkable. About the Guest Chris Walter is the co-founder of Nine Elements, a firmware engineering company specializing in open source BIOS, BMC, and root-of-trust firmware for server and embedded systems. He also co-founded the Open Source Firmware Foundation, a nonprofit umbrella organization supporting open firmware projects, and helps organize the annual Open Source Firmware Conference. Chris's background spans hardware security research, side-channel and fault-injection analysis, and embedded firmware development, giving him a security-first lens on how modern computing infrastructure boots, verifies, and protects itself. Tags: firmware engineering, open source firmware, BIOS and BMC, data center security, root of trust, hyperscale infrastructure, AI-assisted development, hardware lifecycle, confidential computing, Open Source Firmware Foundation

  2. Oct 15 ·  Video

    From Contract AI to Agentic AI: Jerry Ting's Enterprise Playbook

    From Grammarly-before-Grammarly to leading Agentic AI at Workday, Jerry Ting's journey reveals what it actually takes to bring AI into the world's largest enterprises. In this episode of WaveSession #2, Jeff and Ilya sit down with the Evisort co-founder to unpack contract AI, enterprise trust, and why "don't trust us, test us" became a winning sales philosophy. Ting's path to founding Evisort started long before the current AI boom, back when he was building rudimentary grammar-correction tools for his father as an undergrad studying computational linguistics. That early obsession with natural language processing collided with a very human problem at Harvard Law School: reading dense, error-prone contracts by hand. Ting and his MIT-based co-founder spent years in 2016 manually labeling legal clauses and training models before "evals" and "accuracy" were even industry buzzwords, a level of foundational rigor Ting says gives founders from that era a real edge in today's faster-moving AI landscape. The conversation gets into what actually drives enterprise AI adoption and expansion. Ting credits Evisort's growth, customers expanding fourfold post-sale, to forward-deployed engineers who combined legal domain expertise, technical architecture skills, and change-management instincts. He's candid about where many AI startups go wrong: staying too narrow, selling a single point solution, and losing customers to platform players once IT leadership gets involved. They also tackle the murky question of AI ROI: Ting argues that framing AI as "hours and dollars saved" gets you in the door for six-figure deals, but real strategic alignment with the CFO and CEO is what unlocks seven-figure contracts. Ting and the hosts explore the human side of this shift too: job displacement anxiety among law students and entry-level workers, the rise of internal "AI transformation" roles sitting inside legal, finance, and HR rather than IT, and why hands-on experimentation, not passive consumption of AI content, is the only real path to fluency. Ting shares a vivid story of training 500 Workday salespeople to use AI for account research in real time, and reflects candidly on his own transition from founder to Workday executive, including the emotional complexity of an acquisition and why "resting and vesting" was never really an option for him. They close with Ting's outside view on VC and private equity firms building arms to acquire and transform legacy "Middle America" businesses with AI, and why he believes adoption is no longer optional for anyone in a decision-making seat, even amid European regulatory skepticism. This episode covers: Why "don't trust us, test us" became Evisort's core sales strategy in an era of AI skepticismWhy value-based ROI framing has a ceiling, and what actually unlocks seven-figure enterprise dealsHow forward-deployed engineers drove 4x post-sale expansion at EvisortWhy AI transformation roles are emerging inside legal, finance, and HR instead of ITWhy hands-on usage, not watching demos, is the only real path to AI fluencyThe emotional reality of Ting's transition from founder to Workday executive after the acquisitionHow VC and PE firms are converging to acquire and transform legacy businesses with AIAnd more! Takeaway: Enterprise AI adoption isn't won by the most sophisticated model. It's won by trust, hands-on proof, and the domain experts inside legal, finance, and HR who actually understand the workflow being transformed. About the Guest Jerry Ting is the co-founder of Evisort, an AI-powered contract lifecycle management platform he built after identifying inefficiencies in contract review while attending Harvard Law School. Under his leadership, Evisort grew to serve roughly 30% of the Fortune 500 before being acquired by Workday. Ting now serves as General Manager of Agentic AI at Workday, where he leads AI engineering and product strategy. He also teaches at Harvard Law School's innovation lab, advises startups and private equity firms on AI strategy, and is pursuing public company board opportunities focused on enterprise AI transformation. Chapters 0:00 Cold Open: Why Law Tests the Frontier of AI0:37 Meet Jerry Ting: From Harvard Law to AI Founder4:38 The AI Renaissance & Why Contracts Were the Perfect Problem8:45 “Don’t Trust Us, Test Us”: Evisort’s Go-To-Market13:47 Forward Deployed Engineers & Value-Based Selling19:32 Reimagining Work & Upleveling Employees23:34 AI Upskilling: “Just Touch It”29:31 Selling Evisort to Workday34:36 The Refounding of Tech38:31 Enterprise AI Obstacles & Job Displacement45:24 VC vs. PE: Winning the Vertical AI Race50:23 The Rise of AI Transformation Roles

  3. Oct 8 ·  Video

    Sebastian Thrun on Physical AI and the Future of Work

    Long before ChatGPT made AI a household word, Sebastian Thrun was already building it, winning the DARPA Grand Challenge with Stanley, founding Google X and Waymo, and launching Udacity to democratize tech education. In this episode of WaveSession #1, Thrun joins Jeff and Ilya for a wide-ranging conversation on physical AI, the collapse of the hardware-software divide, and why the next decade of innovation will look nothing like the last. Thrun opens by revisiting the 2005 DARPA Grand Challenge, where his team's self-driving car, Stanley, navigated 130 miles of Mojave Desert terrain using little more than a trunk-mounted computer and roughly 100 parameters, a stark contrast to today's trillion-parameter models. He explains how that early work exposed the hard tradeoffs of physical AI: unlike a hallucinating chatbot, a hallucinating self-driving car can kill someone, which is why reliability, not just capability, became his team's obsession. That same thread carries into his account of building Waymo, now 17 times measurably safer than human drivers. From there, the conversation moves into Thrun's years running Google X, where he helped incubate Google Brain, Google Glass, and a string of moonshot projects built on the belief that technology follows an exponential curve and pessimists are almost always wrong. He's candid about failure here: Google Glass didn't fail because the underlying vision was wrong, he argues, but because the form factor and go-to-market were wrong, a distinction he credits to a passage in Mark Pincus's book about how entrepreneurial intuition tends to be right even when specific ideas are wrong. Thrun connects this directly to his current obsession: building an "AI engineer" capable of collapsing the fragmented, over-engineered way hardware and software teams work today into a single, radically more efficient design process. The conversation also digs into what AI means for careers, education, and the workforce. Thrun argues that specialists, lawyers, pilots, accountants, are the most exposed to automation, while generalists who can adapt across domains will thrive, a shift he says is already visible in his own companies, where engineer-to-product-person ratios have flipped. He's equally direct about the societal stakes: this isn't a technology revolution, he says, it's a societal one, requiring redistribution, dialogue, and deliberate effort to keep opportunity from concentrating in too few hands, grounded in his experience scaling Udacity across the Middle East, including a measurable GDP impact in Egypt. Toward the end, the conversation turns personal. Thrun talks about his hiring philosophy (he wants to hear what you screwed up, not just what you're proud of), his belief that blame is corrosive and self-reflection is a competitive advantage, and his own admission that even after decades of building companies, he still feels like an eager undergraduate. He closes with two practical rituals for staying oriented amid constant change: "non-pessimist Tuesday" and "no-blame Thursday." This episode covers: Building Stanley and winning the 2005 DARPA Grand Challenge with roughly 100 parametersWhy physical AI has almost zero tolerance for error compared to language modelsInside Google X: moonshots, Google Brain, and why Google Glass failed as a product but not as a conceptWhy specialists are most exposed to automation while generalists will thriveWhy Thrun calls this a societal revolution, not just a technological oneHis hiring philosophy: asking candidates what they screwed up, not what they're proud of"Non-pessimist Tuesday" and "no-blame Thursday": simple rituals for staying oriented amid constant changeAnd more!Takeaway: Two decades at the frontier of AI taught Thrun the same lesson over and over: intuition is usually right even when the specific idea is wrong, reliability matters more than raw capability, and the real risk of this moment isn't what AI can do, it's who gets left out as it reshapes everything. About the Guest Sebastian Thrun is a serial entrepreneur, AI researcher, and educator who has spent over two decades at the forefront of applied artificial intelligence. He led the Stanford team that won the 2005 DARPA Grand Challenge, founded and led Google X (Alphabet's "moonshot factory"), and co-founded Waymo, now one of the world's leading autonomous vehicle companies. He also founded Udacity, an online education platform focused on making technology training accessible globally, with measurable economic impact in markets including Egypt, Saudi Arabia, and Palestine. Thrun previously served as a professor at Stanford University and Carnegie Mellon University, and continues to work hands-on building AI-driven robotics and engineering tools. Chapters0:00 — Opening: AI Access for Everyone 0:52 — The Stanley Era: Early Autonomous Vehicles 5:25 — From Chauffeur to Waymo: Physical AI Safety 7:54 — Google X & the Moonshot Factory 12:21 — Transforming Organizations: AI 1.0 vs. 2.0 15:33 — The 20x Productivity Shift in Software 17:20 — Democratizing Education & Commoditized Intelligence 22:30 — Google Glass: Intuition, Failure & Project Wingman 29:21 — Specialists vs. Generalists in the AI Era 33:55 — Advice for New Engineers & Graduates 38:21 — Intuitions vs. Ideas: Lessons from Failure 42:39 — Frontier Models Eating Vertical SaaS

  4. Oct 1 ·  Video

    The Real Bottleneck Isn't AI Chips. It's Power.

    We spent two years worrying about GPU supply. That worry is over, and almost nobody has caught up to what replaced it. In the premiere episode of wavesession, Jeff and Ilya cut through the noise around AI's future and land on the one constraint nobody's pricing in: energy. Not silicon. Not compute. Power. This conversation makes the case, in detail, for why the grid, not the chip, is now the hard ceiling on how far AI can scale. The thesis starts with a simple historical pattern: energy intensity has always tracked human prosperity. Jeff and Ilya argue that compute, the ability to deliver intelligence at scale, is becoming its modern successor. They trace how decades of deferred maintenance on the U.S. power grid, much of it running on infrastructure from the 1950s and 60s, is now colliding head-on with a demand curve nobody designed for. A decade ago, a 30-megawatt data center was considered large. Today's hyperscale campuses are pushing past 100 megawatts, built for a load the grid was never built to carry. There's a culture problem underneath the technical one, too. Utilities run on a "five-nines" reliability mindset: slow, cautious, built for stability over decades. Data center operators run on mission-critical, can't-go-down urgency. Two industries, two completely different clocks, now being forced to speak the same language at the exact moment it matters most. This episode covers: Why chip supply solved itself while energy infrastructure never caught upThe historical link between energy abundance and prosperity, and why compute may be its modern equivalentA Duke study showing the U.S. grid could free up roughly 100 gigawatts of hidden capacity if data centers curtailed demand by just 2.5 to 3% at peak, no new power plants requiredHow hyperscalers kept power demand flat for nearly a decade during the cloud buildout through efficiency gains, and whether that pattern can repeat for AIThe moment betting on unproven AI infrastructure stopped looking risky and started looking inevitableWhy efficiency and sustainability have to be built in now, not deferred to some future AGI breakthrough that may never show upAnd more!Takeaway: The AI industry solved its chip problem. It hasn't solved its power problem, and that gap, not GPU supply, is what will actually determine how far and how fast AI can scale from here. Chapters 0:00 — Cold Open: Kevin O'Leary & the AI Hype Problem1:54 — The Computing Stack: GPUs, CUDA & FPGAs10:50 — The FPGA to GPU Pivot & Going All In on AMD16:31 — AI's Impact on Society & Public Fear22:03 — Energy Intensity as a Prosperity Indicator24:37 — The Power Bottleneck: GPUs Abundant, Power Scarce25:22 — Grid Complexity & Data Center Curtailment33:44 — Efficiency Opportunities in AI Data Centers43:01 — Ilya's Vision: Building the Fourth Utility44:11 — Jeff's Vision: Bridging the Adoption Gap50:10 — From "What Can We Compute" to "What Should We Compute"

  5. Sep 16 ·  Video

    The wavesession Podcast: AI Infrastructure, Data Centers, Chips & What's Next

    Introducing wavesession. The people setting the direction of the open AI ecosystem are making calls right now. Founders, researchers, builders, capital providers. They're deciding what gets built, how it scales, and who gets a preview of what's coming next. wavesession is where those conversations happen. Each episode moves across the stack. From the chip to the grid, the software to the firmware, the language to the physics underneath it. These are the people making those decisions in real time. This isn't a highlight reel. There are no polished talking points. It's an honest account of what it actually takes to build open AI infrastructure at this scale, where the technical, financial, and human problems don't come with a playbook. This is a technological revolution happening in the open. It comes with consequences we're all going to live with. wavesession is an invitation to listen in, challenge what you hear, and help shape where it goes. New episodes coming weekly, starting September 30th.

    The wavesession Podcast: AI Infrastructure, Data Centers, Chips & What's Next
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    4 Episodes

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About

WaveSession is a conversation series with the people setting the direction of an open ecosystem around AI. In this show we interview the founders, researchers, builders, and capital providers making the calls on what gets built, how it scales, and who are getting sneak peeks into the future. Each episode moves across the stack, from the chip to the grid, the software to the firmware, the language to the physics beneath it. These are the calls being made in real time, by the people making them. Each session is an honest account, where we explore beyond the superficial, not a highlight reel. The guests are testing approaches that haven't been proven yet, because building open AI infrastructure at this scale means solving problems that don't have a playbook: technical, financial, and human, all at once. WaveSession treats this moment as what it is: a technological revolution, built in the open, with societal consequences we get into as they unfold. It invites the audience to challenge, question, and shape where it goes.

Information

  • Creator
    TensorWave
  • Years Active
    2026
  • Episodes
    5
  • Rating
    Clean
  • Copyright
    © TensorWave
  • Show Website
    WaveSession

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