Open Source Wins, AGI Is Here, and Scorsese's AI Toolkit with CEOs of Cerebras & Black Forest Labs
(0:00) The AI Buildout: Datacenters Bigger Than Cities (Andrew Feldman)
(1:50) Reasoning, Inference, and Breaking Moore's Law
(16:28) Open Source, AI Sovereignty, and the Road to AGI
(40:54) The Innovation Behind Generative Video (Robin Rombach)
(47:31) Martin Scorsese, Robots, and the Future of Hollywood IP
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The IPO Comeback: Why Tech Giants Are Finally Going Public | All-In Liquidity IPO Panel
(0:00) CEOs Andrew Feldman (Cerebras) and Will Marshall (Planet Labs) join the Besties!
(2:05) Both CEOs on going public: Impact on employees, customers, and business operations
(13:18) Timelines for datacenters in space
(19:28) Cerebras business breakdown, AI's impact on the silicon market
(24:45) How Founder/CEOs think about liquidity on the road to going public
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20VC: Cerebras CEO on the Future of Data Centres, Token Costs and Memory | We are Not in an Infra Bubble & Dario Got a Bad Deal with Elon for Compute | Should US Companies Sell to China & Why Most Layoffs are AI Washed with Andrew Feldman
Andrew Feldman is the co-founder and CEO of Cerebras Systems. This month, Cerebras went public achieving a market cap of $70BN, the largest semiconductor IPO in history. Cerebras has a massive commercial backlog with a monumental, multi-year $20 billion compute agreement from OpenAI.
AGENDA:
05:58 - Why we are not in an infrastructure bubble and it is just the start
08:00 - Sam Altman's superpower is his ability to forecast capex spend.
08:58 - Anthropic did not get a good deal with Elon. They got a deal that was available.
10:39 - What is going on with the price of memory and why is it a problem?
16:40 - Are Google best positioned to produce tokens and what challenges do they face?
19:23 - Is Coreweave dramatically undervalued or overvalued?
24:34 - My biggest advice to entrepreneurs scaling their business
30:13 - Why most of the layoffs are AI-washed and
33:41 - What will we spend on tokens for software engineers in five years?
34:48 - Why does the role of HR change so significantly in the world of AI?
35:36 - Why lawyers are the biggest inhibitor of enterprise AI adoption
39:20 - Why Jensen and Nvidia are wrong to sell chips to China
42:49 - What needs to change in the U.S. to build a strategic asset in chips?
51:00 - Should Cerebras invest in companies building on top of their platform; as Nvidia is?
53:28 - Nothing changed when Cerebras IPO'd but I did make 800 millionaires.
Why Cerebras CEO Andrew Feldman Built The World's Largest Computer Chip
Size is the name of the game for the AI chipmaker Cerebras: Their chips are truly massive, about the size of a dinner plate. According to Andrew Feldman, CEO and founder of Cerebras, that is about 58 times larger than the average chip. That sheer size enables blazing fast inference for AI queries. Feldman joins us on the week of his company's IPO to talk about his core product and how it fits into the AI boom. We discuss the history of the GPU, competition between open-and closed-source models, the company's relationship with with TSMC, and more.
Read more:
Nvidia Tells Skeptical Investors That AI Is Ready to Go Mainstream
Trump Set to Sign AI Cybersecurity Directive as Soon as Thursday
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The Story Behind Cerebras’ $63 Billion IPO with Founder and CEO Andrew Feldman
Companies in Silicon Valley from Nvidia to AMD are racing to fuel the AI revolution with postage stamp-sized AI chips. Meanwhile, a chip the size of a dinner plate just fueled a $63 billion IPO for Cerebras. Elad Gil and Sarah Guo sit down with Cerebras founder and CEO Andrew Feldman to discuss the company’s journey to making one of the largest tech go-publics in history. Andrew details the multi-year journey of pioneering wafer-scale AI computing, including surviving a brutal period of being ahead of market demand. He also explains the engineering breakthroughs that led to delivering inference speeds at 20x that of standard GPUs. Andrew then shares how a remarkable $20 billion deal with OpenAI came together in only four weeks. Plus, Andrew’s thoughts on why architecting the future of AI requires the fortitude to be a “professional David” against the Goliaths of tech.
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Chapters:
00:00 – Cold Open
00:36 – Andrew Feldman Introduction
01:19 – Cerebras’ Evolution
02:48 – Wafer-Scale Bet Pays Off
06:38 – Challenges and Breakthroughs
08:37 – Crossing the Market Chasm
10:38 – Scaling Software and Hardware
12:03 – Relevance of AI-Generated Coding
13:31 – Leadership and Hiring Culture
17:16 – When to Quit vs. Persist
19:40 – Why Cerebras Went Public
22:57 – The OpenAI Deal
25:54 – Open Source and Post-Trained Workloads
27:37 – How Speed Opens Up New Business
30:33 – Conclusion
Coinbase CEO's Top 3 Crypto Trends for 2026 + More from Davos!
(0:00) Intro
(0:37) Coinbase CEO, Brian Armstrong on Crypto Adoption
(38:51) Cerebras CEO, Andrew Feldman on Compute Power
(1:18:50) Gecko Robotics CEO, Jake Loosararian on AI and Physical Robots
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Featuring Brian Armstrong (CEO, Coinbase), Andrew Feldman (CEO, Cerebras), and Jake Loosararian (CEO, Gecko), this conversation explores how AI is changing work, infrastructure, and productivity in real time- from the builders shaping what comes next.
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20VC: Cerebras CEO on Why Raise $1BN and Delay the IPO | NVIDIA Showing Signs They Are Worried About Growth | Concentration of Value in Mag7: Will the AI Train Come to a Halt | Can the US Supply the Energy for AI with Andrew Feldman
Andrew Feldman is Co-Founder & CEO of Cerebras, building the world's fastest AI inference and training. Cerebras recently closed a $1.1BN Series G round at an $8.1 billion valuation, backed by top names including Fidelity, Atreides, Tiger Global, Valor Equity and 1789 Capital. Under his leadership, they've leapfrogged GPU limits in inference, operate at trillions of tokens per month, and are filing to go public soon.
AGENDA:
02:43 Why We Did Not IPO and Raised $1BN From Fidelity
05:03 Analysis of Chip and Compute Landscape Today
07:14 NVIDIA Showing Signs They Are Running Out of Ideas
13:57 The Real Questions to Ask on Chip Depreciation
24:54 Energy Requirements for AI: Is it Feasible?
29:25 Mag7 Value Concentration: Feature or a Bug
31:57 Talent is the Bottleneck and Trump Makes it Worse
32:55 The War for Talent: Secrets No One Sees
34:22 Evaluating the Data Centre Economy: Many Will Lose Money
38:01 Three Changes the US Could Make to Beat China in AI
42:30 Why 80% of our Revenues are in the UAE
47:26 Quick Fire Questions
58:59 Why Work Life Balance is Total BS
20VC: AI Chip Wars: How Cerebras Plans to Topple NVIDIA's Dominance | Why We Have Not Reached Scaling Laws in AI | What Happens to the Cost of Inference | How We Underestimate China and Shouldn't Sell To Them with Andrew Feldman
Andrew Feldman is the Co-Founder and CEO @ Cerebras, the fastest AI inference + training platform in the world. In Sept 2024 the company filed to go public off the back of a rumoured $1BN deal with G42 in the UAE. Andrew is the leading expert for all things inference.
In Today's Episode We Discuss:
04:23 Where Was AI Landscape in 2015 When Cerebras Founded
05:57 NVIDIA's Biggest Strength Has Become Their Biggest Weakness
07:09 What Happens to the Cost of Inference?
08:55 Why Are AI Algorithms So Inefficient?
20:30 Why is it Total BS That We Have Hit Scaling Laws?
23:07 What Will Be the Ratio of Synthetic to Human Data Used in 5 Years?
31:37 What Specifically Was So Impressive About Deepseek?
31:51 Why is Distillation Not Wrong and OpenAI Need to Look in the Mirror?
32:34 Where Will Value Accrue in a World of AI?
34:08 How Will NVIDIA's Market Position Change Over the Next Five Years?
39:59 Why is the CUDA Lockin for NVIDIA BS? What is Their Weakness?
40:46 Why is Trump Better for Business than Biden?
49:41 Do We Underestimate China in a World of AI?
52:33 What is the Most Underappreciated Segment of AI?
54:00 Quickfire Round
#222 Andrew Feldman: How Cerebras Systems Is Disrupting AI Inference
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In this episode of the Eye on AI podcast, Andrew D. Feldman, Co-Founder and CEO of Cerebras Systems, unveils how Cerebras is disrupting AI inference and high-performance computing.
Andrew joins Craig Smith to discuss the groundbreaking wafer-scale engine, Cerebras' record-breaking inference speeds, and the future of AI in enterprise workflows. From designing the fastest inference platform to simplifying AI deployment with an API-driven cloud service, Cerebras is setting new standards in AI hardware innovation.
We explore the shift from GPUs to custom architectures, the rise of large language models like Llama and GPT, and how AI is driving enterprise transformation. Andrew also dives into the debate over open-source vs. proprietary models, AI's role in climate mitigation, and Cerebras' partnerships with global supercomputing centers and industry leaders.
Discover how Cerebras is shaping the future of AI inference and why speed and scalability are redefining what's possible in computing.
Don't miss this deep dive into AI's next frontier with Andrew Feldman.
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(00:00) Intro to Andrew Feldman & Cerebras Systems
(00:43) The rise of AI inference
(03:16) Cerebras' API-powered cloud
(04:48) Competing with NVIDIA's CUDA
(06:52) The rise of Llama and LLMs
(07:40) OpenAI's hardware strategy
(10:06) Shifting focus from training to inference
(13:28) Open-source vs proprietary AI
(15:00) AI's role in enterprise workflows
(17:42) Edge computing vs cloud AI
(19:08) Edge AI for consumer apps
(20:51) Machine-to-machine AI inference
(24:20) Managing uncertainty with models
(27:24) Impact of U.S.–China export rules
(30:29) U.S. innovation policy challenges
(33:31) Developing wafer-scale engines
(34:45) Cerebras' fast inference service
(37:40) Global partnerships in AI
(38:14) AI in climate & energy solutions
(39:58) Training and inference cycles
(41:33) AI training market competition
Launching the Fastest AI Inference Solution with Cerebras Systems CEO Andrew Feldman
In this episode of Gradient Dissent, Andrew Feldman, CEO of Cerebras Systems, joins host Lukas Biewald to discuss the latest advancements in AI inference technology. They explore Cerebras Systems' groundbreaking new AI inference product, examining how their wafer-scale chips are setting new benchmarks in speed, accuracy, and cost efficiency. Andrew shares insights on the architectural innovations that make this possible and discusses the broader implications for AI workloads in production. This episode provides a comprehensive look at the cutting-edge of AI hardware and its impact on the future of machine learning.
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Paper Andrew referenced Paul David- Economic historian
https://www.jstor.org/stable/2006600
#165 Andy Hock: Will AI Revolutionize How We Do Business?
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Unveil the transformative power of AI in business and society.
Join us on #165 of the Eye on AI podcast as we sit down with Andy Hock, VP of Product at Cerebras Systems, an AI hardware startup out to accelerate deep learning and change computing forever.
Focusing on the intersection of AI and business, Andy discusses how advanced technologies are reshaping corporate strategies and operations. He offers insights into integrating AI into traditional business models, emphasizing the transformative role of technology in innovation and growth.
We also explore the societal implications of AI. Andy delves into the ethical considerations and challenges posed by AI, including privacy and responsible deployment. He sheds light on the latest trends in AI research, particularly in areas like natural language processing and machine learning, and their diverse applications across industries.
Andy concludes with advice for businesses and individuals navigating the AI landscape, underscoring the importance of adaptability and continuous learning in this rapidly evolving field. Make sure you watch till the end!
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(00:00) Preview and Introduction
(01:09) Decoding Cerebras Systems' Technology.
(03:25) The Power of the Wafer Scale Engine
(05:24) Training and Inference Capabilities
(10:44) Accessibility for Students and Researchers
(12:08) GPU Equivalents and Wafer Scale Engine's Efficiency
(13:53) Utilization and Power of the Chip
(19:02) Understanding Wafer and Circuitry
(23:27) Computing Power of the Wafer Scale Engine
(28:59) Challenges in Cooling the Wafer Scale Engine
(32:31) Utilization of Silicon Waste
(36:21) Cost Comparison with Other Technologies
(40:01) Customer Usage Patterns – Cloud vs On-Premise
(45:22) Training Models for Edge Devices
(47:30) Collaborations with OpenAI
(49:26) Addressing GPU Shortages and Enterprise Needs
(51:01) Inference Capabilities and Future Developments
(57:46) AI as a National Compute Resource
(01:03:41) Closing Remarks
The World’s Largest AI Processor with Cerebras CEO Andrew Feldman
The GPU supply crunch is causing desperation amongst AI teams large and small. Cerebras Systems has an answer, and it’s a chip the size of a dinner plate. Andrew Feldman, CEO and Co-founder of Cerebras and previously SeaMicro, joins Sarah Guo and Elad Gil this week on No Priors. They discuss why there might be an alternative to Nvidia, localized models and predictions for the accelerator market.
Show Links:
Andrew Feldman - Cerebras CEO & Co-founder | LinkedIn
Cerebras
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Show Notes:
(0:00:00) - Cerebra Systems CEO Discusses AI Supercomputers
(0:07:03) - AI Advancement in Architecture and Training
(0:16:58) - Future of AI Accelerators and Chip Specialization
(0:26:38) - Scaling Open Source Models and Fine-Tuning
Advanced AI Accelerators and Processors with Andrew Feldman of Cerebras Systems
On this episode, we’re joined by Andrew Feldman, Founder and CEO of Cerebras Systems. Andrew and the Cerebras team are responsible for building the largest-ever computer chip and the fastest AI-specific processor in the industry.
We discuss:
- The advantages of using large chips for AI work.
- Cerebras Systems’ process for building chips optimized for AI.
- Why traditional GPUs aren’t the optimal machines for AI work.
- Why efficiently distributing computing resources is a significant challenge for AI work.
- How much faster Cerebras Systems’ machines are than other processors on the market.
- Reasons why some ML-specific chip companies fail and what Cerebras does differently.
- Unique challenges for chip makers and hardware companies.
- Cooling and heat-transfer techniques for Cerebras machines.
- How Cerebras approaches building chips that will fit the needs of customers for years to come.
- Why the strategic vision for what data to collect for ML needs more discussion.
Resources:
Andrew Feldman - https://www.linkedin.com/in/andrewdfeldman/
Cerebras Systems - https://www.linkedin.com/company/cerebras-systems/
Cerebras Systems | Website - https://www.cerebras.net/
Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.
#OCR #DeepLearning #AI #Modeling #ML
E29: The AI Chip Revolution with Andrew Feldman of Cerebras
Nathan Labenz sits down with Andrew Feldman, CEO and Co-Founder of Cerebras Systems, a company building a new class of computer system for accelerating AI and changing the future of work. Cerebras Systems is the creator of the world’s largest chip, at 2.6 trillion transistors. In this episode, they discuss the founding story of Cerebras, the experience of creating the world’s largest chip, and the process that goes into chip design and manufacturing for an AI-focused chip.
This episode is the first part of our hardware exploration series focused on the people building at the forefront of hardware applications in AI.
LINKS:
Cerebras: https://www.cerebras.net/
Book: The Chip War by Chris Miller
We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.
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As anyone in AI knows, the pace of progress of new releases is relentless. The AI Breakdown is a daily podcast (10-20min long) that helps us ensure we don't miss anything important by curating news and analysis.
TIMESTAMPS
(00:00) Preview
(04:27) Andrew’s story of creating the world’s largest chip and Cerebras
(07:19) What is a chip?
(08:14) The diversity of chips and what they can accomplish
(09:47) What is it like to design a 2.5 trillion transistor chip?
(12:41) The founding story of Cerebras and building the team
(14:20) Sponsor: Omneky
(23:00) What was the hardest part about building the company?
(26:11) What happens after designing the chip’s blueprint?
(27:29) The tradeoffs needed in chipmaking
(34:08) The comparison between chips and neural networks
(38:31) The generalization vs specialization of a chip
(40:11) Sparse compute vs dense compute
(43:55) Ghost in the machine
(46:54) Supply chain challenges of the Cerebras chip
(54:59) The future for chips
(58:19) Building chip clusters
(58:57) The Cerebras business model
(01:00:41) Building a chip cluster vs using a Cerebras chip
(01:02:57) Giant chips on the edge
(01:05:32) What is the edge?
(01:08:04) Andrew’s favorite AI products
(01:10:08) Would Andrew get a Neuralink implant?
(01:14:16) Consciousness and chips
(01:17:50) AI hopes and fears
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Music Credit: MusicLM
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#77 - Vitaliy Chiley (Cerebras)
Vitaliy Chiley is a Machine Learning Research Engineer at the next-generation computing hardware company Cerebras Systems. We spoke about how DL workloads including sparse workloads can run faster on Cerebras hardware.
[00:00:00] Housekeeping
[00:01:08] Preamble
[00:01:50] Vitaliy Chiley Introduction
[00:03:11] Cerebrus architecture
[00:08:12] Memory management and FLOP utilisation
[00:18:01] Centralised vs decentralised compute architecture
[00:21:12] Sparsity
[00:23:47] Does Sparse NN imply Heterogeneous compute?
[00:29:21] Cost of distributed memory stores?
[00:31:01] Activation vs weight sparsity
[00:37:52] What constitutes a dead weight to be pruned?
[00:39:02] Is it still a saving if we have to choose between weight and activation sparsity?
[00:41:02] Cerebras is a cool place to work
[00:44:05] What is sparsity? Why do we need to start dense?
[00:46:36] Evolutionary algorithms on Cerebras?
[00:47:57] How can we start sparse? Google RIGL
[00:51:44] Inductive priors, why do we need them if we can start sparse?
[00:56:02] Why anthropomorphise inductive priors?
[01:02:13] Could Cerebras run a cyclic computational graph?
[01:03:16] Are NNs locality sensitive hashing tables?
References;
Rigging the Lottery: Making All Tickets Winners [RIGL]
https://arxiv.org/pdf/1911.11134.pdf
[D] DanNet, the CUDA CNN of Dan Ciresan in Jurgen Schmidhuber's team, won 4 image recognition challenges prior to AlexNet
https://www.reddit.com/r/MachineLearning/comments/dwnuwh/d_dannet_the_cuda_cnn_of_dan_ciresan_in_jurgen/
A Spline Theory of Deep Learning [Balestriero]
https://proceedings.mlr.press/v80/balestriero18b.html
Cerebras
Andrew Feldman, one of the founders and CEO of Cerebras Systems, talks about the company's wafer-scale computer chip optimized for machine learning and about the network of chips that company has built that has as much computing power as a human brain.
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