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every year anyone Matt Turck 174Benedict Evans 40Spencer Kimball 37Dylan Patel 30Amr Awadallah 29Sridhar Ramaswamy 24Johnny Graettinger 23Jordan Tigani 20Neil Zeghidour 19Noah Weiss 18
Verbatim, from the transcripts: the passages where Google comes up
The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)
Inside the Paper That Changed AI Forever - Cohere CEO Aidan Gomez on 2025 Agents
- ▶ 0:23 Matt Turck In 2017 Aidan was a third year undergrad student who cold emailed Google Brain
- ▶ 2:44 Aidan Gomez Um, so I'd been getting quite close to it and I was reading up on papers and I just kept seeing Google brain repeatedly across these papers again and again and again, researchers from, from brain. 3 times in the scene
- ▶ 3:49 Aidan Gomez Uh, the end of it, if I skip all the way to the end, um, I was leaving Google after the internship had finished up.
- ▶ 5:39 Aidan Gomez Uh, and then we were, Noam was in conversation with folks over at Google Translate, uh, which was like the second group of people who were working on these projects.
- ▶ 6:33 Matt Turck Was that, was that, uh, how Google Brain operated as a, you know, uh, 2 times in the scene
- ▶ 7:39 Aidan Gomez I've, I've been out of Google for long enough that I'm not sure how the culture has shifted.
- ▶ 11:13 Matt Turck And then, uh, you know, the much, uh, debated question of, uh, why did Google not immediately jump on this, um, and eventually famously open eyes, uh, uh, the company that that's leveraged, uh, the transformer architecture faster. 4 times in the scene
- ▶ 11:37 Aidan Gomez So it went to production inside of search, inside of translate, like the existing product suite. 2 times in the scene
- ▶ 11:37 Aidan Gomez So it went to production inside of search, inside of translate, like the existing product suite.
AI That Ends Busy Work — Hebbia CEO on “Agent Employees”
- ▶ 32:12 George Sivulka Just like you don't ping the cloud to run your Excel model unless you're in Google sheets.
AI Eats the World: Benedict Evans on What Really Matters Now
- ▶ 2:42 Benedict Evans There's an interesting kind of split in that, like, you could say that Anthropik and Claude and ChatGPT are just as good as each other or R&D Gemini, but then go and look at the App Store charts, or look at Google Trends and see which…
- ▶ 16:34 Benedict Evans And at a certain point, somebody says, no, you should, you should put it in Google Sheets.
- ▶ 19:35 Benedict Evans I mean, it's funny if you look at Google Trends.
- ▶ 23:31 Benedict Evans Of course, you know, in 1995, nobody had heard of Google. 2 times in the scene
- ▶ 30:49 Benedict Evans There's make it a feature, which is Google, Microsoft, Amazon, Google, Microsoft, Meta, Apple strategy.
- ▶ 38:09 Benedict Evans And it's clearly Google, because this is a very different way to process and retrieve information and answer questions about it. 5 times in the scene
- ▶ 40:35 Benedict Evans And it's interesting, you look at or listen to the conference calls, you know, and I'm sure you've done the chart, you've missed the chart of the CapEx, where like, Google, Meta, um, AWS, not Amazon overall, AWS only, and, um, and, and, um, 2 times in the scene
- ▶ 42:41 Matt Turck So we, we, uh, in our little, uh, tour, so we talked about, um, Apple, we talked about Google, we talked about AWS, we, we touched upon Meta, uh, a few minutes ago.
- ▶ 45:12 Benedict Evans The other avenue is, why is it that no one installs the Gemini app, or the Copilot app, or the Meta AI app, or the Claude app, or the Grok, is there a Grok app?
- ▶ 51:16 Matt Turck So do we end up with something that kind of looks like Google as a end result? 4 times in the scene
- ▶ 1:02:20 Benedict Evans You go to Google, you can imagine what the screen looks like. 2 times in the scene
Jeremy Howard on Building 5,000 AI Products with 14 People (Answer AI Deep-Dive)
- ▶ 4:37 Jeremy Howard Slight outlier is Google who've really come out in front. 2 times in the scene
- ▶ 7:52 Jeremy Howard It's like what happened to Google like five, 10 years ago.
Rewriting Success: What InfluxDB 3.0 Teaches About Scaling—and Scrapping—Your Core Tech
- ▶ 12:17 Evan Kaplan And, and then there are the, you know, there are the fabrics of the world, the redshifts, Athenas, and the big queries, which also have those same dynamics.
- ▶ 17:42 Evan Kaplan Those are the standards that the lake houses are built on and things like Redshift and, and BigQuery.
Dashboards Are Dead: Sigma’s BI Revolution for Trillion-Row Data
- ▶ 11:28 Mike Palmer So for example, uh, in our world, we needed a Snowflake, a Databricks, a BigQuery, a Redshift,
- ▶ 16:37 Mike Palmer So one of the things that changed, obviously, with the Databricks and the Snowflakes and the Big Queries and so forth is that the data volumes got huge.
- ▶ 24:23 Matt Turck So that's sort of, I was going to Google talks, but actually Google Sheets.
- ▶ 25:01 Mike Palmer And we can chat at the same time, and you can live edit it in the same way that you would do in Google Suite.
- ▶ 26:20 Matt Turck Uh, what, what, um, has been sort of specific and unique about your approach and, um, you know, since we mentioned Tableau up front, uh, you know, Looker, which was acquired by Google for. 4 times in the scene
Glean’s Breakthrough: CEO Arvind Jain on Scaling AI Agents & Search
- ▶ 0:28 Matt Turck Early in his career, Arvin spent more than 10 years at Google, which he joined pre-IPO as an early engineer on search.
- ▶ 2:51 Arvind Jain Uh, it's going to be both close, close, uh, models, uh, from companies like OpenAI and Anthropic and Google. 2 times in the scene
- ▶ 22:25 Arvind Jain Um, and, and even before Rubrik, I was used to at Google, and ironically, we were making it easy for everybody in the world to, like, get answers to their questions, but we're not helping ourselves internally. 4 times in the scene
- ▶ 23:40 Arvind Jain Uh, people are using these models to try to actually advance Google search and, and our team, like, you know, me included, most of us actually came from Google and we were seeing this big impact that the transformer technology was had, you…
- ▶ 32:17 Arvind Jain But now that we have this platform, which is connected to your Salesforce and your SharePoint and Workday and Google Drive,
- ▶ 34:32 Matt Turck Uh, so you're coming from, or you came originally from Google, uh, which was, uh, obviously the world of, of PageRank.
- ▶ 35:28 Arvind Jain And deep integrations into products like Confluence, Jira, Workday, Google Drive, and Slack, and so on and so forth.
Box’s Big AI Leap: Aaron Levie on Agents & the Future of Work
- ▶ 10:03 Aaron Levie And, um, and I think most companies are kind of navigating this well, you know, Google's an interesting one as an example, Sundar is doing an incredible job, but you can kind of feel for, for the, the challenge that he has, because if all…
- ▶ 49:57 Aaron Levie And then like literally a week later, obviously like Google's going to have an open source model that blows you out of the water.
- ▶ 59:07 Aaron Levie You know, 10 hours of Googling and I can just quickly be like, it's wrong there.
Snowflake CEO on Winning the AI Arms Race
- ▶ 0:28 Matt Turck Sridhar had an incredible fifteen-year run at Google, where he was an early employee and then rose through the ranks to lead ads and commerce.
- ▶ 10:53 Sridhar Ramaswamy Now, I worked at Google for 15 years, and what you learn over time is that there are very different kinds of software engineering that happens in what looks like a monolith.
- ▶ 11:40 Sridhar Ramaswamy But what I quickly realized is that our front-end team, the team that developed adwords.google.com,
- ▶ 21:24 Sridhar Ramaswamy We will offer Google Drive.
- ▶ 23:40 Sridhar Ramaswamy Snowflake essentially runs in what we call deployments, which you can think as a point of presence in every major data center that AWS and Azure and GCP have.
- ▶ 25:22 Matt Turck And, uh, so you're coming from a very illustrious and sort of meaty and heavy, uh, search background from Google and then, and then Neva.
- ▶ 27:14 Sridhar Ramaswamy And so that has been very, very heavily influenced by systems like Mustang, which are the basis of how Google search works.
- ▶ 30:36 Sridhar Ramaswamy I am, I am proud, especially looking back at how well we ran Google ads, especially search ads with that focus on quality.
- ▶ 37:46 Sridhar Ramaswamy Much of the early work that, um, I did at Google was again on data centric systems, on serving systems that were high performance, but also data processing systems that were incredibly high performance. 3 times in the scene
- ▶ 40:47 Sridhar Ramaswamy I still use Google via Safari quite a lot. 10 times in the scene
- ▶ 51:20 Sridhar Ramaswamy You have permissions on documents that you have sitting in Google Drive.
- ▶ 1:05:06 Sridhar Ramaswamy It's a little bit like competing with Google.
- ▶ 1:13:04 Sridhar Ramaswamy World-class, as I said, origins with things like Mustang at Google in terms of how we think about search.
- ▶ 1:13:04 Sridhar Ramaswamy World-class, as I said, origins with things like Mustang at Google in terms of how we think about search.
- ▶ 1:18:34 Sridhar Ramaswamy At the end of the day, we are a smallish public company compared to the likes of Google and AWS and Microsoft, or even OpenAI in terms of how much money we are able to put for things like model training.
Chasing Real AGI: Inside ARC Prize 2025 with Chollet & Knoop
- ▶ 0:07 Matt Turck Now, Francois is one of the legends of the space and rose to industry fame as a senior staff engineer at Google, where, amongst other things, he created the ubiquitous Keras deep learning library.
- ▶ 52:35 Matt Turck Uh, co-founders of a new research lab, so I know you don't, uh, you cannot talk about it too much, uh, but whatever you can share, including the, the idea itself, uh, you know, Francois, you've had a, uh, you know, an incredible ride at…
Why This Ex-Meta Leader is Rethinking AI Infrastructure | Lin Qiao, CEO, Fireworks AI
- ▶ 8:15 Matt Turck TensorFlow being the Google framework. 3 times in the scene
Top AI Researcher on GPT 4.5, DeepSeek and Agentic RAG | Douwe Kiela, CEO, Contextual AI
- ▶ 17:53 Douwe Kiela Like you can even see this in the citation profile on Google Scholar.
- ▶ 18:21 Douwe Kiela And so there, there was a lot of other interesting work happening at the times of, uh, Google had a great paper, uh, on realm.
From Selfie to Studio: Captions CEO on AI Video for 10M Creators
- ▶ 21:34 Gaurav Misra We, we did switch over to Whisper originally, uh, but initially we had some model, I think it was some other model by Google.
- ▶ 34:39 Matt Turck As a, only Google can do that.
- ▶ 48:13 Gaurav Misra So we're primarily on Google. 3 times in the scene
- ▶ 48:15 Gaurav Misra So Google Cloud is what we use 2 times in the scene
The AI Coding Agent Revolution, The Future of Software, Techno-Optimism | Amjad Masad, CEO, Replit
- ▶ 3:21 Amjad Masad At the time, Google was putting docs and Gmail in the browser.
- ▶ 3:29 Amjad Masad Uh, Chrome came out, V eight, the JavaScript engine, you know, the web was, was becoming a real application platform.
- ▶ 3:39 Amjad Masad And then I sort of Googled, Googled it, and I was surprised that no one had built it.
- ▶ 16:34 Amjad Masad It was, like, this open source project, and we had, like, a demo page, and I keep getting emails, and I looked at Google Analytics, and, like, we had, like,
- ▶ 36:56 Amjad Masad Um, we had started talking to Google. 4 times in the scene
- ▶ 39:44 Amjad Masad So when we, um, uh, after, after we released, uh, uh, Ghost Rider and we, we got a lot of, uh, excitement, we did this like really big deal with, with Google, um, on the cloud and AI side
Trino, Iceberg and the Battle for the Lakehouse | Justin Borgman, CEO, Starburst
- ▶ 17:37 Matt Turck Uh, but in the cloud data warehouses, obviously you have, uh, Snowflake, uh, Redshift, uh, Google BigQuery, uh, in the world of DataLex, uh, who, originally Hadoop, at some point, early Databricks.
Understanding Data Engineering in 2025 | Ben Rogojan, Seattle Data Guy
- ▶ 13:08 Matt Turck Can you, can you maybe compare and contrast, uh, if I'm a data engineer, do I do different things if I'm at Facebook or Meta or Google, uh, or Uber versus, uh, what I would do in a company outside of Silicon Valley?
- ▶ 21:36 Ben Rogojan (Seattle Data Guy) GCP I think are always a good place to start. 2 times in the scene
- ▶ 43:13 Ben Rogojan (Seattle Data Guy) You can use BigQuery.
- ▶ 47:56 Ben Rogojan (Seattle Data Guy) Um, do we need a BigQuery?
- ▶ 49:01 Ben Rogojan (Seattle Data Guy) Snowflake makes it really easy, and I'd say BigQuery does too, but Snowflake makes it really easy. 2 times in the scene
What You MUST Know About AI Engineering | Chip Huyen, Author of “AI Engineering”
- ▶ 6:43 Chip Huyen And then if it's cooked really well, it's like, okay, now everyone, we are paying too much money for, like, OpenAI and Anthropic or Google, so now we need to build our own model, like, and so, so now we invest into the model.
Dataiku's Secret to Scaling AI in Global Enterprises | Florian Douetteau, CEO, Dataiku
- ▶ 6:05 Florian Douetteau You had, uh, Google pushing new technologies, you had, like, those, uh, those days of MapReduce, those days of deep learning starting to work,
State of AI 2024: Frontier Models, AI Geopolitics, Robotics | Nathan Benaich, Air Street Capital
- ▶ 12:06 Nathan Benaich The other one is, of course, this like constant, um, fight between OpenAI, Anthropic, you know, Google DeepMind, GDM, uh, as being the main contenders.
- ▶ 16:31 Nathan Benaich And then when you look at Google has their own effort.
- ▶ 23:12 Nathan Benaich Um, so I think, like, for us in the sort of, like, we're still used to Google, and we train our minds so much to Google, like, it's a bit harder to move, but if you're in this, like, I need to learn things really fast on the fly, which is,… 3 times in the scene
- ▶ 38:11 Nathan Benaich Um, I mean, Europe has produced, like, pretty impactful deep tech companies as well over the years, whether it's, like, ASML, or, like, Nova Nordisk, or, uh, you know, Spotify, or, like, other things, right, DeepMind.
- ▶ 43:56 Nathan Benaich And, um, if you sum all the papers that, um, use NVIDIA chips versus all the papers that use TPUs, FPGAs, ASICs, Apple, Huawei, the sum of all NVIDIA papers versus the sum of everybody else, the delta is like 11 times. 2 times in the scene
- ▶ 44:12 Nathan Benaich So it was 19 times last year, so it's, it's, it's dropping a little bit, um, mostly driven by the growth of, uh, Google's TPU usage, which is up like five X year on year, but the, the chasm is massive.
Superintelligence, Bubbles And Big Bets: AI Investing in 2024 | Matt Turck & Aman Kabeer, FirstMark
- ▶ 0:21 Matt Turck Meta, Google, and Amazon are on track to invest two hundred billion in AI infrastructure.
- ▶ 3:32 Aman Kabeer Um, so for, for those of you who don't know, 2.7 billion dollar acquihire by Google of the Character AI founders who, funny enough, were already at Google before they went to start Character AI. 3 times in the scene
- ▶ 3:57 Matt Turck The three top companies, MetaGoogle and Amazon, are on track to invest two hundred billion in AI infrastructure this year. 2 times in the scene
- ▶ 26:14 Matt Turck Google had some, some interesting thoughts as well. 4 times in the scene
- ▶ 30:33 Matt Turck But on the other hand, if you hadn't invested at all during that period, you would have missed on Amazon, which was founded in, in, in, uh, you would have missed on Google that was, uh, founded in, uh, in 98 and you would have, uh, missed…
- ▶ 32:25 Matt Turck Uh, and look, but, but, by the way, on the AGI front, this, like, that whole discussion was kind of fun, um, which is that, uh, everybody's talking about AGI, so, uh, you know, uh, uh, certainly, uh, some old men, but, like, Elon said we…
- ▶ 45:57 Matt Turck Amazon and Google 2 times in the scene
Can AI Infrastructure Work Like Magic? Erik Bernhardsson, CEO, Modal
- ▶ 4:05 Erik Bernhardsson Like AWS, GCP, Azure, etc.
- ▶ 18:14 Erik Bernhardsson We use AWS, we use GCP, we use Oracle.
The Death of Big Data and Why It’s Time To Think Small | Jordan Tigani, CEO, MotherDuck
- ▶ 0:39 Jordan Tigani In BigQuery, we were very, very happy when we got the overhead down to, like, 400 milliseconds.
- ▶ 2:17 Jordan Tigani the database, the, the query sizes they were using was a hundred terabytes, and I remembered back from my time at BigQuery, um, you know, we had some of the, 4 times in the scene
- ▶ 3:28 Jordan Tigani You know, after Google came out with, you know, MapReduce and, and GFS and Bigtable, kind of everybody's...
- ▶ 3:28 Jordan Tigani You know, after Google came out with, you know, MapReduce and, and GFS and Bigtable, kind of everybody's...
- ▶ 8:45 Matt Turck Uh, if you already have, uh, you know, BigQuery in place or Snowflake or Databricks or like the whole, like, big data, uh, kind of infrastructure or modern data stack, whatever you call it, 6 times in the scene
- ▶ 10:55 Jordan Tigani And for, for, for Google, it's like, whatever, we own the hardware, we'll just throw lots of cores at it, um, but at some point, you know, like, you gotta pay for those, somebody's gotta pay for those cores, and somebody's gotta pay for…