Pete Soderling

Here is @schrockn, founder of @dagster, on stage at @AICouncilConf in 2019. Cross out "big data," write "AI," and it's a headline you've seen a dozen times this year. The reason hasn't changed much: models get the attention, but the data decides whether the models actually do something useful. We just call it "context" now. What's more, the declarative pattern Dagster was built around is now the one winning in agent work: define the outcome up front, let the model verify against it, and loop until it passes. Declare what should exist, then prove it got produced. This is what makes @PrefectIO acquisition of Dagster click: Dagster defines the outcome, Prefect runs the work, FastMCP handles access. One stack for making automated work trustworthy. Huge congrats to Nick, @floydophone, and the Dagster team!

You don't just turn on a vacuum and hoover up all your company data and call it a context layer. People get things wrong in slack. People get things wrong in email and zoom. And when presented with an answer based on a stale or incorrect context, users often don’t know the answer is wrong. An effective context layer is closely managed with all the maintenance and care of a garden. Artifacts are watered, weeded, pruned, fertilized. The knowledge base is carefully curated and managed. "The model decides how smart your agents are. The context layer decides whether they're right." Well said, @arudis linkedin.com/posts/swaroopj…

.@ashpreetbedi is living in the future. linkedin.com/posts/ashpreet…

"Waiting for perfect" is the worst mistake in AI and data right now. It's the kind of mistake that aligns you with the likes of Dilbert's boss, Lumberg, and other hilariously terrible caricatures of corporate bureaucracy. I asked @Snowflake's Bala Kasiviswanathan what leaders are getting wrong around AI and data right now and this was his answer. He said leaders are getting stuck waiting for perfect. And while they wait, they do the one thing that guarantees they lose: they don't let their teams try anything. The great ones are doing the opposite right now: building platforms safe enough where nobody can hurt themselves or the company, and then letting everyone loose to learn. Here's what "safe" means, and why it works: → The agent runs as you. Snowflake's Coco - as an example - acts on your behalf, so it inherits your roles and permissions. It can't run a destructive operation on a database that your own access wouldn't allow. Coco or not, the same rules hold. → A human stays in the loop. The agent can't touch the risky stuff on its own; someone has to say yes first. → Every move is logged. Audit trails tell you who did what so you can learn from it, and catch anyone acting in bad faith. Get those 3 right and the risk of letting people experiment drops to almost nothing. Stop hunting for perfect, build the safe playground and let your people play. 🎧 My full talk with Bala is on the @ZeroPrimeVC podcast: zeroprimepodcast.com/snowflakes-bal…

@TheEthanDing @TextQL @ZeroPrimeVC Full episode here: youtube.com/watch?v=PwhlxJ…

Own your storage. Own your business logic. Own your compute. If you're renting all three, bound to one platform, you won't scale in the agent era, says @TheEthanDing, Co-founder & CEO of @TextQL. We get into it in our latest @ZeroPrimeVC episode.

@BEBischof @Theoryvc @AICouncilConf @lancedb @motherduck Oh no. There will be more!

@Theoryvc @AICouncilConf @lancedb @motherduck Really hope i didn't peak with this event

How better to spend an evening after a great conference, than a slow boat out to sea! We had a great time at @AICouncilConf and our accompanying event Ship It, co-hosted with @lancedb and @motherduck. Our Head of AI @BEBischof led a talk track on inference systems plus moderated "Modern Inference for Modern Workloads" -- with @zedlewski of @togethercompute; Tuomas Rintamäki from @nvidia's OSS LLMs, and @charles_irl of @modal -- unpacking why a dedicated inference provider is necessary when OpenAI already exists. Dig into all of our learnings: theoryvc.com/blog-posts/ai-…

Enterprise data doesn't need to live in one place anymore with tools like @Snowflake's Coco, says Bala Kasiviswanathan, VP of Dev & AI Experiences at Snowflake. If data access isn't the constraint anymore, what should data leaders focus on instead? We get into it on @ZeroPrime.

finance teams watching devs use fable to center a div

@Theoryvc @motherduck @lancedb @AICouncilConf Amazing

From Theory, @motherduck, and @lancedb: we salute you @AICouncilConf, and the conference attendees. If you missed our boat party, we hope you’ll join us in the future. 🫡

@davemcclure @AICouncilConf Thank you Dave - have learned so much from you 👊

These days only a few big VC funds get 80% of capital & attention -- but thousands of "emerging" VCs toil away for years building their thesis, brand, and track record. @PeteSoder started @AICouncilConf in 2013 (prev Data Council) focused on core topics + job openings for data science & AI/ML engineers. He started @ZeroPrimeVC in 2020 to invest in those same ppl. Many investors have never heard of him, but they should pay more attention. This week AIcouncil.com kicks off with 3 days of great topics and over 1000 AI nerds,engineers & founders. It's really impressive what Pete has created -- a community, a conference, and a VC fund... and much more to come.

For decades, 2 things kept the internet safe: writing code was hard, and finding bugs was hard. AI just ended both of those. @Mozilla CTO @raffi laid it out in his guest essay in The New York Times called "The End of the Internet as We Know It," and this morning he takes the @AICouncilConf stage w/ me to talk about what comes next. See you at 9AM!

We are gonna have some fun 😈

It me

I'll need a green room and a bowl of M&Ms (no brown ones)

Last night at 111 Minna (an SF classic) w/ the @AICouncilConf speakers off the clock 📷. Day 02, let's go.

Last year i brewed coffee during my talk at AI council; this year i'll be forcing speakers in my track to shoot espresso before they speak

Over the weekend, I reviewed slide decks from all the speakers in the Databases and Data Engineering track at the @AICouncilConf . The talks are incredibly strong across a wide range of topics — transactional, analytical, search, vector databases; ETL/CDC; real-world case studies at massive scale, all with a forward-looking perspective on the critical role of Data Infra in supporting AI. I’m so excited about this track and expecting it to be absolutely epic!! Huge thanks to all the speakers for being an integral part of it and helping make it happen Hannes Mühleisen from @duckdblabs , @nikhilbenesch from @turbopuffer , @J_ and Pierre Lacave from @datadoghq, @iskakaushik from @ClickHouseDB , @kelvich from @databricks , Bhargavi Reddy Dokuru from @netflix , Robin Tang from @artie_labs 🙏 The track is on on May 12 (Day 1), starting at 10 AM, don’t miss it if you’re attending AI Council. @petesoder @ZeroPrimeVC