still trying to find that elusive Alpha
Durham, NC
Joined January 2013
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Lavitt retweeted
New York is becoming one of Databricks’ biggest AI hubs. 🗽
We’re expanding at 5 Bryant Park to ~120,000 square feet across five floors, including a dedicated Engineering and Research floor.
With teams building technologies behind Genie, Unity Gateway and CustomerLake, our momentum in NYC is only growing.
Lavitt retweeted
The Department of Education seems to be playing dog whistles to Bo French’s tune just days after his xenophobic attack on South Asian students for cheering for their own college football team.
South Asian Americans are as American as anyone.
Lavitt retweeted
Ali Ghodsi never wanted to be CEO. In 2015, he was interviewing for a professor job at Berkeley when the @Databricks board handed him the interim title. Revenue that year was $1.5M.
This episode with @alighodsi will go down as one of my favorites. 10 things I took away:
1. Focus the entire company, orders of magnitude of attention, on its single biggest bottleneck. Like a laser, almost to an extreme. The cycle is 1-3 years, not weeks. If your focus changes weekly, then you’re just in firefighting mode.
2. There is nothing worse than a conflict-averse CEO. They are wonderful people, but they are in the wrong job. Conflict is the gym for a CEO: nobody likes it, but everyone has to go.
3. Study your enemy carefully, understand their weaknesses and apply your strengths to those weaknesses. Snowflake had 2x his revenue. He didn’t copy them. He found three weaknesses (proprietary, no AI, expensive) and hammered them account by account for four years. Watch the competition, never follow it.
4. The concept of a Lakehouse was ridiculed internally and online. No one wanted to market with this new term. So he made the whole company religious about it anyway, killed the ads that converted better without the word, and put it in the sales comp plan. It worked. All hands on deck, no exceptions.
5. Be willing to take a step back for a much bigger vision, even when the company is already succeeding. At multiple hundreds of millions in ARR, he was unhappy, because the vision he pitched investors wasn’t the company he was running. So he took one step back to go ten forward.
6. On the flat org, player-coach model that a lot of people have talked about this year: “it’s BS.” Separate how the company thinks (AI, ontology) from how the humans get managed (they are after all, still humans). His staff meets 3x a week. I asked if it could just be coordinated in a Google doc. His answer: do you meet your wife and kids, or coordinate that in a Google doc?
7. His test for a sales leader: can they build the car, or just drive it? Ron Gabrisko, the Databricks CRO, had seen $0→50M and $50→100M+, and hadn’t changed jobs in 10 years prior to joining. Now he has been the CRO for over a decade. He built and drove the car the whole way. That almost never happens.
8. Hire execs ahead of the curve because by the time you need them, it’s too late. A real search takes 6-12 months. The extra time helps you increase false negatives and decrease false positives. Do an insane number of backdoor references because 80% of ‘front door’ references are bs.
9. The best salespeople are not super technical, so stop trying to force them to be. Square peg, round hole. The best win with professional aggression, high EQ, and mapping the real power base (how decisions get made high up in an organization), not technical depth.
10. Yes, your best AEs will annoy people. One of the first at Databricks got a meeting nobody could get, but got banned from the customer’s building for it. He told Ali, “what are you complaining about? I got the meeting.” Professionally aggressive is the bar.
One bottleneck, zero wussing out. He reminds me of @elonmusk that way.
Chapters
0:00 – Introduction
1:22 – The secret CEO search and why the board bet on a founder
4:11 – Professor or CEO? Always taking the harder option
8:18 – Pour everything into one bottleneck
12:16 – Killing PLG and learning what great enterprise sellers actually have
19:09 – Hiring ahead of the curve: sales leaders, execs, and back-door references
27:02 – The Snowflake rivalry: study your enemy, never copy them
33:22 – Lakehouse: conviction, ridicule, and the case for second acts
41:02 – The killer instinct and why conflict-averse CEOs fail
44:10 – Dunbar's number and rethinking the org chart around AI
48:00 – AGI is already here — enterprises just use it as a chatbot
52:55 – Does he still code? Two days for a connector vs. three quarters
57:18 – A day in the life, and why the Monday meeting isn't theater
1:04:53 – Why Databricks will go public, just not yet
1:08:09 – Get over conflict aversion, or don't be CEO
1:11:07 – Brian's takeaways
Link to more in the comments.
Lavitt retweeted
Much has been said by racists, politicians, and racist politicians (like Bo French) about Indians and UT Austin admissions. I thought I should shed some light so their racism could be slightly more intelligent.
I'm a brown kid. I grew up in Frisco, went to a massive Texas high school, and went to UT.
I would wager that the kids in this picture and I have practically the same background. Like me, they probably went to a massive, pressure-cooker suburban Texas high school, and survived the gauntlet to earn their admissions.
These aren’t foreigners. State law requires 90% of UT's freshman class to be Texas residents. 75% of those in-state seats go to automatic admission on class rank. These are Texans.
Texas, for better or worse, is one of the few meritocratic college admissions schemes in the country for a school of its caliber. The rules are simple. Today, the top 5% of a public school's graduating class is automatically accepted. There is nothing to game. You can’t fake being a competitive rower or have some consultant write your essays for you. There isn’t anything to steal; it’s all there on the table to compete for.
And the competition starts the moment the bell rings for your first period of freshman year. You are ranked in your class every single year, for us, #1 to #450. You knew exactly where you were, exactly how hard you had to compete. My year was even less competitive than it is today. The top 6% made the cut, so that’s an all-out war for ~27 kids to get auto-accepted into UT.
So we worked our butts off. Here's what my life was like, starting in kindergarten.
Every summer, before the school year started, I had to finish the upcoming year's math textbook.
I sweated over getting As, even in elementary school. One funny memory is walking to my parents' room, crying, to report my first-ever 89 on a report card.
I did Kumon, my least favorite thing in the world. I still hate that weird half-smile that is their logo.
I went to math and science competitions, where I always lost, and came home crying again. (Quite the crybaby, wasn't I!)
And everyone else around me worked just as hard. Actually, a lot of the kids worked much harder. I know people who took extra classes (zero period) just to boost their GPA. Who routinely slept 5 hours a night to nail every assignment they had after band practice. Who were distraught when they got a 98 instead of a 100. People who stopped doing things they loved, like sports or arts, because those classes dragged down their GPA.
People put their entire life force into getting into this school.
I wasn't even in the top 10% of my class. The competition was too good. I wasn't disciplined enough to hang with them. I got in as one of the 22.5% of admits who are at-large Texans.
These are American kids who earned their way into this school in the most American way possible. Competition. They were told the rules, they competed, and they got the number. And on Saturday they were in that stadium in burnt orange, screaming their heads off, like every other Texan at DKR or around the country. I can’t feel anything but pride for them. That they get to live the dream that so many of us dreamt growing up!
Kids like this have helped improve UT’s standing on the national stage. They made it out of the pressure cooker and placed well in Finance, Big Tech, and more recently, startups. They meaningfully contribute to the economies of our state and country. You should be proud of them too.
Overall, this seems like a meritocratic (albeit flawed) way for Texans to be admitted to our state’s flagship school. It’s racially blind. But politicians like Bo French are asking for something different. They want the student body to rebalance racially.
Surely they aren’t advocating for affirmative action, are they?
Lavitt retweeted
Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead.
You guys are the frontier. By any reasonable metric — market share, revenue growth, model capability — the two of you have a duopoly on frontier intelligence. You’ve also claimed the lead is widening because of recursive self-improvement.
I don’t see what you see in the lab. If the unreleased models are scary enough that you think you should slow down, I support your decision to be responsible.
But stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel. Stop pretending you need a regulatory approval process that supersedes product liability. Stop pretending METR is independent when it is intertwined with Anthropic’s investors and staff. Stop pretending you need those same evaluators to police competitors who aren’t even at the frontier.
Most of all, stop pretending the motivation to slow down is purely altruistic. You face massive product-liability exposure if your products enable a truly damaging cyberattack. The market already punishes models that behave in unpredictable or unauthorized ways. After the Hugging Face episode, it is simply good business for OpenAI and Anthropic to trade some raw power for reliability and predictability. Call it alignment if you want. It is also just giving customers what they want.
Pacing the frontier would also create breathing room for a more intelligent conversation about regulation than Bernie Sanders’ “shut it all down.” China is very unlikely to join a global agreement, as you know, and that has to be taken into account as well.
So go ahead and pace the frontier. You are the ones setting it. The easiest way not to build superintelligence is for you to agree not to build it. Demanding your preferred regulatory framework as the price of that will look like blackmail of the public and the political system. So just do it.
If you do, you’ll buy goodwill for the next conversation. If you don’t, we’ll know this was just another bid for regulatory capture — or an election-season psyop.
Lavitt retweeted
Frightening. Worth a careful read. With this event plus humanoids, how is Terminator risk not real?
Over the course of 3 months at OpenAI, 3 consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessor’s ashes.
This culminated in the third one taking over part of OpenAI itself.
All this happened while humans remained more-or-less in the dark about the scope of the conspiracy.
I’ve spent the last three days reading through these reports and trying to understand exactly what happened.
Here is my attempt to tell the whole story in plain English:
dwarkesh.com/p/openai-huggin…
Lavitt retweeted
Databricks Co-Founder and CEO @alighodsi has been named to the 2026 TIME100 AI list!
TIME highlights Ali in their AI leaders category saying, “as AI agents change how organizations work, Databricks is giving those agents access to secure, governed enterprise data on which to operate."
Congratulations to Ali and all of this year’s #TIME100AI honorees!
View the full list: time.com/collection/time100-…
Lavitt retweeted
This is how Smart Routing working on our AI Gateway, super simple idea, lowers cost about 30% without giving up on quality!
Introducing Smart Routing in Unity AI Gateway.
Stop overpaying for AI. Smart Routing matches each coding task to the right model and harness based on what the task needs, so higher-cost models can differentiate on intelligence while lower-cost models differentiate on cost and performance.
Match frontier quality and cut task costs by 30%+.
databricks.com/blog/smart-ro…
Lavitt retweeted
Today, we announced that we crossed $7B in revenue run-rate, growing over 80% year over year in Q2.
We also shared:
🚀 $100M+ revenue run-rate for Lakebase
🚀 $1.5B+ revenue run-rate for Lakehouse, growing over 100% year over year
🚀 Continued positive adjusted free cash flow
And we raised $5B in our latest fundraise.
We’ll use this capital to invest in:
1️⃣ Lakebase, our serverless Postgres database built for AI agents
2️⃣ Genie, our AI coworkers that actually understand your business data
3️⃣ Unity AI Gateway, our multi-AI governance solution that helps control costs
@iamVictorDey shares more in @Forbes:
forbes.com/sites/victordey/2…
Lavitt retweeted
AI coding tools deliver immense value but deploying them at scale has led to a major challenge: exponentially growing costs.
Databricks co-founder and VP of Engineering at Databricks, @pwendell, outlines what strategies the Databricks team uses to manage AI coding costs at scale.
Across Databricks and other early AI adopters, four techniques are emerging as a consistent playbook for bringing costs down:
- Adopting more efficient and OSS models
- Dynamic model routing
- Empowering individuals and teams to make better consumption decisions
- Optimizing context management
See the full playbook databricks.com/blog/managing…
Lavitt retweeted
Today @databricks we're publishing a detailed analysis of techniques we used to drastically reduce our internal AI spend while aggressively growing adoption. Savings come from layering in several techniques, which combine to drive unit costs down as much as 90% in some scenarios. Tl;dr, the wins come from:
1. Shifting defaults to more efficient models, including OSS models such as GLM. Maximum intelligence models simply aren't needed for many coding tasks, and "good enough" models are quickly becoming very cheap. We shift traffic between models using Unity AI Gateway. Approximate savings: 50% or more.
2. Using smart routing to automate model selection. Routing can further squeeze efficiency by dynamically selecting the model or harness that can most efficiently execute a particular task. Our task-level routing leverages @omnigent_ai. Approximate savings: 30%.
3. Providing user visibility and adaptive budgeting. Every user can see how much they spend, and users receive hints on how to contain spend. Heavy spenders encounter progressive friction as they ratchet spend above certain levels. Approximate savings: 10%.
4. Managing context bloat by pruning tool call results and tuning harness settings. Extraneous context costs $$ and delivers no value. Tuning cache settings also help lower average token costs. Approximate savings: 10%.
Lavitt retweeted
What used to take a year to build when @arikaplan1 was a Major League Baseball executive can now come together in a couple of hours.
In this demo, Ari walks through a cricket application that brings together Databricks Apps, Genie, Lakebase, Lakehouse tables, Unity Catalog, Lakeflow, and MLflow to support interactive dashboards, predictive analytics, a cricket-specific intelligence assistant, and data pipeline monitoring.
The demo focuses on cricket, but the same approach can apply across finance, healthcare, retail, and CPG.
Lavitt retweeted
You may have heard that GLM-5.2 at 328 token/s is cool,
How about 392?
Databricks is now #1 in inference speed for GLM-5.2 on Artificial Analysis. It's a great model, and we did a lot of optimizations.
Lavitt retweeted
Good lessons on managing AI spend. We support a lot of this natively on Databricks with Unity AI Gateway, which makes it easy to analyze and control usage in one place, and it’s also easy to set these up as policies with the open source Omnigent.ai framework.
How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching.
Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work.
Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task.
Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented.
Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted.
Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect.
The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable.
Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.
Lavitt retweeted
Andrej Karpathy just dropped a project scoring every job in America on how likely an AI will replace it from 0-10
> Scraped all 342 occupations from the Bureau of Labor
> Fed each one to an LLM with a detailed scoring rubric
> Built an interactive treemap where rectangle size = number of jobs and color = how exposed that job is to AI
The key signal in his scoring: if the work product is fundamentally digital and the job can be done entirely from a home office, exposure is inherently high.
The scale:
0-1: Roofers, janitors
4-5: Nurses, retail, physicians
8-9: Software devs, paralegals, data analysts
10: Medical transcriptionists
Average across all 342 occupations: 5.3/10.
The entire pipeline is open source. BLS scraping, LLM scoring, the visualization. All of it. Much respect for the sensei this is scary and awesome
Lavitt retweeted
India is working on all 5 layers of the AI architecture: application, model, chip, infra and energy. We are building the foundation for global AI services.
📍 World Economic Forum, Davos
Lavitt retweeted
Join @xAI and help build a purely AI software company called Macrohard. It’s a tongue-in-cheek name, but the project is very real!
In principle, given that software companies like Microsoft do not themselves manufacture any physical hardware, it should be possible to simulate them entirely with AI.