CEO / co-founder @larridin Previously founded / built / sold DynamicSignal & Adify Early at Flycast & comScore Helped Carbon Health, Udemy, TubeMogul, etc...
California
Joined January 2008
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- Following3.1K
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The biggest problem holding back AI right now: cost is exploding, and almost none of it is tied to business outcomes.
We all know SOMETHING great is happening. Nobody's quite sure what.
Today we fix that — introducing Larridin Token Spend & Insights.
larridin.com/blog/token-spen…
Russell Fradin retweeted
We actually have data on this across 15k engineers.
Some of the top spenders are your best and most productive employees. Blanket spend limits limit your BEST people!
Ok so at @cxodev we talk to a lot of people about their AI spend and individual cap here’s what we tell them:
1. Capping your top spenders is counter productive because 99 times out of 100 they are the biggest impact
2. It’s easy to manage top spenders because you can find them and talk to them about what they’re doing and make it more efficient
3. “Budget divided by headcount is easy but not optimal” - @zachdavis
4. The good AI users will be extremely annoyed by this and do unnatural things so no one comes and bothers them about it
5. 👏 you’re 👏 under 👏 using 👏 your 👏 session 👏 data (to tell where tokens are going)
6. Yes, 2027 is the year when every CFO is going to ask where all the ROI from this spend is
7. No, “we cleaned up tech debt” is not enough
8. Again, 80% of your budget leverage is in R&D, elsewhere you’re probably underutilizing AI. Most trailing orgs are really far behind in adoption
9. Go find your spend centers and eval cheaper models. Probably higher impact than a global cap
10. Your true AI talent will leave if they can’t build / work in the future (uncapped)
An alt proposal to caps: use it or lose it. Pull access entirely for anyone that shows no interest in AI, pour that budget into the people who are driving impact.
Focus on the R in ROI not just the I.
Russell Fradin retweeted
Larridin’s Engineering AI Impact Report: Q3 2026 is out, and the numbers show a shift that’s moving faster than most leaders realize. AI-written code is becoming the majority, spending is getting more disciplined, and teams are reorganizing around output.
Results include nearly 2x growth in AI use in pull requests; a sharp drop in spend growth per month; and nearly half of teams reorganizing across specified periods. Per-engineer AI spending varies by 10x between high and low-end spend, and AI-native engineers get twice the bang per buck spent on AI vs. low-AI engineers.
If you're an engineer, engineering manager, or influencing AI usage at your company, this report is a must-read.
larridin.com/data
Russell Fradin retweeted
How engineering leaders are changing the org chart for an AI-native SDLC:
Our survey:
• 40.8% moved to product/outcome teams
• 19.4% changed seniority/role mix
• 15.3% added roles in verification, agent platforms and AI enablement
larridin.com/data/engineerin…
This was a lot of fun!
"How work even gets done in your organization..."
Thanks to @sophiadew for interviewing Larridin CEO and Co-Founder @rfradin on news outlet @MTSlive.
As I type this I am flying over the Pacific Ocean streaming my 16 year old's soccer match on my laptop while I work on my iPhone on a plane with Starlink. It is magic.
Russell Fradin retweeted
@SurveyMonkey ran AI atop their curated data. 14% accuracy.
Nine months of context engineering later, 98% accuracy.
Meenal Iyer, SVP, Data & Enterprise AI at SurveyMonkey, told us how her team closed the gap.
Watch how SurveyMonkey built the context layer that took their AI from 14% to 98%.
Full video here: youtube.com/watch?v=CG5djzYN…
Big news from us today! @Larridin is now live and available through @googlecloud!
Larridin is proud to announce that the Larridin platform is now available through @GoogleCloud Marketplace. Advantages:
Simpler procurement and billing
Private offers, agreed with Larridin
Potential use of existing spending commitments
Onboarding support from Larridin
Larridin answers the question every CxO has to be ready to answer: What are we getting from our AI investment? Plus Developer Intelligence and Workflow Intelligence.
Reach out to us to learn more.
Links:
larridin.com/marketplace
Great post from @jasonlk on @Larridin and how people are leveraging our tools in multiple areas. Thanks Jason!
saastr.com/saastr-ai-app-of-…
Is there somewhere I can bet that, within the next 2 years, some Senator will want to outlaw data centers in space because it is taking away an important source of property tax revenue for local communities? And he will announce it standing on stage with mayors of communities who had worked to block data centers in 2026 / 2027?
Breaking News: Google is planning to launch an experimental satellite into orbit in Big Tech’s first move toward A.I. data centers in space. nyti.ms/3V08lJo
Extremely excited for my friend @gaganbiyani! The Horowitz Andreessen Academy has a chance to meaningfully impact the evolution of education, something Gagan has been working on for almost 20 years!
Good luck on your partnership with @bhorowitz, @pmarca & @a16z!
Was the @AmericanAir flight delayed 3 hours with basically no communication? Yes!
But, was the food on the plane also disgusting? Yes!
But, was the WiFi expensive and slow? You won’t believe this, but ALSO yes!
Astonishing how much worse it is vs United on my flight out…
I did have one of my wisdom teeth pulled without any pain meds due to an infection. It was EXTREMELY painful. 0/10. Would not recommend.
I think @rodriscoll nailed the math here for CFOs. AI is the fastest growing OpEx line item CFOs have seen in their careers. It, alongside the the rest of the software stack has to be audited.
At @Larridin we are seeing millions in AI spend optimized each year across our customers. The next AI category isn't another model, it's audit, measurement and optimization.
What do you cut, what do you fund, where do you train and what do you scale...
Companies are starting to think about next year's AI budgets, and the big question is what happens to overall spending. There are two forces at play.
1. There are maybe 5% of companies who tokenmaxxed earlier this year and are now “tokenminimizing” like crazy. Coinbase is the poster child here and is pointing to a 50% reduction in spend from the peak.
2. And then there are the toedippers, the 95% of companies who have just started on AI. If even 25% of those companies start to expand, their growth will swamp the reduction from the tokenmaxxers.
Tokenminimizers vs toedippers will be what decides FY27 budgets.
Everyone loves @meetgranola (nice work @cjpedregal) so I thought it would be interesting to share some data we see from people using it most effectively!
With Larridin data, we found that 84% of Granola users don’t use Granola, or any AI-powered tool, for meeting follow-up. But heavy Granola users, who do use Granola for follow-up, are 27% faster than light users.
And they have less friction, because all the work is done in one tool. Larridin customers can see that there’s a training opportunity here: They can get more work done, faster, by using Granola’s full capabilities.
Russell Fradin retweeted
AI is still unevenly distributed.
With the same company, tools, and prices, the gap between the curves is skill.
AI spend alone can't tell you much about productivity. Spend and output only correlate for AI-native engineers and with equal spend, deeply AI-native engineers ship about twice as much as partial adopters.
We also see that while partial adopters see real returns, marginal payoff drops by about half once spend passes ~$600/week. Meanwhile, the deeply AI-native cohort hasn't hit a ceiling where more spend is less productive (marginally or effectively).
And for low-AI teams, extra dollars buy activity without output.
Today the spread between AI-native engineers and everyone else is ~2x at equal spend. The data suggest that gap is not done widening.
cc @rfradin @jlarrison @aktwits @Larridin
