2025-04-14 08:00:00
I’ve been watching my behavior evolve as I use AI more.
Coding new projects, I started first by describing a big idea : generate a new website that ingests podcasts and transcribes them. But I quickly discovered this approach was like asking someone to build a skyscraper without blueprints—the AI choked on requests too broad & lacking structure.
So I broke the project down into smaller blocks. Write a function to download the list of podcasts in the last week from these sites. But I noticed how low-leverage that felt : I knew I was yoking a strong animal to pull along a child’s wagon.
Today, I use AI to write a basic PRD, ChatPRD is great for this. Then I ask the AI to read it and create an implementation plan. I approve the plan and then insist that the AI write tests first for each function and then test each component.
At this point, I can let the AI run wild for 10-15 minutes. With the plans and test cases, it can iterate to success itself.
As I watch the code cascade through the window, I have the same feeling of being driven by a Waymo. Enjoying the journey much more now that the toil is managed by a computer.
Top of mind for me now : how do I parallelize this so I can manage 10 browser tabs or terminal windows simultaneously? My limiting factor is structuring ideas & questions in a way to succeed with the AI working.
Outside of coding, I’m seeing myself default to AI search & reverting to Google.com when I suspect something is fishy in the AI’s output.
This is now also true beyond online searches : rather than asking a human expert, I’m experimenting with asking an AI first. Why haven’t equities moved onto the blockchain in the same way debt has?
The rub in this effort is knowing when to trust the AI. Like any human, the longer an AI bloviates, the less I believe it.
My deeper concern is what nuances has the AI excised? What am I missing from its summary?
How have you changed how you work with AI?
2025-04-10 08:00:00
Pricing is one of the most complex topics in software. Changing pricing is never simple. It is a company-wide evolution that has the potential to completely reshape your entire business & your customer relationships.
Few have executed this transition better than Barr Moses, Co-Founder & CEO of Monte Carlo.
Join us for a candid conversation with Barr as she shares how Monte Carlo transitioned from ARR to daily revenue as the core operating metric for the business.
In this deep-dive session, Barr will unpack:
This conversation is designed for founders & GTM leaders seeking to build pricing systems that:
The best pricing isn’t just about revenue—it’s about expressing your unique value proposition & creating a business engine that compounds over time.
📅 April 24, 2025, 10:00am PT
📍 Virtual
🔗 Register here
Spots are limited — secure yours today!
2025-04-07 08:00:00
With another tariff induced red-tinged day hammering markets, I wondered: Do private markets follow public ones? The data suggests yes—but with a delay & at a fraction of the magnitude.
A 1% increase in Nasdaq’s (QQQ) quarterly return translates to a 0.47% rise in median Series A valuations—but only after a two-quarter delay.
The inverse holds true as well: when public markets contract, private valuations follow suit approximately six months later at roughly half the intensity.
You can see this in the charts where the Nasdaq plummeted, but it would take some time for the private markets to react both for the median Series A, but even more for the top quartile (P75).
These relationships are statistically detectable but don’t explain most of the variance: the Nasdaq’s lagged returns explain only about 7.5% of the variation in Series A valuations
This means that while public market performance does influence private valuations, it’s just one factor among many.
Most early-stage valuation variance comes from factors entirely independent of public markets: total venture capital fundraising, cycle positioning, growth rates among top-decile companies, & most critically, interest rates.
In our earlier analysis, we discovered a hyperbolic relationship between the 10 year interest rates & Series A activity. As rates fall beyond certain thresholds, venture activity doesn’t increase linearly - it explodes.
And the correlation here is much stronger at -0.43.
For founders & investors, this suggests two practical insights: public market crashes will eventually ripple into the private markets, but with both a delay & diluted impact—and the unique attributes of individual companies & interest rates still matter far more.
The private market isn’t immune to public market gravity, but it orbits with its own momentum around interest rates.
2025-04-03 08:00:00
The new tariffs have markets down broadly. What do they mean for startups?1
In terms of first order effects, tariffs can impact a startup in two ways : the input costs and consumers’ demand.
Most software companies don’t import or source critical components of the software from abroad so the COGS / gross margin structure of the company shouldn’t change unless they rely on significant hardware purchases, for example GPUs or robotics components.
Revenue growth, however, is a different story.
Across the S&P 500, 13% of revenue is generated in Europe and 29% abroad. Technology companies are 15% in Europe and 48% abroad, topping the list of international exposure.
Assuming this trend is broadly true across US companies, we should expect at least uncertainty if not slowing growth or contraction in about one-third or one-half of their businesses.
Uncertainty breeds indecision. Investments into new areas may be challenged, slowing sales cycles, resulting in pipeline shocks, a phenomenon we witnessed in 2022.
Protectionism, the stated goal of these tactics, may have second order consequences abroad. The Buy American motif has a mirror image that may arise ‘Buy European’ may shift buyer preferences in the favor of local solutions.
Most software companies maintain healthy gross margins - on average north of 70% - and may be able to bear some of the tariffs, in addition to passing on costs to customers.
But there’s reason to remain optimistic. IT spend represents about 2.5% of GDP & Holden Spaht at Thoma Bravo published their argument for why it will grow to 4% by 2030, creating another approximately $2t in market cap along the way.2
AI’s role in reducing the costs will be big part of this shift, and perhaps the overall protectionist environment will accelerate AI adoption further, as companies seek to control what remains in their grasp - their costs - in an environment of tremendous uncertainty.
As for valuations, the 11% decline in the Nasdaq since the beginning of the year isn’t enough to offset the exuberance & record setting revenue growth of the current wave of AI companies to materially change the valuation environment - at least not yet.
While new tariffs primarily threaten startup revenue through market uncertainty rather than direct costs, the drive for efficiency in response may accelerate AI adoption, aligning with optimistic long-term growth forecasts for the tech sector.
1For a good broader macro overview, Ray Dalio’s perspective is useful. as is his book on Debt Crises
2Assuming a 6x revenue multiple.
2025-04-02 08:00:00
As startups scale, effective sales implementation becomes the difference between stagnation and sustainable growth. After analyzing hundreds of sales organizations across startups, I’ve distilled the key pieces of advice that founders and leaders should keep in mind.
Your first sales hire should generate predictable and consistent revenue, not just hunt elephants
Effective sales organizations separate lead generation, qualification, and closing responsibilities
Great salespeople don’t just ask about problems—they teach customers about problems they didn’t know they had
Effective sales pitches clearly articulate value relative to cost and competitive alternatives
Inbound and outbound sales processes require fundamentally different approaches—build your organization accordingly
Effective sales leaders build systems that consistently convert prospects to customers rather than relying on charisma
The most successful startup leaders recognize that scaling sales requires careful attention to structure, process, compensation, and enablement rather than simply adding more salespeople.
2025-03-31 08:00:00
We’re excited to announce our Head of AI, Bryan Bischof, and our first entrepreneur in residence, Philip Zelitchenko.
A practicing professor of data science at Rutgers and a Math PhD, Bryan has spent the past two decades building AI and data science practices across thought-leading startups, including Hex, Weights & Biases, Stitch Fix, Blue Bottle Coffee and many others.
Recently, he published his learnings on a year’s worth of learnings building with AI here.
When we first met Philip, he joined our Office Hours to share his practical approach to data product management—covering how to define clear data product requirements and build teams that deliver with focus.
Philip has led data and AI teams at ZoomInfo, DocuSign, and multiple startups, with a focus on execution—bringing machine learning, experimentation, and analytics into production to solve real business problems. He also led Israeli Defense Force teams training in the Negev desert.
Welcome, Bryan and Philip!
