2025-06-20 08:00:00
Systems of record are recognizing they cannot “take their survival for granted.”
One strategy is to acquire : the rationale Salesforce gives for the Informatica acquisition.
Another strategy is more defensive - hampering access to the data within the systems of record (SOR).
Unlike the previous software era where SORs built platforms on top of themselves to develop broader ecosystems (in Salesforce’s case Veeva & Vlocity), the AI shift does seem to be more defensive.
[App builders] can no longer index, copy or store the data they access via the Slack application programming interface on a long-term basis.
This isn’t the first time within the AI ecosystem that companies have shut off access to APIs. The DeepSeek launch shook the ecosystem’s assumptions.
Newer models employ distillation : asking a previous model many questions as a way of training itself for far less to achieve similar results. The result : tighter controls over AI API access. OpenAI, Anthropic, Cohere, Google are just some of the foundation model companies that now have clauses explicitly banning developing competitors via the API.
Some startups have found terrific success by partnering with SORs : Abridge, AI for clinical documentation, has grown tremendously through its partnership with Epic. ServiceNow announced its intention to acquire MoveWorks for $2.85b.
AI has increased the stakes because the underlying workflows that have powered software are changing. BDRs no longer manage one email account, they manage five or ten or more. The growth rates of AI companies are faster than ever, & the context/data stored within systems of record is some of the most valuable assets in the market.
The combination of these three forces will drive more M&A, bigger outcomes, & greater defensive behavior as incumbents search for the right strategy for their business. For startups relying on big partners, the potential for platform risk has never been more acute.
2025-06-18 08:00:00
DuckLake is one of the most exciting technologies in data.
While data lakes are powerful, the formats that manage them have become notoriously difficult to work with.
“I think one of the things in DuckLake that we managed to do is to cut, I want to say like 15 technologies out of this stack.”
How does it achieve this? Instead of building a custom catalog server, DuckLake uses a simple, elegant idea: a standard database to manage metadata. It uses a database for what it’s good at. This clean architecture allows DuckLake to manage huge data lakes—with millions or even billions of files—across AWS S3 or Google Cloud Storage.
This simplicity also delivers incredible performance. In tests, DuckLake achieved sub-second query planning on a petabyte of data with 100 million snapshots—a scale that other systems can’t handle.
DuckLake speaks SQL, the lingua franca of data. Its architecture provides full ACID compliance, so concurrent reads and writes are handled seamlessly, allowing entire teams (and their AI agents) to work on the data lake simultaneously.
By returning to first principles, DuckLake delivers the power of a modern data lake without the complexity. Its simplicity and performance make it a vital part of the future of data.
2025-06-16 08:00:00
Databricks seems to be closing the gap on Snowflake faster than expected.
Last week Databricks shared some important updates on their business which allows us to compare the progress of the two companies.
Quarterly revenue between the two company shows nearly identical slope, two parallel lines. Snowflake recently exceeded $1b in quarterly revenue mark while Databricks just touched $750m and is targeting $925m for the next quarter.
Snowflake’s revenue growth rate has been on a long glide path to nearly asymptoting at 25% year over year. Databricks saw a resurgence in their growth rate from mid-23 to early 25, from about 50 to 60% - exceptional in a company at this stage.
This growth rate was catalyzed by the resurgence of interest in AI workloads after the launch of GPT-3.5 (the dawn of the modern AI era) in late 2022 and a rebounding software economy in early 2023.
Comparing the number of large customers generating more than a million per year for each company, Snowflake has about 90 more large customers.
Databricks continues to operate at about 15% better gross margin primarily because customers operate their own compute when using Databricks, whereas Snowflake bundles the compute and storage, compressing margins.
Databricks achieved profitability for the first time in the most recent quarter. Meanwhile, Snowflake tends to operate between -30% to -40% net income margin. In addition, Snowflake has heavy stock-based compensation expenses that creates sawtooth patterns in their profitability.
The gross margin impact of bundling cloud services is a contributor, but without additional insight into the relative sales and marketing and research and development budgets of the two companies, it’s hard to ascertain exactly the reason for the delta.
As the battle has continued, the distance between the two companies has narrowed & the future promises more of these two data decacorns duke it out. Customers win when market leaders compete this intensely.
2025-06-13 08:00:00
The Seed Surge of 2021 will lead to a raft of acquihires.
In 2021 the total number of US software & AI seeds jumped from 2900 to 4300 - a 49% jump. Seeds fell to about 3000 creating a seed tabletop.
Series As moved in lockstep both on the way up and the way down - creating a squeeze.
These data form part of a longer term trend of a greater number of seeds but a relatively fixed number of Series As.
The result?
An 86% reduction in the seed to Series A conversion rate. Part of this may be seeds are seed-strapping : raising a seed and continuing on profits, catalyzed by the efficiencies of A.
AI is another component : pre-AI and post-AI companies, which are roughly demarcated by the launch of GPT 3.5 in late 2022. Retooling products with AI takes time.
Even with those factors, the amount of “excess” seeds (quantity more than the Series A market can handle), continues to grow, resulting in the decreasing conversion rate.
This dynamic mirrors 2015 when a surfeit of seeds drove a steady increase in the number of acquihires.
Acquihires, acquisitions typically under $20m for talent, may become de rigeur as incumbents seek to bolster their teams with AI talent & update existing products with new capabilities.
2025-06-10 08:00:00
Doctors and security research have more in common than you might think.
Doctors defend human bodies against an ever-shifting landscape of viruses & infections. Security researchers do the same thing, but at massive scale—protecting thousands of servers instead of a single patient.
The doctors’ responsibility are to defend a human body from an ever-shifting landscape of potential viruses and infections. Each human body is slightly different. The research around human health evolves all of the time as well as the research around potential infections.
Doctors with AI are 10 percentage points more accurate delivering diagnoses than those without AI.
Vulnerability management, the practice of identifying the security vulnerabilities that might be exploited is exactly the same thing But at much larger scale because instead of a single patient, security researchers are managing tens of hundreds of thousands of Servers, computers, routers and other kinds of infrastructure, each with their own uniquenesses at large companies.
Prioritizing the most important issues to address is critical and some critical severity vulnerabilities might be relevant for one company but not relevant for another company, just like A patient might be genetically predisposed to a condition where another one may not be.
In security like medicine, the ability to respond quickly to the most important issues separates the strong from the compromised.
This is exactly the challenge Maze is solving. Founded by an experienced team from Tessian, Elastic, & Amazon, they’re building AI that thinks like a senior security researcher—considering your company’s unique topology to prioritize vulnerabilities that actually matter.
Maze have replaced rules-based systems with AI that considers the company’s unique topology & infrastructure To prioritize the most important vulnerabilities & understand the impact of potential breaches.
Wiz, CrowdStrike, Orca, and other systems will produce a team of three AI analysts. Two AI analysts will determine whether or not the issue is exploitable or not, and how urgently to fix it.
Agentic Security is the future of security. We’ve seen tremendous results from working with Security Operations Center Automation, with our portfolio company, DropZone.
Maze is seizing the opportunity to transform the $16b vulnerability management market. The company is hiring!
2025-06-09 08:00:00
Where venture capital flows, innovation follows. And for more than a decade, few faucets have been watched more closely than Y Combinator. An analysis of their investment patterns since 2020 doesn’t just reveal the accelerator’s strategy—it provides a map to the entire startup ecosystem’s next chapter.
With Demo Day approaching this week & inspired by Jamesin Seidel’s YC Series A analysis, I wondered how YC investment patterns have changed since 2020.
Cybersecurity and industrial/manufacturing are the two fastest growing categories. Education & life sciences are right behind. The Wiz acquisition and the overall growth rates of security companies as a durable budget within software spending has propelled security more broadly. Similarly manufacturing startups have seized on the tariff-induced reshoring opportunity.
B2B companies have increased their share from roughly 80 to 90% over the last five years, which is a parallel to the broader venture industry.
Crypto/web3 remains around 5% of investments. The 2022 spike followed the Coinbase IPO in 2021. It’s a steady but not a very large fraction of companies.
AI companies on average raise a little bit more, but the delta is not yet statistically significant - even though AI companies broadly do raise a premium.
Ultimately, YC’s portfolio mirrors the broader industry’s shift toward pragmatism. The significant growth isn’t in speculative tech, but in essential tools for manufacturing, security, and B2B. The takeaway is clear: the surest path to funding runs straight through solving a customer’s most expensive problems.
