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How much should quotas increase next year?

With the end of the fiscal year approaching for many software companies, management teams are running the numbers for different scenarios.

This year has been tumultuous : most startups reduced their bookings, and grinded through the first six months suffering through pipeline shocks - the result of CFO-budget pressure.

For the last 3 quarters, startups have been executing on reduced targets, attempting to squeeze more juice from their Meyers.

The Federal Trade Commission’s focus on technology has transfigured the M&A market for startups.

The Google anti-trust lawsuit & Amazon anti-trust cases may stifle those companies’ ability to acquire startups for significant sums. Facebook has faced a similar suit for illegal monopolization, which resulted perhaps indirectly in the divestment of two of its more recent acquisitions. Microsoft’s most recent acquisition of Blizzard nearly didn’t succeed & the FTC continues to pursue means to reverse it or win concessions.

In 2021, Amazon announced they had reduced prices on Amazon Web Services 107 times since launch.

This drop in prices has grown AWS into a $90b revenue business in 17 years. As storage & compute became less expensive, the economic viability of new use cases became increasingly apparent & developers built software on the cloud.

The same cost-reduction phenomenon is occurring with blockchains, though it’s not nearly as well publicized.

Yesterday, both Google and Microsoft announced their earnings for their cloud businesses. I’ve been tracking the growth rates of these companies and product lines for the last 18 months to develop a broad gauge of enterprise buying patterns after the downturn.

In the chart, it’s immediately evident that Mongo has seen dramatic increase in growth rate last quarter. We await the earnings from everyone aside from Google and Microsoft in the next few weeks.

On November 8th, I’ll share my 10 Top Trends in Data & AI at the IMPACT Summit.

Last year, I covered 9 topics:

1. Cloud data warehouses will process 75% of workloads by 2024.
2. Data workloads segment into in-memory, cloud data warehouse, & cloud data lakes.
3. Metrics layers unify the data stack.
4. LLMs change the roles of data engineers.
5. WASM becomes an essential component of web applications.
6. Notebooks win 20% of Excel users.
7. SaaS applications use the cloud data warehouse as a reading & writing database.
8. Data observability becomes a must-have.
9. The decade of data continues.

How many of those turned out to be true? I’ll be scoring them!

The patois of data teams has become a dialect of modern engineering teams because the commonalities in the stack.

This convergence signals how far data teams have evolved into core engineering teams. Machine learning’s demand for data has accelerated this movement because AI needs data to function.

Data teams receive tickets from their internal customers & develop data products that serve both internal & external users, much like a classic product management & engineering team.

On Thursday, November 2nd at 9:30 AM Pacific, Office Hours welcomes Raj Sarkar to talk about Unleashing Outbound Marketing Fury.

Raj started his career as a marketing manager at Cisco focused on the SMB market. He then jooined Google to become the lead product marketing manager on Google Cloud Platform (GCP). Next, he headed marketing at Atlassian for Trello, Confluence, Jira for Business Teams & other products. Last a short stint at Palo Alto Networks before becoming CMO at 1Password.

Within the last few weeks, we’ve seen two significant exits in Startupland, the first new pricing information we received in many quarters.

Klaviyo was the first half for IPO in at least a year. Atlassian’s acquisition of Loom is also the first unicorn sale in about the same time frame.

Naturally, many of the first analyses compared the last round valuation in the private markets to the exit value. In both cases, the exit values amounted to less than the last private round - discounts of 36% & 20%.

Last week, Office Hours hosted Oliver Jay, former CRO at Asana to talk about Avoiding the PLG Trap. More than 900 people registered for the event.

The full recording is here or embedded below on the web.

Oliver shared some trenchant insights about his experience leading the GTM teams to success. These are three of the lessons that stood out to me espcially managing the transition from product to sales-led growth (PLG -> SLG).

Over the last few weeks I’ve been experimenting with chaining together large language models.

I dictate emails & blog posts often. Recently, I started using Whisper for drafting emails and documents. (Initially there were some issues with memory management, but I’ve since found a compiled version that works well on my Mac called whisper.cpp)

After tying Google’s Duet I wondered if I could replicate something similar. I’ve been chaining the Whisper dictation model together with LLaMA 2 model from Facebook. When drafting an email, I can dictate a response to LLaMA 2, which will then generate a reply using the context from my original email.