Let's begin w/ positives:
• Current Q3, 70% YoY growth
• 50k Customer growth was 132% (highest since 2020)
• 5k & 50k Customers are sticky, spending more: DBNRR of 130% and 145% respectively to last Qtr
Where were the negatives? 1/6
2/ Reason the stock is down:
• Revenue of 70% YoY, slight deceleration
• Q4 Guide: 54% YoY, 5% sequential slowdown from 12% QoQ from Q1-Q2
• FY Guide: 64%, light
• FCF: -29% & high S&M/Operating exps remaining the same
+ This is unacceptable when yu trade for EV/S of 60+ :(
3/ $ASAN: It turns out this indicator in my thread came true.
Enterprise customers were more likely to Decrease their spending on $ASAN over the next couple of months which is why $ASAN likely had a slow guide.
+ Obsessed with computers from a young age
+ He graduated from MIT with a Computer science degree
+ Worked for Mckinsey in Europe
+ Came back to New York to work for Tiger Global for 3+ yrs until 1999 when Tiger returned capital.
The internet has not broken yet.
Some quick thoughts 1/
2/ My priority is to focus on companies that can sustain 30-40% growth rates for a 3-5yr.
The market is looking for companies that have growth PLUS (optionality, product-lock-in, switching costs, mission-critical) to deduce the durability of a 3-yr CAGR to decide its multiple.
3/ Other things is looking for 30%+ CAGR Rev growth combined with lock-in, high land & expand DBNRR?
Alternatively, for non-SaaS companies. I'm looking for companies that are still relatively early in capturing their TAM and are showing qualities that they are market leaders?
This is a phenomenal breakdown of today's Enterprise AI/ML stack (S/O: @BatteryVentures).
I'm constructive on the tailwinds within this space and I'm long: $CFLT, $CRWD, $AMPL, $PLTR.
The report below is a great read for any AI & Enterprise SaaS Enthusiast.
Thread Summary: 1/
2/ Today's Org has a fragmented data and AI environment. Here are the different job roles required to perform certain data tasks.
For example, 1) the ETL process is going to undergo some innovation. 2) More people will become data citizens IMO.
The visual below shows it.
3/ The Data Value Chain:
The picture below explains -
i) the lifecycle of machine learning in an organization,
ii) the different applications required, and
iii) how data flows through the modern organization.
2/
- Q3: 40% YoY would be Net $30M, 35% ideal
- Q4 Rev: $405M (>27% YoY) is a minimum.
- FY 2021: $1.6B which would be roughly 40% YoY would be great.
- Sadly, I expect high SBC Costs
- Gov't contracts should boost FY 21 based on recent contracts (Army, IDIQ)
Metrics below
3/ Enterprise Clients is a BIG PART of my thesis:
Acceleration in the commercial would be HUGE. In Q2, they added 20 net new clients (booked $925M of contracts (175% YoY): Could we get around 190+ in Q3?
I'll watch Billings, RPO Growth, and Total Contract Value, ideally $4.0B+>
Confluent: The Central Nervous System that is empowering Data in Motion for the Leading Corporations.
Why Confluent could become as dominant as Snowflake as a leading Open-Source Data Infrastructure.
This is my long-awaited $CFLT Investment Thesis:
1/Thread Overview:
1) First Principles Analogy for Kafta (the foundations of Apache Kafta is needed to know CFLT) 2) $CFLT Overview and how it fits Kafta 3) Investment Thesis 4) The AI & Data Analytics Opportunity
Let's GO!
2/First, we need to know Apache Kafta?
An analogy: Imagine the task of going shopping, arriving home and you need to distribute those bought items across a BIG house FAST. Hassle? What if I gave you a robot to perform that task? Yay!