1/ $ASAN's Results (-16% What happened?):

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.

4/ IMO, It's key to pay attention to $ASAN's result relative to $MNDY future.

I smell the competitive nature and low product moat beginning to affect each player, despite $MNDY superior metrics

Disclosure: I've positioned sized appropriately in $MNDY to make it a tiny position.
5/ The quarter was good, but in an environment where the Fed is about to taper. The market demands perfection.

I think the stock would be interesting and good for consideration around $55-$65.

@BlaineCapital has a great breakdown below on
6/6:

They are all phenomenal companies that can provide great future returns *at a lower price due to low product moat

I've written and talked about both companies recently, so I thought it was important to provide my followers with an updated view. Ty!

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More from @InvestiAnalyst

4 Dec
I went down a rabbit hole digging into one of the most successful and secretive hedge funds: Coatue Global Mgmt.

+ 483% since June 2013 with a 50% annualized return.
+ Top 1% of Tech L/S Hedge Funds
+ One of the most successful Tiger Cubs.

A thread on some interesting findings: Image
1/ I expect most people already know Coatue, but brief overview:

Phillipe Laffont (PL) is the Founder of Coatue Global. He worked for Julian Robertson (the legendary founder of the defunct Tiger Mgmt).

To learn more about Tiger, read @mariogabriele piece
2/ Phillipe Laffont was born as a rising star:

+ 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.
Read 34 tweets
4 Dec
Friday Musings:

Amidst this taper, my focus is Cloud, Security & AI/ML:
• $SNOW | $S
• $ZS*
• $DDOG*
• $PLTR* | $NET

Non-SaaS:
• $TOST | $DLO
• $GLBE
• $BILL*
• $AFRM*

*Existing positions
+Others: Watchlist

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?
Read 10 tweets
14 Nov
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.
Read 7 tweets
8 Nov
$PLTR: Palantir’s 3Q Report:

Below is a short thread of my article on the important metrics I’ll be tracking. I'm fairly neutral.

+ 3Q Rev beat: $396M; >35% YoY
+ FY guidance
+ Foundry & Gotham business lines
+ Ent. Product GTM.

Let me explain: 1/n

seekingalpha.com/article/446637…
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+>
Read 5 tweets
8 Nov
Oct to November Portfolio Holdings Update:

P-1 (Core) and P-2 (Mgmd).

1/n: I provide all my company & portfolio commentary from the past month in the thread below:
2/This month was one of my best!

Portfolio 1-Core
+ Net New Buys: $DLO
+ Added: $AFRM, $CFLT (Based on Q3)
+ Sells: $NET
+ Trims: $UPWK (Based on Q3 ER)

Portfolio 2-Mgmd:
+ Net New Buy: $RILY, $COIN (I'll explain!)
+ Trimmed: $LC, $ARCB & $SI (Explosive moves!)
+ Sells: CTS-TO
3/ Port 2:

All stocks hit new highs and had big performance MTD.
See $ZS, $AMD, $ARCB, $SI. I don't talk much on them here.

FYI this is a performance-focused portfolio for family w/ strict criteria I use such as high EBITDA etc

Below is the framework:
Read 23 tweets
6 Nov
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!

Kafta helps distribute from source > target
Read 27 tweets

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