I built a full-fledged Chrome plugin in two hours today using GPT-4 and DALL-E.

It delays access to time-wasting sites, which I prefer vs. site blockers.

Here's a link for alpha-testers:

drive.google.com/drive/u/0/fold…

And here's what I learned about GPT-4's strengths/weaknesses: 1/ First, as many others have reported, GPT-4 is much more capable than GPT-3.5. I tried this once before but was bogged down in bugs almost immediately and gave up.

I'm seeing lots of comments on LTV:CAC and "unit economics" in SaaS.

Both are helpful mental models- but I see them cited as if they convey some sort of absolute truth.

Models and metrics are to be used, not believed.

Some examples of how investors are using them wrong: 1/ First, we don't have the disclosures to calculate the "unit economics" of public SaaS companies.

S&M/Net New ARR is a laughable proxy:

*Some sales efforts take years to pay off
* Some customers come in self-serve via brand with zero CAC
*Churn and expansion confound

Just built this Hedge Fund name generator in about 10 minutes using @OpenAI chat. 🤯 Didn't write a line of code, I just went back and forth with it a bit and pasted the output into @Replit. Incredible. Here are the prompts I used, typos and all 🧵:

…undNameGenerator.jaredsleeper.repl.co 1/ "Build me a hedge fund name generator with javascript and html. It should have a "generate name" button and return a name in big, bold letter. It should be pretty. When the generate name button is clicked, it should take one word at random from two sets of words: a set full.."

E-commerce in the US is in crisis: penetration peaked in Q2 2020 and has been stagnant since at ~16% of retail spend. Where will it go from here? In this thread, I’ll break down the bull/bear arguments, discuss a potential POV and share a link to the model used to derive it 🧵: 1/ First, to level set: though macro factors (stimulus, inflation) confound absolute numbers, this chart built with Census Bureau data says it all. Since Q2 of 2020, e-commerce penetration of retail has been flat to down:

I recently spent a week in 🇮🇳 featuring a slew of startup meetings, a ten-mile run in Mumbai, a walk through Chandni Chowk and tons of time with the @SteadviewCap team. I came away feeling better than ever about the prospects for Indian tech. My five biggest takeaways: 🧵 1/ First, about that run. India’s streets aren’t for the faint hearted. As I dodged color-filled Holi-balloons, intense entrepreneurial hustle was omnipresent. Every alley seemed to be packed with independent shops and vendors of all shapes/sizes. Culturally, India grinds.

Summary of the seven notable downturns in the BVP Emerging Cloud Index (including this time around).

So far, this downturn is pressing the bounds of normal, but still normal.

Mini-🧵 on four things that are different this time and a thought on how it may end: 1/ Multiples started higher than in all but the early-2021 correction. There's still ~24% downside to the average "bottoming" multiple.

My last thread on steady-state SaaS valuations got lots of thoughtful pushback, which I'm going to respond to collectively for efficiency's sake. Here's a thread on what the SaaS doubters have wrong: 🧵👇 1/ Huge thanks to @DoxasticCap, @Post_Market, @Value_Ideas, @ExitMultiple,
@BluthCapital, @rsandler21969 et al for engaging.

@ebitdaddy90 gets props for the most representative/punchiest quote: "Assuming the average saas co will have 40% opm in public markets is pure delusion."

I don't know who needs to read this, but a friendly reminder that the typical scaled SaaS company is probably worth ~8x revenues at "steady-state," which is a decade of >15% growth away for most.🧵👇 1/ Most SaaS products are quite sticky, and some are effectively utilities- during the pandemic, some travel companies defaulted on their debt but continued paying their SaaS contracts because they couldn't operate without them!

I spent some time this week thinking through the impact of crypto on SaaS. I'm always on the lookout for companies that might disrupt the existing SaaS paradigm I've written about, in which most scaled companies are pretty safe from disruption. Crypto might change that! 🧵👇 1/ Surprisingly, I've seen few arguments that crypto will lead to a disruption of SaaS. Whenever there's a new technology/paradigm scaling rapidly and you're a fan of what is, today, inarguably a mature one- it's worth brainstorming whether there's a threat.

It's done! Here's the final thread in my scaled SaaS investing framework series. I saved my favorite topic for last: how to recognize extensible products that will create durable growth, with a few famous case studies. 🧵👇 1/ There are two ways products can be extensible: they can be natively flexible or "centers of gravity" that eventually attract other features/functionality into their orbits. I'll give a case study for each.

This one was fun! Continuing my SaaS framework series: a thread on how TAM tailwinds drive excess growth for scaled SaaS companies, with a case study on $SHOP and a list of the top five trends I see putting wind into the sails of software companies today.🧵👇 1/ A TAM tailwind is very simple. TAM penetration is a fraction (X/Y), and the faster the denominator (Y) of that fraction grows, the better. I'm not referring to product extensibility (new modules, etc.)- this is about the growth in demand for the core product.

A few weeks ago, I published my public SaaS framework. Here's the third of five threads diving deeper. When is it right to worry about obsolescence risk? I'll cover the "product model superiority" of SaaS and discuss which categories are vulnerable to obsolescence. 🧵👇 1/ First off, obsolescence is rarely a meaningful risk in SaaS. Investors often trumpet SaaS's "superior business model" given how much they love ratable recurring revenue. Even more important is the built-in anti-obsolescence- a sort of "product model superiority."

Last week, I published my public SaaS investing framework. This is the second of five threads diving deeper into each of the factors: when is it right to worry about competition hurting scaled SaaS companies? 🧵👇 1/ Competitive risks are usually overstated for big SaaS companies. A software product is a constellation of functionality, and even products in the same category that nominally compete head to head can be different enough that they aren't perfect substitutes.

Yesterday, I posted an outline of my public SaaS framework. As promised, I'm going to go through each of the five factors in detail with a mini-case study. First up: what is TAM saturation and what are the warning signs for a SaaS company at scale? 🧵👇 1/ First, some words on TAM. These three letters are essential to SaaS investing and yet are often glossed over. Shockingly (to me at least), many public companies pitch themselves to investors using poorly supported estimates from third-party research reports. 🤯

I took some time to write up my public SaaS framework and honed it down to three "negative factors" to avoid and two "positive factors" to seek. I'll post a separate thread on each in the weeks to come. For now, here's a working outline of the five things I watch for🧵👇 1/ The (now thoroughly understood) reality is that SaaS is a great business model and, at a certain scale, remarkably predictable. Net retention and sales efficiency change slowly in most cases, leading to a fairly narrow cone of uncertainty.

Some personal news: I'm pursuing an exciting new opp and on leave this summer. One upshot is I can rejoin fintwit. I've learned so much here (thanks @AltaFoxCapital , @jamesjho_ , @GavinSBaker , @DanRose999 et al) so I'm excited to jump in. My first🧵is on $ZM + $FIVN 👇 1/ I'll break down some category history, the nature of $ZM's competition with $MSFT, why I think the acquisition makes sense and why I expect Microsoft will respond with a move of its own.