Cosimo - e/acc ⏩

🇮🇹 VC @ Neva SGR

[Cosimo - e/acc ⏩ @CosimoScarinci]

@boardyai Making intro is one of the main activities of VCs. It would be great to give it a try.

[Lucas Vaz @lucasbagnocvaz]

https://t.co/wkTd7Thw67

[Nico Wittenborn @ncsh]

We are in the age of consensus capital:

1- Almost 75% of all LP $ raised by 5 funds

2- Almost 75% of all VC $ invested in 5 companies

[carried_no_interest @carrynointerest]

WILL AI DESTROY YOUR SOFTWARE COMPANY? OR: WHICH SOFTWARE COMPANY SHOULD I BUILD?

I'm a software private equity investor. I look at 10-20 software businesses each week to acquire. I see good, bad, and everything in between.

***I am reposting this since apparently all the pods think software is cooked (it isn't)***

I keep getting this freaking dm 'Now that I can build ANY software company with AI (lol) what should I build?'

I've also seen this nonsense going around of 'software is dead'. I'm going to address both of these here.

First things first, software is not just software. Software exists across a variety of dimensions/attributes. We have to cover these as they play a massive role in which software will be 'fine' and which software is 'at risk'. This should also tell you indie builders out there which software to go after.

For the purposes of this exercise, I'm going to use a darling example company to analyze these attributes: Calendly.

Here are some of the attributes I think about when I look at acquiring a business:
1) 'time to yes': How long does it take to sell your software after a customer learns about it? Is it a 3-6 month sales cycle? Is time to yes instant? DO I NEED TO TALK TO SOMEONE ON THE PHONE TO PURCHASE IT? If, like calendly, I can buy it myself with my corporate card? Time to yes has knock on effects on so much: price, churn, etc. It is an important attribute to cover. Calendly's time to yes can be instant.
2) 'implementation time': Software that takes a long time to implement. I call this 'implementation time'. Others in my space call it something else. This is the amount of time it takes between wiring the money and the company actually USING the software. An example might be vertical software for a veterinarian. It could take a full month to configure the software, get all the users onboarded, work through bugs, etc. How long does it take to implement calendly? Well about 4-5 minutes.
3) 'Time to Utility': This is the time between 'the customer can actually use the software' and 'the customer has derived utility'. If we are using calendly as an example, again this near instant. If this is vertical SAAS for managing a veterinary clinic, this time might be a month or two. Sure the software is making the business run 'smoother' but the time to utility might be longer than you think.
4) 'Tear Out Time': some people call this switching cost, but I delineate the two. How long does it take a customer to 'tear it out' and switch? This is NOT the same as switching cost, although some people mistakenly (imo) lump them together. This is measured in TIME. Calendly's tear out time? Near instant for most people.
5) Switching Cost: How much does it cost me to switch? This can be a real number. Let's say I want to switch my CRM from Salesforce to Attio. Not only do I have a probably high 'tear out time', I also have to pay SOMEONE to migrate my sales data from Salesforce to Attio. There's a real dollar cost here, even if Attio claims to have the softawre to do it. What's my switching cost from Calendly to Cal? ...near 0.
6) ACV: Average contract value. Finally we get to price. How much are you charging for your software? If you are Salesforce, this could be 20k. If you're calendly? 8 bucks a month.
7) Churn: self explanatory.
8) Data Moat / Network Effects: Does your business collect data that is difficult to collect via LLM? Do you have data that drives product utility that is difficult to attain? Do you have network effects?

THESE ATTRIBUTES ARE THE LENSE THROUGH WHICH YOU SHOULD THINK ABOUT SOFTWARE.

Now, getting back to the question: 'will AI destroy my software company?'. Buddy, if you have high time to yes, a high implementation time, and a low time to utility, AI is unlikely to cook your software company. AI DOES NOT REPLACE A 3 MONTH SALES CYCLE AND A HIGH IMPLEMENTATION TIME. Customers are NOT just going to churn because some guy is billing half as much. CEOs value time and attention. If your software is mission critical, tear out time is high, AND switching cost is high? No one is churning. If you ask a CTO if they would tear out Gitlab to save 50%, they'd probably laugh in your face.

Now: to the inverse. Let's talk about Calendly. Calendly is the perfect example of a business that I think will be in trouble due to AI. Why? (I'm a calendly customer btw)
1) My time to yes is instant. If someone comes to me with a $2 a month replacement, I can instantly say yes.
2) My implementation time is instant. I cancel calendly. I synch my email to new service. Done.
3) Time to utility is instant.
4) TEAR OUT TIME IS 5 MINUTES. YIKES! This is problematic software.
5) Switching cost: Near 0.
6) No data moat. No network effects (rly).

To all you indie hackers out there: these are the types of businesses you might want to copy. Why? YOU DON'T NEED AN ENTERPRISE SALES TEAM TO SELL YOUR PRODUCT. Like it or not, even if AI can make a copy of software, spoiler alert you have to go out and SELL IT. There's a reason some B2B SAAS companies NEED venture funding: the sales cycles (time to yes, imp time, time to uitlity) ARE LONG LOL. You NEED those venture dollars to sell this software that will (hopefully) have a very low churn rate. Many of these businesses are fine. Their main problem is people above and below them in the vertical chain. Not addressing this here.

What's that mean? DISTRIBUTION MATTERS MORE THAN EVER. If calendly can be copied, learning how to distribute in a low cost way becomes more valuable than ever before in software. SEO. Partnerships. Large scale cold email. Having influencers on board from day 0. These things matter even MORE.

So is software dead? Hell no...but companies like calendly will surely have some problems.

So to you boot strappers out there, here's what you want:
1) Time to yes can be instant.
2) Time to utility should be as fast as possible.
3) Low Implementation time.
4) Keep price low enough that someone can say 'yes' without needing to call you on the phone but high enough that can make some serious dough.
5) Hopefully find a way to get data that's hard for someone else to get. That'll help with competition and switching cost.

YOU WILL HAVE CHURN. But you can also build a 5mm ARR business with 4 employees.

Good luck anon

[Zane Hengsperger @zanehengsperger]

I summarized my favorite manufacturing research piece into a one pager teaser.

Make sure you read the whole thing below.

[Katherine Boyle @KTmBoyle]

American Dynamism.

[Jared Sleeper @JaredSleeper]

Adjusting for inflation, the Bessemer Cloud Index has been roughly flat since September 2019.

Almost six years.

I haven't run the numbers, but I'd guess total revenue for the index has roughly tripled over that time period.

Entry multiples matter.

[Rex Salisbury @rexsalisbury]

Stripe spends 1/3 of their annual letter roasting europe for their lack of growth.

ouch.

[Aaron Levie @levie]

Vertical AI is going to produce some of the biggest opportunities we’ve seen in software in the past decade. The opportunities may seem too narrow at first, but the TAMs will be much larger than we realize when fully exploited.

[Luke Sophinos @lukesophinos]

There are four strategies in vertical SaaS right now where I strongly believe there is a lot of opportunity / money to be made:

1. Buying the businesses within the industry you serve, automating back office functions with AI, greatly improving EBITDA. Over time you should be able to automate a lot of functions beyond back office, but AI not there yet.

2. Buying 2-3 complementary vSaaS companies in the $2M - $7M of ARR range at 3-4x ARR, pulling them together into a single platform, hitting profitability, flipping it to Growth Equity / Private Equity within ~5 years. If embedded payments is still an opportunity it's a huge bonus and you can juice scale ARR.

3. Buying ONE vSaaS company at $3-6M of ARR that's a VC-backed zombie that hasn't implemented payments yet. Probably can buy for 2-3x ARR. Get it fit/profitable, add payments. Grow it to $10M+ of ARR with 3-4 years and sell it to PE or a strategic.

4. Vertical AI that replaces a specific job function in your space and/or can leverage data from the legacy ERP solutions w/o a typical API integration. Think ChatGPT computer access via customer logins.

Some areas I think folks should be really cautious of:

1. PE firms buying later stage vSaaS ERP's ($50M ARR+). AI is eroding a lot of moats these folks have relied on for a very long time.

2. Starting a vSaaS copy cat of an existing product in the vertical. This talk track of "We built it with an AI foundation" I just don't subscribe to. You don't have any of the data. AI is going to supercharge <$15M ARR vSaaS companies that don't move too slowly yet.

3. vSaaS businesses that give the tool kit to someone to start a business in an industry. IE going so up market in the process that you lock in your product with them for life. I think most people are just not entrepreneurs, convincing someone to start a business is too hard and I don't believe will scale.

4. The intellectual lazy vertical AI talk tracks I keep hearing of when the actual AI automates no where near 20% or more of the task the employee actually does. To me, this is just folks taking advantage of the moment.

What do you agree/disagree with here?

[Aaron Levie @levie]

AI Agents will dramatically expand the size of the software market. Here's how that will work. 

Traditionally, software companies are stuck within the constraints of existing IT budgets, with IT expenses running in most companies somewhere between 3-7% of revenue (with tech and banking often a bit higher). This has always introduced a natural ceiling on the amount of spend for most categories of software. But in an era where the software, because of AI, is *solving* the problem for the customer and not just *enabling* a solution to the problem, the ceiling gets blown up.

In an AI-first enterprise, AI Agents will: help marketing teams will spin up campaigns faster in all regions; code and test software for engineering; answer and triage first layer of support tickets; scale outbound campaigns and generate leads; automatically review and work through contracts; and so on. None of these outcomes traditionally came from IT spend.

This then directly leads to software TAM growth. Just take a micro example of legal use-cases as an easy case in point. In the US, the contract management and ediscovery software categories are a few billion dollars each, give or take. However, the size of the legal services market in the US is somewhere around $400B, nearly 100X larger than the related software categories. If AI made the legal services operations even 20% more efficient (which is likely an understatement in the medium run), the software spend in this space could very easily grow by 5-10X.

You can apply this logic to basically any category of work, and the math is similar. Importantly, the spend is not inherently going to be zero sum with what we spent on before. Net new dollars for AI (not replacing labor) will appear in many areas: startups and small businesses will go after problems they couldn't afford before; teams in large companies can scale out an operation far more than they would've otherwise; and teams that maybe had a business requirement but not enough budget or weren't prioritized before, can now solve a problem more quickly. 

Going forward, a company will simply decide how much productivity they want to spin up in the form of AI Agents, and they can just modulate quickly based on the ROI of whatever Agents they're using. Because of this flexibility for scaling out work, the new-use cases it now solves, and the ability to go past the typically limited IT budgets. AI Agents will make software markets much, much larger.

[Garry Tan @garrytan]

Great founders don’t capture TAM

Great founders create TAM

[Eric Vishria @ericvishria]

Remember when 3x, 3x, 2x, 2x, 2x was the gold standard for annual growth in SaaS companies?

That was cute.

[Turner Novak 🍌🧢 @TurnerNovak]

Since TikTok is getting banned, here are my top TikTok’s from the past few years:

This one was a banger. I filmed it so you could use the Duet feature and record a split screen, pitching me.

Went viral on Twitter and actually ended up investing in a company that pitched me 😂

[Harry Stebbings @HarryStebbings]

“Google could just create this in seconds”.

This is the most moronic VC statement.

Most software products any large incumbent could build.

1. They tend to have horizontal markets where specialised features could not be incorporated.

2. It’s not their focus.

3. A company is GTM, CS, CX. So much more than product.

Don’t listen to the ass of a VC who says “meh Google could build it”.

[Beff (e/acc) @beffjezos]

Folks @ycombinator has gone fully based and e/acc-pilled with their recent RFS 🔥🌉

[Beff (e/acc) @beffjezos]

We're so back.

[Beff (e/acc) @beffjezos]

What are the vibes going to be like for the second half of the 2020s?

Something like this, accelerate.

[Beff (e/acc) @beffjezos]

This team about to raise a $2T Seed Round