What's 🔥 in Enterprise IT/VC #371
Back to First Principles Building Startups from Inception, hands on keyboard ⌨️ - get the Google Doc now
This encapsulates everything that was wrong about how businesses were built during the ZIRP era 👇🏼
No more “work around the work” - get hands on keyboard!
But many startups are still in for a rude awakening come 2024 - along those lines, here’s a post from Zach Coelius highlighting what’s in store.
But…I have good news for you. Anyone starting a company today has the advantage. Today’s founders have the opportunity to go back to basics, back to reality and to build companies the way they always used to be built - as lean, mean fighting machines!
Even better, I’ve compiled my notes/thoughts on what building lean actually means. By no means is this comprehensive but it does resemble some of what I’ve seen work in my 27 years partnering with enterprise founders from Inception.
Get it now, share it, send me comments:
“Back to first Principles Building Startups from Inception” in a Google Docs here - it’s a live doc and continual work in progress.
I’ve also added a 🧵 here which has some comments from founders.
Here’s an excerpt and please go to Google Docs for the live document which will be updated throughout the year - excerpt below:
Why are investors and founders commenting on how to build startups? It’s because many feel duped! The venture capital machine has been fueled with larger and larger funds and founders and startups were many times forced to supersize 🍟 their rounds and take much more capital than they ever needed or knew what to do with. And as we all have seen this 🎥 many times before, too much money kills creativity, kills startups, and causes founders and investors to make decisions based on what over-inflated valuation the business is marked at versus building what the customer needs in the most capital efficient way possible! This 🎥 rarely ends well.
So I’ve accumulated some random thoughts and notes on going back to basic and first principles thinking around building startups from Inception (engaging with founders well before incorporating - more here) - these are not hard and fast rules as there will always be exceptions but hopefully some of these thoughts can help you as you get started so feel free to mix and match and even create your own!
Back to basics…
The Why Matters!
Why are you starting this company and committing your next 5-10 years to solving this problem?
Believe it or not, some founders can’t answer this question well - of course, I do prefer in the enterprise space those founders who have experienced this pain in prior roles and as technical founders, their first instinct is how to automate the solution
Don’t start a company because you think VCs will invest (AI) but because you are on a mission to solve this nagging problem, you believe you can build a superior solution to solve a massive customer pain, repeatedly, and get them to pay you for it.
Ha, lots of nuances here and sounds basic! Solve a pain you care about and know and do it way better than either manual processes or existing cos but here’s the rub - you can’t give away too much for free or believe you have a real business if folks are only using you because you are free. You have to have something so good that you can have customers willingly pay you. If not, then there is not an urgent enough pain.
Speed out of Gates
Move fast. Speed kills!
Small is beautiful: lean teams move much faster than large teams.
Hands on keyboard for all functions, ideally can build a team but still reverts to #1.
First engineering hires, senior enough but not too senior, hungry builders needed
Location matters for speed
During COVID we shifted to a default remote world and the idea you could hire the best available talent anywhere made a lot of sense. That being said, what I’m seeing now is more experienced founders are starting their companies with “Default first 10 in one location” mentality. Let’s face it, teams who are together iterate way faster than not. This does not mean we will go back to a world of 5 days a week in office but I am seeing 2-3 days of hard core togetherness to ship faster and iterate.
Another couple pages of thoughts here in this Google Doc
As always, 🙏🏼 for reading and please share with your friends and colleagues.
👇🏼💯 from Robert Greene
Creativity is a combination of discipline and childlike spirit.
this is so good - only thing I’d do is replace talent with grit
My rant on Inception Investing continues - how our boldstart partnership with Security Scorecard started - this year it crossed the $100M ARR threshold!
ICYMI, all of the talks from re:Invent 2023 in one Google Doc with links to youtube
From IBM, the company that brought you Watson’s Law “AI in everything” from 2018, here’s the AI enterprise roadmap which is actually quite interesting…this is what they are telling their customers and hearing from customers in terms of needs - we are just scratching the surface for AI in the enterprise and the next few years are going to be 📈
This will become a bigger and bigger deal (Amjit Masad - Replit founder) as more models are exposed to mass market - repeat after me, “There is no enterprise AI at scale without AI Security”
If prompt injection is fundamentally insolvable, as I suspect it is, then there is a sizeable security company waiting to be built just around mitigating this issue.
some great interviews on enterprise GTM from Sapphire Ventures Summit - Navigating a New Normal: GTM Learnings From Our Inaugural Sapphire Ascend Summit
This is going to be huge - ML/AI on the edge, starting with Apple laptops, wait till phones can run models
Crypto finally delivering the one use case that matters 👇🏼
Calling all security founders - need advisors, board members? Lacework has a book of 200 CISOs who are interesting in joining you (get it here)
How does a LLM work? This is worth the 5 minutes (Link to visualization here)
On OpenAI overhyped and the future from CNBC interview with COO Brad Lightcap
In your eyes, what’s the most overhyped and underhyped aspect – specifically – of AI today?
I think the overhyped aspect is that it, in one fell swoop, can deliver substantive business change. We talk to a lot of companies that come in and they want to kind of hang on us the thing that they’ve wanted to do for a long time – “We want to get revenue growth back to 15% year over year,” or “We want to cut X million dollars of cost out of this cost line.” And there’s almost never a silver bullet answer there – there’s never one thing you can do with AI that solves that problem in full. And I think that’s just a testament to the world being really big and messy, and that these systems are still evolving, they’re still really in their infancy.
The thing that we do see, and I think where they are underhyped, is the level of individual empowerment and enablement that these systems create for their end users. That story is not told, and the things that we hear from our users or customers are about people who now have superpowers because of what the tools allow them to do, that those people couldn’t previously do.
Let’s talk about the business of generative AI. Critics say there are consumer apps galore, but is there a risk of saturation? What does the technology really mean for business?
We’re in this really early period, and I think it’s really important that we maintain the ability for the world to sustain a very high rate of experimentation and a very high rate of trial and error. If you look at historical trends of past phase shifts in technology, there’s always this really important experimentation phase. It’s very hard to get the technology right from day zero. We get there eventually – the end state of the technology, we eventually converge to that point – but it’s only after really trying a lot of things and seeing what works and then seeing what doesn’t, and for people to build on top of the things that work, to create the next best things.
My spicy take on this is I think the most important things that get built on top of this technology are actually things that haven’t been created yet. Because it takes some cycles of building with the tools to really understand what they’re capable of, and then how to combine the tools with other aspects of technology to create something that’s really greater than the sum of its parts. And so that’s to be expected, I think it’s very healthy.
AI is coming for white collar jobs - here’s consulting + legal examples (Bloomberg)
Consulting giants and law firms are looking to artificial intelligence to speed up the time it takes junior staffers to make it to the prestigious partner level as the technology eliminates vast swaths of the repetitive, time-consuming tasks that typically filled up their first few years on the job.
At KPMG, for instance, freshly-minted graduates are now doing tax work that was previously reserved for staff with at least three years of experience. Over at PwC, junior staffers are spending more time pitching clients rather than the hours they used to spend prepping meeting documents. And at Macfarlanes LLP, junior lawyers are interpreting complex contracts that their more—experienced peers used to have to handle.
Wide moat businesses according to Michael Maboussin - famed research analyst, author, and currently Head of Consilient Research