What’s 🔥 in Enterprise IT/VC #398
The 5 Ps of Inception Investing + outworking your competition from Tom Brady & Roger Federer
This week we were all reminded of what greatness is from the world of sports. Clips from Roger Federer’s speech to Dartmouth’s graduating class and Tom Brady for the Hall of Fame were shared by many an investor and applied to startup life. I’ll add to that list, as I’ve watched this clip from Tom Brady at least 10 times and leave it here for you. To be clear, the fact that I am sharing a Brady clip is huge since I’m a Ravens fan who experienced many playoff losses to the 🐐. That being said, he is the 🐐 and I immensely respect the guy for his accomplishments and how he went about his job.
To be successful at anything. The truth is — you don’t have to be special. You just have to be what most people aren’t. Consistent, determined and willing to work for it. No shortcuts. If you look at all my teammates here tonight, it would be impossible to find better examples of men who embody that work ethic, integrity, purpose, determination, and discipline that it takes to be a champion in life.
Tom BradySo after last week’s Forbes Midas List profile on Inception Investing, a number of readers reached out and asked how we do it at boldstart ventures; how do we know to invest before any code is written or a product exists? Besides looking for the intangibles as Tom outlines above, here’s a framework that I like to use. Let’s call it the 5 Ps.
5 Ps for Inception Investing
People - what gives me the reason to believe that you or your team are the ones to tackle this problem and build something really big. Who are the other potential early hires that you will bring for speed out of gates?
Product + Problem - what problem are you solving and how you are uniquely solving it? What is your unique technical insight from which to build a wedge into the customer and a moat over time. What is the magic 🪄 to make a customer’s life 10x better with your product or solution than without.
Pain - is this a burning pain now? We Need painkillers not vitamins - this avoids investing in the trap super cool technology in search of a problem to solve
Passion + Perseverance - starting a company is really F&*(#ing hard. Do you have the passion to survive the ups and downs, the mission to deliver game changing product to customers, the perseverance to do whatever it takes to win? What I’ve seen over 28 years is founders who start companies born out of a pain that they have experienced to which they want to automate is usually a much better signal than founders who put a whiteboard together and look for white space while having zero prior experience.
Potential/possible - art of the possible. There are 999 reasons or more in terms of why a startup can’t work, but if 1 goes right, how big can this be? What is the potential opportunity? Tell me your dreams which may sounds whacky and far-fetched but IMO when creating new markets and seeing the world differently, whacky is good! As I like to say it’s not the TAM you start with, but the TAM you exit with. This means one can and should many times start narrow with a product that solves a focused problem for an ICP but has the vision to go much bigger if they conquer the first hill.
Ultimately zooming out, I love founders who are incredibly product focused and obsessed who have incredible user empathy and can drill down into the day-to-day minutiae of how their product will be 10x better for the user’s life than what exists now. At same time, the founder must have an incredible ability to zoom out as well and tell a story of what this journey can look like over the next 3 to 5 to 10 years if they are successful. Zoom in and Zoom out!
So that’s it, the 5 Ps. What it doesn’t talk about is how we strive to look for the consistency and determination and ability to grind every single day like Tom Brady mentioned. Yes, we can find some of that through references of failures and recoveries but also even in the diligence process by asking challenging questions and seeing how one responds and also how quickly. Did you know that many of our diligence sessions extend over the weekend where we as investors have to make the time but also founders to continue the dialogues.
Here is tennis great Roger Federer sharing a few wise words as well - Grit is > Gift and Discipline is Talent.
While not about grit and work, this mental framework is one we should all carry as well as investors or founders.
Even a great shot, an overhead backhand smash that ends up on ESPN's top 10 playlist – that too is just a point.
Here's why I'm telling you this.
When you're playing a point, it has to be the most important thing in the world. And it is.
But when it's behind you, it's behind you.
This mindset is crucial – because it frees you to fully commit to the next point with intensity, clarity, and focus."The message is clear for all of us - if we want to be great, we have to bring it every single day. There are no secrets and no shortcuts!
As always, 🙏🏼 for reading and please share with your friends and colleagues.
Scaling Startups
#Reminder that thinking is great but don’t overthink - you have to go out and just do it, experience it, and adapt from there
#initial equity for first 10 hires from Pete Walker Carta
#Vintage Investment Partners, leading fund of funds out of Israel, shares a video clip from VCs on how we are thinking about AI - includes yours truly…
Enterprise Tech
##Will AI replace your engineers? This is a must read from Charity Majors
#The Pragmatic Engineer Gergely Orosz chimes in as well on the importance of entry-level engineers for the LT:
Could not agree more. Charity verbalizes what I’ve struggled to put a finger on.
That writing code is the easiest part of software engineering. Sure, AI coding tools make this easier.
But any company not investing entry-level engineers because of this cannibalize themselves.
Consider that hiring a new grad or junior engineer makes the senior engineers better: by forcing them to explain seemingly trivial stuff (that often turns out to be not as trivial!), look at things from a different POV and grow as a mentor/leader. Plus consider the business more!
🧵 here#one more take from Deedy, a VC at Menlo Ventures
#AI Agent infrastructure market map from Madrona evolving…
#🤯 the King of AI, OpenAI, ARR more than doubled in last 6 months to $3.4 Billion
OpenAI has more than doubled its annualized revenue to $3.4 billion in the past six months or so, OpenAI CEO Sam Altman has told staff, a sign that growth in the ChatGPT developer’s business is accelerating despite intensifying competition.
Annualized revenue—a measure of the past month’s revenue multiplied by 12—was $1.6 billion in late 2023, The Information previously reported, and about $1 billion last summer. That rapid growth reflects how quickly businesses and individuals have incorporated OpenAI’s conversational AI and chatbot in their work.
The Information#More LLM 💰💰💰 - Mistral, the European OpenAI competitor raises $600M at $6B valuation, a year after it launched 🤯 (FT) - one other tidbit, the company only has 60 people with 45 in France
Mistral AI, the Paris-based artificial intelligence start-up, has raised €600mn in new funding at a valuation of almost €6bn, just a year after the Microsoft- and Nvidia-backed company was launched as an unlikely challenger to OpenAI.
The investment, which has tripled the company’s price tag since December, is led by General Catalyst, alongside several of Mistral’s existing investors, including Lightspeed, Andreessen Horowitz, Bpifrance and BNP Paribas. Corporate backers include Nvidia, Salesforce, Samsung and IBM.
“We were told when we started . . . that this is a market that is never going to be disrupted,” Arthur Mensch, Mistral’s chief executive, told the Financial Times. “We showed that this wasn’t the case and we effectively disrupted the OpenAI business model.”#Databricks State of AI Report is out! Here are a few key takeaways:
11x more AI models were put into production this year
After years of being stuck experimenting with Al, companies are now deploying substantially more models into the real world than a year ago.
On average, organizations became over 3 times more efficient at putting models into production. Natural language processing is the most-used and fastest-growing machine learning application.
70% of companies leveraging GenAI use tools and vector databases to augment base models
In less than one year of integration,LangChain became one of the most widely used data and AI products. Companies are hyperfocused on customizing LLMs with their private data using retrieval augmented generation (RAG). RAG requires vector databases, which grew 377% YoY.(Usage inclusive of both open source and closed LLMs.)
76% of companies using LLMs choose open source, often alongside proprietary models
Many companies select smaller open source models when considering trade-offs between cost, performance and latency. Only 4 weeks after launch, Meta Llama 3 accounts for 39% of all open source model usage. Highly regulated industries are the surprise GenAI early adopters. Financial Services, the leader in GPU usage, is moving the fastest, with 88% growth over 6 months.#Apple finally announces Apple Intelligence and IMO the groundbreaking element is around privacy and security (The Verge). While OpenAI will be the default LLM, Apple and OpenAI are not paying each other at all and Apple also said it would bring other model providers to its platform. Hmmm, so where does the value accrue here?
At WWDC on Monday, Apple revealed Apple Intelligence, a suite of features bringing generative AI tools like rewriting an email draft, summarizing notifications, and creating custom emoji to the iPhone, iPad, and Mac. Apple spent a significant portion of its keynote explaining how useful the tools will be — and an almost equal portion of time assuring customers how private the new AI system keeps your data.
That privacy is possible thanks to a twofold approach to generative AI that Apple started to explain in its keynote and offered more detail on in papers and presentations afterward. They show that Apple Intelligence is built with an on-device philosophy that can do the common AI tasks users want fast, like transcribing calls and organizing their schedules. However, Apple Intelligence can also reach out to cloud servers for more complex AI requests that include sending personal context data — and making sure that both deliver good results while keeping your data private is where Apple focused its efforts.#If you’re interested in a deeper dive into the world of cryptography, check out this 🧵 from Matthew Green, Professor of Cryptography at Johns Hopkins
#great post from Jamin Ball Clouded Judgement - Is Seat based pricing dead? esp. in a world of AI
In summary, for a lot of infra / dev tools, seat-based models:
1. Don’t align value delivered with price charged (ie they undercharge)
2. Blow up the margin structure of the vendor as product usage creates incremental marginal costs without generating incremental revenue
With historical app-based software the usage of applications closely mirrored the number of people (seats). There’s only so much work 1 person can do, and as you hire more and onboard more seats the usage grows linearly. That linear growth can then turn exponential if you push more product SKUs to the end users. But with AI, that will almost certainly change. The promise of AI is that it will allow you to do “more with less.” The less here is people.#Also cool to see Superhuman, a portfolio co, with an appearance at the Apple WWDC
Markets
#This went viral from Kyle - kind of insane how quickly the future of mobility is now worth less than a tiny inception round
#What is Databricks worth? Breakdown from Tomasz Tunguz
Databricks revealed some sensational growth this week, as they did last year. Exiting this quarter to $2.4 billion annual run rate, the company’s revenue growth is accelerated year-over-year by 10 percentage points.
Net dollar retention is a major driver of growth at 140%, which is top decile. The table above shows the other data points that we’ve collected through their press releases in the last two years.
The fastest growing product category mentioned is the data warehouse revenue : 100% growth, now at $400m annually, nearing 20% of revenue.
With these modest data points, we can build a basic linear regression model for what the company is worth. This model is rough with an R^2 of approximately 0.45.
Assuming Databricks continues to grow at roughly 50%, that implies the company in the public markets would be worth approximately $54b. However, the company will likely trade at a premium to this. It is one of the few companies that provides significant exposure to AI software in the public markets.
Compared to Snowflake, Databricks is growing nearly twice as quickly. Without additional data, it’s difficult to comment on the relative sales efficiency or profitability.
Databricks’ growth rate is yet another example of the rapid demand for AI infrastructure & the systems needed to power it.
Read more here:
Beautifully compiled Ed!