The Six Million Dollar VC (Part 1).
Welcome to 🧠 The dMBA Principal Track.
⌚️ Todays’s edition is a 7 minutes & 3 seconds reading.
To crypto Venture Vanguards.
As a kid born and raised in the 80’s, I'm continually captivated by how cult movies and TV shows from that era have attempted to portray our future… well, what has now become our present !
While AI and robots are taking an increasingly prominent role in our lives, I’m grateful that I can experience first-hand some of the most far-fetched dreams of 40 years ago, but I know everybody doesn’t share this opinion.
Let’s take an example : any of you remember the 6 Million Dollar Man ?
Although the series wasn’t explicitly laying down a vision for the future, it did stage cutting-edge technologies that were only just emerging at the time, echoing some of the concerns associated with radical breakthroughs such as their impact on society, international security or government secrecy. Sounds familiar, right ?
As investors, we aren't exempt from this rapid evolution, especially in crypto.
To secure a spot at the cap table of the most coveted deals, VC’s and Angels will need to augment themselves with Steve Austin-like capabilities rather sooner rather than later.
Let's get down to the nuts and bolts of this fascinating trend together : welcome to the dMBA Principal Track ! 🧑💼
The purpose of the dMBA is not limited to documenting my own journey from angel investing towards building a fully-fledged VC firm.
We’re laying the foundations of something much bigger here : the first open-source business education graph, making venture careers accessible to anyone - regardless of wealth, genre, geography or personal background.
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The Six Million Dollar VC (Part 1).
Why it matters ?
The VC landscape is fiercly competitive. As a Principal, one of your primary duties is to systemize the identification of the most promising companies in your fund’s vertical, among the myriad of projects started daily.
By leveraging intelligent strategies, you will be able to secure early access to top opportunities, and allocate more time to build strong relationships with project teams and communities. This is key to establishing trust with founders early in the fundraising process, ensuring you’ve already got an unfair advantage when the deal starts gaining traction among other VC’s.
Building and optimizing your deal flow funnel helps you do just that. The advent of machine learning and large language models is another reason why data-driven approaches to deal flow management are becoming increasingly popular.
What’s involved ?
A data-driven deal flow helps you narrow down your focus to find the diamonds in the rough, filtering out opportunities based on increasingly complex criteria as your research progresses.
Typically, it involves collecting and processing a significant amount of data on new companies or projects from online sources such as social networks, blogs, forums or public & commercial databases. This data can include the team’s identity, but also the employee or contributor headcount, website traffic, patent filings, academic publications, payment data or product reviews : anything that helps you build a comprehensive profile for a potential target.
Challenges
By employing an evidence-based approach, augmented VC’s aim to reduce the influence of biases in investment decisions, as well as diversify their portfolio without excluding any founders based on subjective (and unfair) criteria such as genre, geography or social connections.
Nevertheless, relying solely on data can lead to missing out on extremely disruptive startups, whose business model isn’t easily “dissectable” by algorithms and LLM’s.
Moreover, identifying and collecting data about emerging crypto projects isn’t always as straight forward as it is for traditional startups. Crypto founders often prioritize building a community around their product, rather than creating a company profile on Crunchbase from Day 1.
Where to start ?
As Andre Retterath demonstrates in his research and his excellent newsletter Data-driven VC, a balanced approach that combines data analysis with human intuition and expertise can be the most effective way to build a 6 Million Dollar deal flow for the long haul.
Today’s opening post for the dMBA Principal Track is inspired by Andre’s work and Nick Havryliak’s article on ChatGPT prompts for Crypto research and analytics. Nick is the CEO of Assisterr, a new Web3 analytics platform powered by natural language. Kudos to them for sharing so much valuable knowledge in public !
Remember when ?
Once upon a time, in a galaxy not so far away, readers of this newsletter explored a futuristic, blockchain-powered economy where investing had become indissociable from data analysis. You can still access these older posts here, by the way !
If you were part of the dMBA tribe at the time, you might recall an extensive list of sources like DappRadar, Github, Dune Analytics or DeepDAO that we envisioned could become the top structure of VC deal flow funnels in the future.
This was before ChatGPT
Of course, scouring so much data from different sources to find potential opportunities can become daunting without clear, automated processes in place.
This is particularly true when you attempt to break down social media activity, the lifeblood of war in crypto where community is everything : what are the most discussed emerging projects ? How do people feel about them ? Are they endorsed by thought leaders ?
Enter your new copilot for sentiment analysis ! Even if - like me - you're still waiting for access to ChatGPT Plugins, you can use alternative scraping tools to collect Tweets, Reddit posts or Product Hunt reviews related to specific projects or verticals you’re interested in. Remember to sort out hashtags, keywords and other metadata (author, timestamp, follower count, likes, retweets, etc.) improve the quality of the analysis.
A first approach is to leverage ChatGPT to classify the sentiment of each piece as positive, negative, or neutral. Then, prompt it to calculate the percentage of content in each category, or visualize trends over time - not to mention the automated processing of engagement volume.
Another idea : task your virtual intern with identifying recurring challenges expressed by public opinion, and uncovering potential problems that have not yet been addressed by any startup.
ChatGPT also allows you to extract easily from a large pool of data the crypto projects that have been endorsed by individuals with a significant following and proven expertise in the space such as founders, developers, analysts of fellow investors.
Understanding whether they have simply mentioned the projects, given positive feedback, invested in teams or collaborated with them can be an indication of early traction (even if it's just the tip of the iceberg and needs further validation).
Riding the tailwinds
There are numerous other ways you can leverage ChatGPT to discover crypto projects with early community support.
For example, have you thought about collecting data on Github about developer activity ? Such as the number of commits, the frequency of commits, the number of contributors, and lines of code added or removed in a list of relevant repositories.
If you follow this path, you can look for metrics on the broader community’s level of interest and engagement : the number of stars, forks, or watchers in specific repo’s.
Another one : scrape Gitcoin to retrieve funding information for projects with an active grant round, including the amount of funds raised, number of contributors and the projected allocation of the grant.
You might also leverage CoinMarketCal’s API to identify market-moving events, such as a whitepaper / roadmap release, a version update, a public appearance or any other milestone that could rally the community behind a project.
Finally, data on the governance activity of decentralized projects such as the number of proposals submitted, votes cast and outcomes can be useful in assessing the level of activity and engagement within a DAO. Look for details about the number of active members, their roles and contributions, as well as the distribution of voting power.
At each of these steps, LLM’s are able to perform a more targeted and accurate data analysis than the human brain for a fraction of the time and effort. As opposed to losing focus and become distracted after a few hours of analysis, AI can repeat the process indefinitely until the task it has been assigned in completed.
As investors, surviving in a competitive environment means adapting to this rapidly evolving frontier. But in my view, these tools are best exploited when they allow us to preserve time for expertise and, above all, human relationship building. Stay tuned for Part 2 of this mini series in the dMBA Principal Track, as we’ll showcase concrete prompt and Plugin examples to achieve this mission !
Julien
Who am I ?
🏎 Former F1 engineer.
🏆 15+ years contributing to the success of high performance organizations, on & off-track.
🕵🏻♂️ 5+ years extracting signals from the crypto noise.
📊 On-chain data & crypto analytics specialist.
🧠 Unlocking access to business education with the dMBA.
👼 Open-source angel investing & VC firm building.
🌎 Check my complete profile here !
How can I help ?
If you are a founder or investor on a mission to make the global financial system more accessible, transparent & positively impactful : let’s connect. Besides fundraising, I’d be stoked to support with on-chain data, analytics or any research work specific to crypto (e.g. tokenomics, governance).
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⚠️ Disclaimer : the content of this newsletter is for educational & entertainment purpose only. In no situation should it be considered investment, tax or legal advice. The reader is invited to build his/her own opinion about the views expressed, and take appropriate decisions for his/her specific situation or objectives. Digital assets are highly volatile and the risk of capital loss must never be underestimated : do your own research, and only invest what you can afford to lose.