š§ 2nd Brain VC ā šļøšØ Superstrat3gy
Dear Friends,
Since 2017, I've been blending lessons from high-performance racing with crypto analytics, on-chain data and protocol design. Itās not been a casual Sunday picnic, but still, Iām super grateful for this journey that has transformed me from a crypto enthusiast into a professional angel investor.
After many great conversations with founders & investor peers, it was clear that this backstory would become the new cornerstone of my strategy to break into Venture Capital.
Long story short: we recently rebranded as Superstrat3gy, and weāre back on Substack today under new colours - namely, Purple Sector.I intend to use Superstrat3gy as a ābuild in publicā journal, of what I hope will soon become a fully-fledged VC firm: thesis-driven, research-centric, powered by data and AI.
So, this is effectively Purple Sectorās 1st issue - a sort of warm-up for whatās to come. And youāre more than welcome to engage in this journey with me!Iām preparing some cool stuff for the next issue, with a list of the most influential attributes of Rockstar founders. Weāll also dive into portfolio construction techniques, LP communication tips, the intersection of AI + VC + cryptoā¦
But please, feel free to share, comment or ask any question you might have: after all, youāre now part of the Race Strategy Team for VC! š
The Outlap
Weāll kick off this fresh series with a deep-dive into advanced predictive techniques. Thatās an approach widely used in F1 strategy planning, that Iāve been exploring lately to reduce the bias of gut feelings & cognitive distortions. The endgame: improving my whole investment process, from deal sourcing to decision making.
Avanced what?!
If youāre familiar with early-stage investing, honestly ask yourself: How structured and repeatable is your process for spotting high-potential startups or macro trends? How does it impact your investment efficiency?
Advanced predictive techniques are mostly based on probabilistic thinking: weāll try to understand how this approach can not only sharpen an investorās decisiveness, but also foster a better risk planning - short and sweet.
Weāre confronting 2 approaches of ādoing Ventureā here: one that relies on intuition & holistic judgements, and one built on numeric probabilities. So, what gives? Letās dive in!
Itās always been like thatā¦
It turns out, not many VCs have explored these techniques before. Because they are simply unaware, or because they might also find such abstract concepts counterintuitive & overly complex. But we'll see below that the real challenge is in learning & transitioning to a new mindset.
Indeed, it happens frequently that Venture Capitalists fall prey to cognitive biases such as overconfidence and confirmation bias, hindering their ability to adopt a self-critical, open-minded approach.
For some investors, the struggle to update beliefs in the face of new evidence or information is often real, leading to poor decision-makingā¦ and ultimately poor performance.
Besides this, effective predictions rely on continuous feedback and iterative learning from past predictions, But due to the long timeframes associated with the development of startups, and the infrequency of clear outcomes, the VC environment often lacks clear feedback loops.
Finally, instead of engaging in collaborative strategies, many VC firms still operate in silos, limiting the integration of diverse perspectives that challenge conventional wisdom.
ā¦but it doesnāt have to!
At Superstrat3gy, we thrive on challenges: thatās why Iāve started exploring how probabilistic thinking could be incorporated in my own strategy & workflows. Spoiler alert: itās easier & quicker than it seems.
As an angel investor, my thesis focuses on the convergence between tokenization, DeFi, DePIN & decentralized AI to form the bedrock of the computable economy: a broad societal and macroeconomic shift, unlocking new incentives, governance models & digital institutions.
But what are the odds of this thesis being concretely realized in 2030, or 2035? Thatās what Iām trying to find out by:
Step 1 : Decomposing the Problem
Breaking down complex problems into smaller, manageable parts helps to tackle each aspect with more precision.
For example, considering the computable economy thesis, rather than trying to form a holistic (but vague) judgment, Iāve decomposed the realization factors into smaller components such as the maturity of the technology, the progress in adoption & integration, the supportiveness of regulatory frameworks, investment flows etc.
Step 2 : Converting Hunches into Numeric Probabilities
Relying on gut feelings without quantifying them leads to inconsistent decision-making. Assigning numeric probabilities to outcomes transforms gut feelings into a combination of qualitative assessments derived from 1st principle questions.
Now that we have broken the success (or failure) factors of the thesis into smaller components, we can start assigning probabilities to each: for example, whatās the probability that blockchain infrastructure will achieve robust scalability & seamless interoperability in the next 5 to 10 years?
Iāve rated to 85% the odds of scaling solutions being widely adopted and integrated into major networks, creating a truly interconnected ecosystem that surpasses the needs of global financial infrastructure. Meanwhile, the blockchain interoperability market is growing rapidly, connecting different blockchain networks to share data and transfer assets effortlessly.
Step 3 : Updating Predictions
This is just a simple example of how creating a distribution of possible outcomes and using tools like decision trees or scenario analysis helps adopting a structured and objective evaluation process - while keeping the door open to alternative outcomes.
By regularly refining predictions as new information becomes available, I want to stay agile and ideally positioned to make informed decisions, while enhancing my accuracy over time.
This means updating my assertions in an iterative manner: for this, Iām using incoming data such as transaction speed, volume, fees, unique active addresses or TVL in interoperability protocols to adjust the above estimates.
Conclusion
Combined together, these steps create a dynamic, adaptive deal-sourcing & evaluation strategy. Weāll find out the impact on the unicorn hit with time, but I feel already the benefits of more efficiently tracking major trends & opportunities.
By breaking down problems, converting hunches to probabilities, and regularly updating predictions, I aim to reduce cognitive biases as much as possible, and distill real signals from the noise: next lap, weāll see how this translates in profiling Rockstar founders! Take care and once again, thanks for following along š
Julien
š Portfolio News
Talking about the Computable Economyā¦ Portfolio company Axone is currently going through Outlier Ventures Crypto x AI Basecamp and is also making great strides towards mainnet. Please have a look at Emmanuelās latest article below, to understand why the Web of Things is coming and how Axone protocol is ready to lead the way š
