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Should you work in Venture Capital?

Should you work in Venture Capital?

Breaking into VC & a Deep Dive on Self-Driving Labs

Giammarco Pacifico
August 09, 2023

Did you ever think of working in venture capital?

Since you are reading this, chances are you have considered VC as a career in the past…or maybe you are trying to break in right now.

If that’s you, then you’re in the right place!

Before going any further, find some time to learn how VC firms work and the process of closing venture capital deals.

Don’t worry it doesn’t take years to learn the basics - and you can get a headstart in less than 30 minutes by reading this.

(Side note: If you want to go deeper on more nuanced topics like VC vs CVC, financial modelling, legal structures, carry points and more, just hit reply and let me know)

Now that you know the rules of the game, let’s talk about how to enter the field and start playing.

If you are early in your career, you’re looking at analyst/associate positions.

The analyst role/title is often misunderstood as someone going through endless spreadsheets and business models to find the perfect deal.

That might be what investment bankers analysts do (I’m not an expert here) but it has definitely nothing to do with venture capital.

Every VC firm has the same ultimate goal: get into the best deals.

Their playbook to be a successful VC has 4 parts:

Sourcing deals
Evaluating opportunities
Winning deals
Supporting companies

(Side note: I’m intentionally excluding fundraising for new funds which is a continuous process and usually reserved for senior professionals)

Great VC firms are good at all of these while excelling in 1 or 2 categories.

Junior VCs are usually responsible for sourcing and evaluating lots of deals.

Sourcing the best deals means 2 things:

The best deals come to you because of your network, your authority in the space or your audience

and/or

You find the best deals by being in the right place at the right time

How hard can that be?!

As one of the most accessible people within the firm, your deal flow is going to be bad 99% of the time.

You’ll be pitched all the time and saying no to a husting founder sucks - especially when that’s your default answer almost all the time.

Building inbound deal flow takes years (if not decades) of hard work where you consistently show up for your peers, founders and your broader community.

Outbound deal flow feels like sales (aka hand combat) where you might end up scraping the web for weeks, sending 1000s of cold emails, cold calling and hustling your way to hidden gems.

Wanna know the icing on the cake?

It takes decades to be proven right.

If you’re starting as an analyst, it can take 6-8 years before you get a chance to lead your own deals and, even when you finally invest in your dream company, it will take another decade for that company to succeed (if ever).

At least you’ll make a lot of money, right?

Wrong.

Across all private equity and investment asset classes, VCs make the least amount of money.

Even large VC funds that can rely on huge annual management fees pave in comparison to hedge funds or buyout funds.

If you’re optimizing for wealth creation, VC is not the place to be.

Let me show you:

An associate in a European VC firm (3 years of experience) has an average salary of €51k/year and only 10% of them are eligible for carry
A Principal/VP in a European VC firm (15 years of experience) makes on average €160k/year and 90% of them are eligible for carry
An investment banking analyst in Europe (1-3 years of experience) makes on average €120k/year

What if Andy started a VC firm with David Wallace?!

See what I mean?!

Competition is fierce

For decades analysts and associate roles were sought after by MBA graduates and insiders (elitarianism for the win ✌️)

Nowadays, especially in deep tech and healthcare, it’s common to see PhDs and engineers taking up these roles because they have a clear advantage in both sourcing and evaluating deals.

This means your competition is … practically everyone!

Did I already talk about sales?

Most of your time will be spent selling.

You’ll meet with founders and you’ll sell them why your firm is the best
You’ll sell your deals internally to other partners
You’ll sell your insights and access to everyone, including other fellow VCs hoping to get good deals when the time comes

I hope that by now, I’ve probably convinced you that being a VC is tough.

If you are still reading, let’s see why it might still be the right decision for you:

Meeting people (even through cold outreach) is easier than in most other industries: everybody finds time to speak with an investor
Working with some of the smartest people in the world: ambitious founders who are trying to change the world
You get to explore multiple fields while getting a high-level view of how very different industries work (even in specialized funds, you’ll have to learn many topics at once)
You are invited to a lot of free events (this is your dream job if you get energy while being surrounded by people all the time)

Still interested in getting in?

Here is how I’d go about it:

Network by giving first

Try to build relationships with partners.

Instead of asking for a “coffee chat”, offer them something valuable for free.

Some examples:

Introduce them to a startup that you know (or if you are a student make the intro to a relevant professor on campus)
Organize an event and invite them to speak (some of them will say no and that’s ok)
Send them a memo on a startup that you think they should check out (even if you don’t personally know anyone at the startup)
Do some research on a space that fits their investment focus (bonus points for non-obvious areas), write a summary with references and send it to them

Build expertise in a field

Becoming an expert in your chosen field does not only mean knowing about the technology or market, but it’s also (and arguably most importantly) about knowing the founders and players in the ecosystem.

In-person events and online communities are the best places to start.

Show your work and personality

The simplest way to stand out is to be yourself in public… because there is nobody else like you.

When you share what you’re all about, people that resonate with your content start to gravitate in your orbit.

Whether that is a blog, a newsletter, social media or any other form of content, sharing your ideas will also help you clarify your thinking, which is a must-have for every VC role.

In summary, the key to VC is to be thoughtful and to be different!

Good luck getting in!

Self-driving laboratories & the future of research

What are self-driving laboratories (SDLs)?

They are AI-assisted robotic platforms that combine fully automated experiments with algorithms capable of deciding the next set of steps to achieve a predefined outcome.

This includes aggregating input data to design the initial round of experiments, collecting data during such experiments and reviewing results against the hypothesis to design the following experiments. And the cycle starts over.

A subtle but fundamental difference compared to programmable robots is that SDLs don’t just define the experimental conditions to be tested but also which underlying hypothesis should be proven and which experiments are best suited to achieve that.

Why is this important? And why now?

Some well-known challenges in scientific research are:

The physical discontinuity between the different stages of R&D (from synthesis to characterization and validation, experiments have to be run on sophisticated machines in different locations, under different conditions and rules)
The time gap between running an experiment, evaluating results and deciding the new conditions for the next experiments (it can take months to gather all the required data and even more to get the right people together to decide on the next steps, especially when large budgets are at play)
Collection, aggregation and analysis of large quantities of data (which is highly dependent on the scientist or lab running the experiments)

Recent AI advancements, especially in multimodal language models, made SDLs a lot more popular but this approach stands on the shoulders of giants:

Smart automation
Reaction miniaturization (via flow chemistry)
Droplet microfluidic system (for analytical characterization)

What are the advantages?

Automation of repetitive tasks (the ones every scientist dread)
Handling of high-dimension big data
Efficient space exploitation with the physical connection among steps
A sequence of continuous experiments with negligible decision gap
Programmable through natural language

The fear that SDLs are going to replace scientists is misplaced.

This approach will instead free scientists from the most mundane tasks, allowing them to focus on new conceptual tasks, leveraging the AI algorithm as a sparring partner in experiment design and execution.

Credit: Keith A Brown @KABrownLab

SDLs are already here (it’s not science fiction)

Limitations

The main limitation at the moment is cost.

From the cost of liquid/solid dispensing units to the mobile robots, almost all readily commercially available robotic manipulators are not suited for SDLs.

It also requires a huge startup cost and modern labs cannot easily be reconfigured to fully-automated SDLs.

On the software side, there is no standardized suite of AI algorithms that can perform end-to-end experimental workflows.

This opens up an opportunity for companies to develop vertical libraries that are going to run the next generation of labs, offering a springboard to hundreds of thousands of researchers while allowing final users to fine-tune them as needed.

The best shot at creating and commercializing these libraries is to focus on specific industries and applications where deep data pools and know-how facilitate reinforcement learning and market adoption.

It’s too early to say whether these solutions will be proprietary or open-source (or maybe both) but the standardized data produced by SDLs is going to become a stepping stone in a new era of research reproducibility (and maybe new collaborative incentives in academia)

Where is the $ coming from?

It seems SDLs are still too early for Venture Capitalists (but maybe not all of them!) and it is still in the hands of academic labs before practical commercialization efforts emerge.

What’s promising (and somewhat unique) is that governments are already investing heavily in this future - just a few months ago the University of Toronto received a $200M grant to accelerate the future of “self-driving labs research”.

This week's top scientific reads

Latest funding rounds in health & bio

Ready to turn this news into your next career opportunity? Here is how

Likeminded raised €5.9M to accelerate their digital platform for mental health support in the workplace 🇩🇪
Bearmind closed a CHF 2M seed round for its helmet sensors and software for brain injury prevention and cognitive sport performance 🇨🇭
Entia raised $17M for their at-home blood testing platform for cancer patients 🇬🇧
Epiterna raised $10M to develop their high-throughput screening platform to develop anti-ageing therapies 🇨🇭
KetoSwiss raised $8M for its dietary-preventive therapy of migraine and related diseases characterized by metabolic dysfunction 🇨🇭

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