Should I Work in Big Tech or an AI Startup?

Historically - when top AI and CS researchers opted to leave academia to head into industry it meant going one place - a FAANG company.

However - with so much movement within AI (and reorgs at many of the FAANGs) - we’re seeing many of the top researchers leave the comforts of FAANG companies to start or join top AI startups. 

In this post we’ll walk you through some of the differences between working at a big tech research lab, OpenAI/Anthropic vs. joining a fast-growing AI startup – specifically what it means for your research, pace of work, and compensation.

Where are the best researchers going?

While big tech (primarily FAANG) companies have historically attracted most top researchers out of PhD and post-doc programs, that is quickly changing.

OpenAI and Anthropic are compensating researchers very well and talent continues to flow to both of them - these are the hardest companies to get an offer with. And, as venture capitalists get increasingly excited about productizing AI for consumers and businesses, we are also seeing many of the best research scientists leave to start or join smaller AI companies. 

One of the most interesting changes in the last year is an increase in funding and research for robotics companies. There are four companies we see talented researchers joining: Tesla, Figure, Skild.ai, and Physical Intelligence. 

Tesla and Figure are both building their own hardware and software. In comparison, Skild and Physical Intelligence and focusing on the software layer - building foundational models that can be applied to any robot. 

All are rightfully well capitalized. Building hardware and foundational models are expensive. Skild recently raised a $300M Series A while Physical Intelligence raised $400M at a $2B valuation and Figure raised $675M at a $2.6B valuation.

The reason so much capital is being poured into AI robots is that, if successful, they have the potential to replace a significant amount of human capital. Many people estimate there will quickly be more robots than humans on the planet. 

As these companies ramp up, we continue to see more researchers and engineers joining. It’s a very interesting time to consider joining these companies. Financially - the valuations are still low enough (except Tesla) to allow room for a 50x gain ($2B -> $100B). And - you’ll likely skip out on the bureaucracy you’d find at a FAANG company - but be prepared for growing pains!

Beyond Robots & Hardware

For the past few years we've also seen an increase in researchers founding - and joining - AI startups.

First, there was a wave #1 of AI researchers starting companies. For example:

Mustafa Suleyman, the co-founder of DeepMind left to start Inflection AI alongside famed LinkedIn founder Reid Hoffman. After raising $225M+ at a $1.2B valuation, they released their first product called Pi - an AI companion and assistant. Shortly afterwards, Inflection was acquired by Microsoft and Mustafa now runs a large AI division there. 

Six of the eight original authors of the renowned Attention Is All You Need paper from Google have gone on to start billion-dollar companies and many of the best researchers have followed them: 

• Noam Shazer started Character.AI raised $150M at a $1B valuation and then was acquired by Google for $2.7B
• Aidan Gomez started Cohere (an OpenAI model competitor). Cohere has raised hundreds of millions and is currently valued at $5.5B.
• Jakob Uszkoreit started Inceptive, an mRNA company using AI. 

Not all of the acquisitions have been smashing financial successes. Character.ai, Inflection and Adept.ai all were acquired for large sums of money but, after paying investors, back only amounted to modest payouts for employees (still slightly better than staying in big tech all along).

Recently, there have been a number of AI companies that have been making significant progress and generating substantial revenue. Wave #2 if you will. 

One of our favourites is Midjourney who didn’t raise capital but is rumored to be at $200M ARR. Similarly, Pika.art (which raised $80M) and Together AI (valued at $1.25B after a $100M raise) are seeing significant traction.

Joining this second wave of AI companies can be very compelling but it’s worth making sure there is some moat / defensibility. If the foundational model wars have taught us anything, it’s that without a moat a startup can lose its edge in an instant. 

Life in Big Tech vs. a Startup

So should you follow some of the best researchers and join a hot, quickly-growing AI startup? 

It comes down to three things: 

Type of Work

When it comes to work, we see people choose to join or stay at big-tech research labs specifically because they are very research and publishing focused. If your goal is to publish as many papers as possible, it’s very likely the best place to be is big-tech. 

Historically, big-tech has been willing to invest (via salaries and GPUs) in AI researchers to allow them to push the boundary of what is possible with the technology. However, the industry seems to be moving away from open source and publishing – and with the competition in AI increasing and interest rates rising – we will likely continue to see more closed-source research and more pressure to produce research that is commercially valuable today. 

Work at most of the very hot AI startups includes a mix of research and product. It’s much rarer to be publishing papers but frequently researchers are doing cutting-edge research. 

For example, researchers at Skild.ai are focused on building foundational models that robots can use as their “brain.” It's certainly a hard and exciting problem but given the intense competition they are unlikely to open source everything via papers. Although you will often produce fewer papers, in many ways, the research at startups can be some of the most exciting because it’s immediately applied to a real-world company.

Financial Outlook / Risk 

With numerous layoffs, big tech might not be as stable as it was a few years ago but the fact that their equity is public (i.e. you can sell it to anyone on the stock market) makes compensation at companies like Amazon, Google, NVIDIA, etc. more stable than joining an early-stage startup. (More on big tech research scientist compensation here)

Most of the well-funded AI startups will pay a base salary between $200-450K in the US. The equity you receive will likely either end up being worth zero or very, very valuable.

We recommend expecting no liquidity (i.e. ability to sell your equity) for 5-10 years when joining an early-stage company. We have former clients who joined companies like Cohere in the early days and are now worth 10s of millions of dollars. However, Cohere hasn’t allowed their employees to do a secondary offering (selling your shares to investors) so they haven’t been able to cash in yet. 

It’s very hard to predict whether a company will succeed and - if they do - if they will allow secondary offers, so you need to be financially prepared for your equity to be worth $0 for a very long time (even if the company continues to grow and raise venture capital).

People & Pace 

There are exceptional research teams in both big tech and startups. In the end, you spend the majority of your waking hours working and the people you work with can make a huge impact on your happiness and career satisfaction. You won’t know which culture or which team you gel with the most until you talk to them so we’d recommend considering both big-tech and startups with an open mind.

In terms of pace, startups tend to have a reputation for requiring longer hours and more fast-paced work – however, every company and team will be different. (Curious about what it’s like to work at a company? Here are some questions to ask the hiring manager and people on your team)

We are seeing an increasing number of the researchers we work with make the jump from big-tech to well-funded, incredibly exciting startups. It’s not the right choice for everyone, but it’s a path worth considering given how many benefits there are. 

If you want our take on your specific situation you can apply for a free call here.

Or - if you're interviewing (for startups or FAANG companies) - check out our 60-page guide to technical interviews.

How to Recruit at Startups

While big tech recruiting tends to be fairly standardized (especially if you’re in a CS PhD program), recruiting at hot startups is often quite different. It’s much rarer to have recruiters reach out - normally you need to do the leg work. 

Here’s how to get a job at a startup:

I’ve written a more detailed article about networking into startups including several of the AI companies I think are very compelling – and specifically which team member I’d reach out to on LinkedIn. You can read it here.