Is this it? Are we back? Everyone in the startup and venture world has been waiting for months for the re-opening of the IPO window. After a record breaking 2021 (1035 IPOs, beating the previous record of 480 in 2020), 2022 saw a dramatic decline (181 IPOs) and 2023 so far has not been much better.
Common wisdom in the market over the last few months has been that Q4 2023 would be the time the IPO window would cautiously re-open for technology companies (recent non-tech IPOs like restaurant chain Cava being considered non-representative). And it would be crucial that some of the very best companies (the usual suspects being Stripe, Databricks and Instacart) would go out first, to pave the way for a bigger wave of quality companies right behind them.
Well, this week has been an exciting one – on Monday, ARM filed its F-1 (here) and just today (Friday August 25), both Instacart (here) and Klaviyo (here) filed their S-1s. It’s going to be exciting to see what happens this Fall in IPO land.
New IPO filings also mean fresh opportunities for the time-honored VC tradition of S-1 breakdowns, even though timing is unfortunate given summer vacation schedule – here and here.
Consistent with my general investing focus on data and ML/AI, I’m going to pick Klaviyo for this first breakdown of 2023, as it’s a heavily data-driven business. As I did in the past (see the S-1 quick teardowns for Snowflake, Palantir, Confluent, C3, nCino), this is meant as a QUICK breakdown – mostly unedited notes and off-the-cuff thoughts, in bullet point format.
An overnight success 10 years in the making, Dataiku, the leading enterprise AI platform targeting Global 2000 companies, was named “Partner of the Year” for data science, machine learning and AI by BOTH Snowflake and Databricks at their respective annual summit a few days ago.
The company, in which we’ve been proud investors since leading the Series A in 2016, has scaled impressively over the years, reaching $200M in ARR at the end of 2022, with a team of over 1,200 people.
It was a pleasure welcoming back CEO Florian Douetteau, for a conversation where we covered:
* Dataiku’s centralized approach to enterprise AI
* Emerging use cases for Generative AI in the enterprise
* Some leadership lessons learned along the way
Here are the links to the podcast (subscribe! give us 5 stars! etc), and the YouTube video:
Synthesia, the market leader in AI video generation, is announcing this morning an exciting $90M Series C financing.
This is a great milestone for the company, making it a newly-minted unicorn and adding strong new partners (Accel, Nvidia, and some great individuals) to the team. Beyond the fundraising momentum and accolades, however, Synthesia has first and foremost been building a really impressive business. Over 35% of the Fortune 100 now use Synthesia’s enterprise offering, and over 50,000 businesses use its self-serve product.
Much to their credit, the company’s founders (Victor, Steffen, Lourdes and Matthias) were very early to the Generative AI wave, starting the company at a time (2017) when there was significant technology risk and limitations, to the point that what they were doing was probably a bit weird, if not outright crazy. Several years later, by the time FirstMark led the Series A early 2021, the term “generative AI” was still not a thing – my blog post announcing the round used the term “video as code” and in my 2021 MAD landscape, Synthesia appeared in a box we called “synthetic media”, for lack of a better term.
Anecdotally, this Series C is another example of the growth market showing some sign of life lately, at least for A+ companies, in the same vein as Pigment’s recent announcement, albeit perhaps less surprisingly given the hype around Generative AI.
Given the explosion of Generative AI and the flood of brand new startups that were created in the space over the last 6 months, there are interesting early lessons to learn from Synthesia’s journey so far:
Pigment just closed an exciting $88M Series C (TechCrunch coverage here). The company is rapidly emerging as the global leader in the next generation of business planning platforms (aka Anaplan replacement), a testament to the power of a world-class team delivering great execution consistently over years.
While we at FirstMark are certainly excited to have been a part of the journey as investors since the seed round, this round is also noteworthy in terms of what it may (or may not) signal about the broader market. A few thoughts:
Growth market: The last 18 months have been incredibly slow in the growth market. Yet this growth round at Pigment was completely preemptive, and highly competitive, involving a number of top firms. What does it mean for the growth market? Maybe it’s an example of continued flight to quality during a downmarket phase – investors seeking A+ companies. But maybe it’s also a sign of the growth market coming back to life, progressively. I certainly get an impression, talking to my growth friends, that everyone is antsy to do more deals after largely sitting on their hands for the last 18 months, and the market has recalibrated somewhat to more reasonable levels. The obvious caveat is that one shouldn’t derive pattern from a small n, but anectodally, another portfolio company of mine will also announce a growth round soon.
Note: those are quick thoughts on some of last week’s most interesting news in AI. I may, or may not (!) do this on a regular basis.
This last week in AI: Adobe killed all the Generative AI design startups with Firefly, and then Microsoft killed all the other Generative AI startups with its plugins and Fabric releases.
I’m sort of kidding, but sort of not. Kidding because, again and again, founders and startups find a way. But sort of not, because the speed of deployment of AI by the Big Tech incumbents is truly something to behold. Companies like Adobe and Microsoft have, of course, massive distribution advantages. It is undeniably problematic for startups to see Adobe deploying Firefly in Photoshop and Microsoft deploying AI copilots across, well just about every single of its products for consumers, businesses and developers (see the dizzying list of announcements at Microsoft’s Build conference this week).
I don’t think, however, that the world wants a Microsoft and Google dominated AI world. The best version of the future for the Generative AI landscape is to be “polyglot” with a variety of tools and companies. Open source is going to be play a huge role and it’s comforting to see so much energy there. And I have faith startups will build the best specialized tools and vertical solutions. It’s going to be a fun ride ahead.
Every year, as part of our MAD project, we do a presentation at Data Driven NYC about the top trends we see across data and ML/AI. (here’s the 2022 version for reference).
The presentation, done this year with my FirstMark colleague Kevin Zhang, is a whirlwind tour of top trends, as opposed to anything particularly in-depth, as we tried to keep it short. But hopefully it should provide a good overview of what’s been happening in those spaces, for anyone interested in a recap.
Software Daily (aka Software Engineering Daily) has been on my podcast rotation for a while, so it was fun to get a chance to be a part of it – thanks to Jocelyn Houle who moonlights as podcast host on top of her day job at Securiti. While this was done in connection with the publication of the MAD 2023, we ended up talking a lot of about venture capital and entrepreneurship in general, including some personal stories.
The video is below, and here’s the audio-only podcast: Apple, Spotify.
One of the cool parts of publishing the MAD landscape every year is the conversations that come with it. Here’s a fun chat I did recently with Joe Reis and Matthew Housley, co-founders of data consulting company Ternary Data and co-authors of the O’Reilly book, Fundamentals of Data Engineering (see their recent talk at Data Driven NYC). We covered a lot of things, check it out!
It has been less than 18 months since we published our last MAD landscape, and it has been full of drama.
When we left, the data world was booming in the wake of the gigantic Snowflake IPO, with a whole ecosystem of startups organizing around it.
Since then, of course, public markets crashed, a recessionary economy appeared and VC funding dried up. A whole generation of data/AI startups has had to adapt to a new reality.
Meanwhile, the last few months saw the unmistakable, exponential acceleration of Generative AI, with arguably the formation of a new mini-bubble. Beyond technological progress, it feels that AI has gone mainstream, with a broad group of non-technical people around the world now getting to experience its power firsthand.
The rise of data, ML and AI is one of the most fundamental trends in our generation. Its importance goes well beyond the purely technical, with a deep impact on society, politics, geopolitics and ethics.
“It’s been crazy out there. Venture capital has been deployed at unprecedented pace, surging 157% year-on-year globally […]. Ever higher valuations led to the creation of 136 newly-minted unicorns […] and the IPO window has been wide open, with public financings up +687%”
Well, that was…last year. Or more precisely, 15 months ago, in the MAD 2021 post, written pretty much at the top of the market, in September 2021.
Since then, of course, the long-anticipated market downturn did occur, driven by geopolitical shocks and rising inflation. Central banks started increasing interest rates, which sucked the air out of an entire world of over-inflated assets, from speculative crypto to tech stocks. Public markets tanked, the IPO window shut down, and bit by bit, the malaise trickled down to private markets – first at the growth stage, then progressively to the venture and seed markets.
We’ll talk about this new 2023 reality in the following order:
In the hyper-frothy environment of 2019-2021, the world of data infrastructure (nee Big Data) was one of the hottest areas for both founders and VCs.
It was dizzying and fun at the same time, and perhaps a little weird to see so much market enthusiasm for products and companies that are ultimately very technical in nature.
Regardless, as the market has cooled down, that moment is over. While good companies will continue to be created in any market cycle, and “hot” market segments will continue to pop up, the bar has certainly escalated dramatically in terms of differentiation and quality for any new data infrastructure startup to get real interest from potential customers and investors.
Here is our take on some of the key trends in the data infra market in 2023.
Everybody is talking breathlessly about AI all of a sudden. OpenAI gets a $10B investment. Google is in Code Red. Sergey is coding again. Bill Gates says what’s been happening in AI in the last 12 months is “every bit as important as the PC or the internet” (here). Brand new startups are popping up (20 Generative AI companies just in the Winter ’23 YC batch). VCs are back to chasing pre-revenue startups at billions of valuation.
So what does it all mean? Is this one of those breakthrough moments that only happen every few decades? Or just the logical continuation of work that has been happening for many years? Are we in the early days of a true exponential acceleration? Or in the early days of a hype cycle and mini financing bubble, as many in tech are desperate for the next big platform shift, after social and mobile, and the crypto headfake?
1) Update public profiles: remove any reference to ever having ever liked crypto/web3, deny any rumors that I was claiming to be “down the rabbit hole” less than a year ago, say that I would have “definitely done deep due diligence” on FTX
2) Show thought leadership: tweet incessantly about Generative AI, change my PFP to a Lensa avatar, talk about how GPT-4 is “insanely mind-blowing” (reminder: cold email OpenAI to actually get early access to GPT-4)
3) Add value: advise CEOs to (a) grow at least as fast as before, but also (b) drastically cut all costs. Use terms like “EBITDA” and “FCF” which I just learned about in 2022. It’s all about “responsible growth” now (hint that I always advocated for it, deep down)