AI – Matt Turck

Quick S-1 Teardown: CoreWeave

In the tradition of previous quick S-1 teardowns (Snowflake, Palantir, Confluent, Klaviyo, Cerebras, etc), some quick notes on the CoreWeave S-1 from my colleague Aman Kabeer and I. As in prior efforts, this is not meant to be 100% comprehensive (and it’s certainly not investment advice!).

The CoreWeave IPO is going to be fascinating to watch: partly because it is undeniably exciting, and partly because it is not going your standard tech IPO. It presents a profile that in some ways is typical of a hyper-growth tech unicorn (explosive growth, large losses, dual-class stock structure with Class A/B shares, etc.), but in other ways, it is very unlike most tech IPOs of the past. Its specialized business model, heavy infrastructure focus, heavy customer concentration (Microsoft), reliance on big partners (NVIDIA), financial structure ($7.9B in debt) and unusual risk factors make it a unique case that blends the characteristics of a cloud provider, a hardware company, and a startup riding an exciting but also sudden and unproven market wave.

SOME KEY TAKEAWAYS

Frivolity: Crypto pivot, NJ in da house: For all the jokes on social media about founders, startups and VCs pivoting from Web3 to AI, CoreWeave is an example of a business that started as a crypto mining operation, stockpiled GPUs, and pivoted to AI with spectacular success.

In the same (frivolous) vein, for all the “all AI is in SF” mantra, CoreWeave is headquartered in… New Jersey.

The First Generative AI IPO: Depending on pricing, there’s likely going to be tremendous interest in the IPO. This is in part because of the well-documented dearth of tech IPOs, but most importantly because it’s the first IPO of the Generative AI era (Cerebras, as far as we know, is still stuck in CFIUS review of their relationship with G42).

There are very few “AI pure plays” on public markets – Palantir is one example (cue in the never ending discussion as to whether they are truly an AI company), as is (even more arguably) C3 AI. Other than that, the way to “play” Generative AI has been to invest in Mag 7 companies.

It is no accident that the first companies to file S-1s in the Generative AI era are infrastructure companies. The market has been forming supply-first (chips, data centers, foundation models), with the major hope that the demand side surfaces equally meaningfully in years to come.

Not a Real Estate Play: A negative take on CoreWeave and comparable companies one would often hear in tech circles is that the company is a “real estate play”, with limited technology and software. The argument seemed to be supported by the fact that the co-founders of the company come from a financial, rather than technological, background.

AI in 2024: Never a Dull Moment

In AI, it’s been the year of big, bigger and biggest. My colleague Aman Kabeer and I just did a fun episode of The MAD Podcast where we discussed what we’re seeing in the market, our favorite trends and new stories, and where we see things going.

Here’s the video, and I summarized below a few highlights:

Is SaaS dead?

Many SaaS stocks have been getting clobbered in public markets. Some see the “end of software“.

Is SaaS dead?

What seems to be happening:

tough macro, cost cutting
AI sucking the air out of the room
SaaS vendors perceived as “last generation” despite best efforts to add AI quickly
enterprise budgets for AI are not net new, they’re taken from somewhere (SaaS budgets cut)
Bulk of budgets going to OpenAI/Azure etc because low hanging fruit to “do AI” (knowledge bot, coding)
for the more specialized enterprise apps, customers feel like they can/should “build” internally rather than “buy”

What happens next:

customers realize that “build” is a headache, not always the best option
OpenAI / Azure etc can’t / doesn’t want to build hundreds of problem specific/ vertical specific apps
Takes time, but legacy and new SaaS companies truly become AI-first (not just marketing), abstract away complexity of deploying LLMs
macro environment eventually rebounds
AIaaS becomes the new SaaS – what is old is new (unedited Sat morning thoughts)
Question is what happens to all current SaaS unicorns and public companies as this transition happens

(unedited Saturday morning thoughts)

MAD 2024: Trends in AI & Data (video)

As a companion to the 2024 MAD (ML, AI & Data) Landscape (blog post, PDF, interactive website), my colleague Aman and I had a fun chat about some key trends we see in data and AI.

Some topics we covered:

The impact of open source in AI
The future of AI agents
Where are we in the AI hype cycle?
The emerging AI stack
Will AI kills SaaS?
Is the Modern Data Stack dead?

Full Steam Ahead: The 2024 MAD (Machine Learning, AI & Data) Landscape

This is our tenth annual landscape and “state of the union” of the data, analytics, machine learning and AI ecosystem.

In 10+ years covering the space, things have never been as exciting and promising as they are today. All trends and subtrends we described over the years are coalescing: data has been digitized, in massive amounts; it can be stored, processed and analyzed fast and cheaply with modern tools; and most importantly, it can be fed to ever-more performing ML/AI models which can make sense of it, recognize patterns, make predictions based on it, and now generate text, code, images, sounds and videos.

The MAD (ML, AI & Data) ecosystem has gone from niche and technical, to mainstream. The paradigm shift seems to be accelerating with implications that go far beyond technical or even business matters, and impact society, geopolitics and perhaps the human condition.

There are still many chapters to write in the multi-decade megatrend, however. As every year, this post is an attempt at making sense of where we are currently, across products, companies and industry trends.

Here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019 (Part I and Part II), 2020, 2021 and 2023 (Part I, Part II, Part III, Part IV).

Our team this year was Aman Kabeer and Katie Mills (FirstMark), Jonathan Grana (Go Fractional) and Paolo Campos, major thanks to all. And a big thank you as well to CB Insights for providing the card data appearing in the interactive version.

This annual state of the union post is organized in three parts:

Part: I: The landscape (PDF, Interactive version)
Part II: 24 themes we’re thinking about in 2024
Part III: Financings, M&A and IPOs

This Week in AI: Databricks’ Acquisition of MosaicML

(This post is part of my “This Week in AI” series, which is general off-the-cuff market commentary. I’m not an investor in either MosaicML or Databricks)

$21M per employee. That’s the price Databricks is paying for MosaicML — a total of $1.3B for 62 employees (in Databricks stock, and also includes employee retention packages).

One thing is clear – if you’re going to be aggressively acquiring Generative AI startups, you’re going to have to pay up

But it may turn out to be cheap in the long term given the size of the opportunity.

That’s because, beyond any Generative AI capabilities, Databricks’ move needs to be understood in the broader context of its fierce rivalry with Snowflake.

This Week in AI: Incumbents vs Startups (Microsoft Build & Adobe Firefly)

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.

Here’s a good piece from Sharon Goldman at VentureBeat on some of the Microsoft Build AI announcements that triggered those thoughts above

Generative AI interview on the Infinite Machine Learning Podcast

I recently got an opportunity to chat with Prateek Joshi on Infinite Machine Learning, his excellent podcast.

It was a wide-ranging conversation about Generative AI (which I would recommend listening at 1.25x speed or more, makes me a lot more articulate). We covered a range of topics including:

AI going mainstream with ChatGPT
The opportunity for Generative AI in the enterprise
Defensibility and moats of Generative AI companies
A mental model for thinking about what AI is best suited for, in terms of startup opportunities
Desirable characteristics of AI startup founding teams
Rapid fire: favorite books, favorite questions to ask when interviewing a candidate, why VC is a craft business

Links to the episode:

MAD 2023: Top 10 Trends

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.

See below for:

the video (20’53”)
the list of top trends for easy perusal
the slides

Interview on the Software Daily Podcast

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.

MAD 2023 Interview with Ternary Data

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!

The 2023 MAD (Machine Learning, Artificial Intelligence & Data) Landscape

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.

MAD 2023, PART II: FINANCINGS, M&A AND IPOs

*(note: this is part II of the 2023 MAD Landscape. The landscape PDF is here, and the interactive version is here)*

“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:

MAD 2023, PART III: TRENDS IN DATA INFRASTRUCTURE

(note: this is part III of the 2023 MAD Landscape. The landscape PDF is here, and the interactive version is here)

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.

MAD 2023, PART IV: TRENDS IN ML/AI

(note: this is part IV of the 2023 MAD Landscape. The landscape PDF is here, and the interactive version is here)

The excitement! The drama! The action!

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?

The answer to all those questions is… yes.