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.
(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:
(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.
(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?
For anyone interested in a quick overview of our long-form 2021 Machine Learning, AI and Data (MAD) Landscape, here are the Cliffs Notes! My co-author John and I did a presentation at our most recent Data Driven NYC, focused on top 10 trends in this year’s landscape.
As a preview, here they are:
Every company is a data company
The big unlock: data warehouses and lakehouses
Consolidation vs data mesh: the future is hybrid
An explosive funding environment
A busy year in DataOps
It’s time for real time
The action moves to the right side of the warehouse
The rise of AI generated content
From MLOps to ModelOps
The continued emergence of a separate Chinese AI stack
Below is the video from the event, and below that, the transcript.
Full resolution version of the landscape image here
It’s been a hot,hot year in the world of data, machine learning and AI.
Just when you thought it couldn’t grow any more explosively, the data/AI landscape just did: rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc.
It has also been a year of multiple threads and stories intertwining.
One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, Datarobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index – both indexes will be updated soon).
But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the last year or so. As we will discuss, part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market.
In the last year, there’s been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicle, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entire new categories (data observability, reverse ETL, metrics stores, etc.) appearing and/or drastically accelerating.
To keep track of this evolution, this is our eighth annual landscape and “state of the union” of the data and AI ecosystem – co-authored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019 (Part I and Part II) and 2020.)
For those who have remarked over the years how insanely busy the chart is, you’ll love our new acronym – Machine learning, Artificial intelligence and Data (MAD) – this is now officially the MAD landscape!
Today, Dataiku is announcing a major new financing – a total of $400m at a $4.6B valuation, led by Tiger Global (which had also invested in the company’s Series D), alongside a great group of existing and new investors.
While financings are ultimately just milestones, this is certainly a testament to the remarkable progress the company has been making towards becoming a major global software player, as it has scaled to hundreds of customers around the world and some 750 employees (and yes, hiring a lot more).
Beyond the headlines and high-fives, what is the story? Here’s a quick industry backgrounder and reminder for anyone new to the company.
A huge part of the data world has been historically focused on business intelligence, with both historical players (Tableau, Microsoft’s Power BI, Google Looker) and newer players (SiSense, Mode, etc.). Business intelligence tools enable you to analyze the past and the present of your business: “which region performed best last quarter?”, “who are our best salespeople?” etc. This is sometimes referred to as descriptive analytics.
Dataiku is a leader in another part of the data world, which different people call different names: data science, enterprise AI (for artificial intelligence), enterprise machine learning. Beyond the semantics, the core idea is to make it possible to asnwer questions about the future of your business, based on the analysis of historical data: “which customers are most likely to buy this product?”, “which customers are most likely to churn?”, “which transaction is most likely to be fraudulent?”, “which region is most likely to show strong demand this month?”. This area is sometimes referred to as predictive analytics.
We all have insatiable appetite for video, both in our personal and professional lives. Time and again, video is shown to capture our attention better than any other medium. This is increasingly how we learn, explore, collaborate and get entertained.
However, especially in an enterprise context, creating professional-quality video remains a complex and costly endeavor. For all the capabilities of smartphones, most companies still need studio-level equipment to produce enterprise-grade videos: cameras, sound equipment, actors, post-production editing. The process is time-consuming, and not very scalable. Shooting a video in multiple languages, for example, requires multiple actors or dubbing, Any update requires everyone to go back to the studio.
But what if video could be just… code? What if it could be infinitely flexible and customizable at scale, as simple as an API call?
Today we’re excited to announce that FirstMark led a $12.5M Series A investment in Synthesia – a fast-growing startup that offers exactly that.
Synthesia makes creating a business video as simple as writing an email or putting together a powerpoint presentation – a compelling “text to video” experience.
Today, we are previewing a new public market index – the MAD (for machine learning, AI and data) index.
Readers of this blog know that we have been tracking the data ecosystem since 2012, through annual landscapes (see the 2020 Data & AI Landscape).
Over the last few years, a funny thing happened – some of the small startups we had started tracking grew up, did an IPO and became large public companies.
Not so long ago, public market investors used to say there’s was no good way of “playing” the Big Data and AI trends, due to the lack of public companies in the space. This is less true today.
However, there isn’t much out there in terms of looking at those public companies as a group. For example, see this Seeking Alpha piece, Top 3 Artificial Intelligence ETFs To Consider, where none of the companies listed are actually AI companies.
Hence the idea of the MAD Index. It’s still a small group of companies, but my colleague John Wu and I were curious to see how they fared in public markets, now and going forward.
This is just a start. We anticipate that a number of companies will join this group in the next year or two, and we’re excited to see how this index matures.
For anyone following the software industry, there’s been a little bit of snark about C3.ai (“C3”) over the years. Here’s a company that was founded by Silicon Valley royalty (Tom Siebel, who sold Siebel Systems to Oracle in 2006 for just shy of $6B), with seemingly limitless access to capital, that somehow seemed to be pivoting every few years to something new – from energy at first, to the Internet of Things, to Artificial Intelligence.
C3 also largely eschewed the startup echochamber – funded personally by its founder at first, it didn’t raise money from the usual VC suspects, target well-know startups as its first customers, or open source any AI frameworks, working instead with a small group of Fortune 1000 and government customers. As a result, it didn’t build the kind of buzz that often precedes the most notable startups on their way to becoming public.
Lo and behold, what emerges in this IPO is a solid company by enterprise software IPO standards, with $157m in revenue, growing 71% yoy, a 75% gross margin and a $69m loss.
It will be interesting to see how the market reacts to this IPO.
On the one hand, C3 is not growing anywhere as explosively as a Snowflake, and in fact seems to have just had a bad quarter of decelerating growth. There are also other concerns, including account concentration and a substantial loss (not as pronounced as a Snowflake or Palantir, but still on the higher range of the software market).
On the other hand, the tailwinds around the deployment of ML/AI in the enterprise are very strong, and C3 is clearly positioning itself as one of the very first enterprise AI companies to go public: its ticker symbol on the NYSE will be “AI”, and the term “machine learning” is mentioned 56 times in the S-1.
This IPO will be an interesting test for the continued appetite of financial markets for all things AI.
Here’s a quick analysis of the S-1 and main characteristics of the business, put together by my FirstMark colleague John Wu and I.
In a year like no other in recent memory, the data ecosystem is showing not just remarkable resilience but exciting vibrancy.
When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Yet, as per Satya Nadella, “two years of digital transformation [occurred] in two months”. Cloud and data technologies (data infrastructure, machine learning / artificial intelligence, data driven applications) are at the heart of digital transformation. As a result, many companies in the data ecosystem have not just survived, but in fact thrived, in an otherwise overall challenging political and economic context.
Perhaps most emblematic of this is the blockbuster IPO of Snowflake, a data warehouse provider, which took place a couple of weeks ago and catapulted Snowflake to a $69B market cap company, at the time of writing – the biggest software IPO ever (see our S-1 teardown). And Palantir, an often controversial data analytics platform focused on the financial and government sector, became a public company via direct listing, reaching a market cap of $22B, at the time of writing (see our S-1 teardown).
Earlier this week, Forbes published a piece on ScaleFactor, a startup using AI to automate accounting, which shut down after raising $100m.
Here’s the heart of the issue covered in the story: “Instead of [AI] producing financial statements, dozens of accountants did most of it manually from ScaleFactor’s Austin headquarters or from an outsourcing office in the Philippines, according to former employees. Some customers say they received books filled with errors, and were forced to re-hire accountants, or clean up the mess themselves.“
