#31 - Navigating the AI & Data Landscape: Insights from Matt Turck’s 2024 Analysis
Key insights and strategies from Matt Turck's 2024 AI, Machine Learning, and Data Landscape report
If you’re in charge of the Data & AI strategy at your company, you know how tough it can be to keep up with the external landscape. On one hand, it’s thrilling to see so many new companies emerging, each offering exciting AI, analytics, and smart operations solutions. On the other hand, it’s hard to create a clear roadmap for your own team when everything seems so unstable and constantly changing.
While I won’t dive into the complexities of navigating our dynamic future in this post, I want to share something that could really help you. Every year, Matt Turck and his team at the VC firm FirstMark put together an analysis of the AI, Machine Learning, and Data Landscape. This is, hands down, the most thorough and insightful research I’ve come across on the topic. I’d love to share a few highlights with you.
You can read the full blog here.
And, if you prefer listening, check out this interview about the 2024 MAD landscape between Matt Turck and Databricks’ Tristan Hand here.
Here are some key insights that stood out to me:
The 2024 MAD landscape is more crowded than ever, with 2011 logos, up from 1416 last year. Matt explains that this is due to two big waves of company creation and funding: the first driven by data infrastructure companies triggered by Hadoop, and the second by ML/AI companies, driven by Generative AI.
Every software company is now a data company. Today, software is driven as much by data as it is by code, and AI is following this path. Matt envisions a future where AI becomes “deep code,” leading to an explosion of companies building application-level products on top of it.
OpenAI and Microsoft: despite their close partnership, both are aiming to be the go-to providers for AI applications. Matt notes that, much like Kubernetes for software, there are “commodities” for AI applications that both companies are competing to offer.
Snowflake, Databricks, and Microsoft Fabric: In the data infrastructure space, Snowflake and Databricks are moving at different speeds. While Snowflake is slower to embrace AI, Databricks stands out as a key player in Generative AI with its acquisitions and product development. Meanwhile, Microsoft Fabric is emerging as a promising vendor, offering an end-to-end SaaS platform for data and analytics. This is great news for mid-sized companies that have fewer resources and are looking for a comprehensive platform solution.
Putting together a Modern Data Stack requires stitching together various best-of-breed solutions from multiple independent vendors. As a result, it’s costly in terms of money, time, and resources. This is not looked upon favourably by the CFO office in a post-ZIRP budget cut era. - Matt Turck
There’s so much more to uncover in his full blog, and I really encourage you to explore it. Take some time to think about the key actions you can take from these insights—whether it’s for your own development, educating your team, or shaping your Data & AI strategy. Here are a few actions I’m considering:
Look into Microsoft Fabric and Azure AI.
Find the right balance between getting the infrastructure right and delivering data products quickly.
That’s it for this week! If you enjoy or get puzzled by the content, please leave a comment so we can continue the discussion. Throw in a like as or share as well if you know someone who may enjoy this newsletter. Thanks!