I stated in my April port review, “although not at all an immediate concern, I see Datadog the most likely among what I own to be looking at more competition, as As Good solutions (being built on top of Snowflake, utilizing the new highly efficient LLM as a business model) are now inevitable IMO.” I’ve seen a lot of changes in portfolios here. I haven’t seen much in why, with all due respect, other than personal differences in risk appetite and the love of the hunt for where the puck is going. I’m betting a lot on the timing being unknowable for what will shake out, given the quick commoditization of intelligence.
Snowflake 32%
Cloudflare 26%
Tesla 32%
Datadog 8%
Intellia 1.5%
Given the remarks below, by Olivier Pomel, I’m not alone in the above assumption. The following is largely why I consolidated further in my highest conviction positions (future proofing). I don’t see any industry sector not being significantly altered. Therefore, I don’t see diversification as an alternative to this consolidation.
(Bolding, and what’s in parentheses below are mine)
Oliver Pomel, CC, Opening Remarks:
“AI, …long term, will significantly expand our opportunity in observability and beyond. We think massive improvements in developer productivity will allow individuals to write more applications and to do so faster than ever before. And as we surpass productivity increases, we think this will further shift value from writing code to observing, managing, fixing and securing live applications.
In the short to medium term, we believe the rise of AI will increase the demand for compute and storage to train and run models, but it will also increase the value of proprietary data and further drive digital transformation and cloud migration as these are all prerequisites for adoption. We also do expect quite a bit of noise in the market, as the technology stack is progressing and changing very quickly.
Now, from a product perspective, we believe that we at Datadog are uniquely positioned to deliver value to our customers in this new world (sarcastic?). First, we built Datadog from day one as a pure SaaS business precisely to be able to put all that to work at full scale and to train models to solve our customers’ problems.
Second, our large surface of contact with our customers gives us the insertion points to make AI relevant. This is where we see the value of having a very broad customer base and being designed to be used every day by every single engineer.
And third, we serve today some of the largest builders and consumers of AI services and are quickly adapting to their needs in a rapidly changing field.
Olivier Pomel
QnA
First, I’d say it’s – we can all agree that it’s a fascinating time to be alive to see all these rapid innovation in the world of AI. The first thing I’d say is that it’s still fairly early in terms of what the market is going to look like in the AI world. Right now, there’s one particular thing that’s been – that used to be very hard, which was in building conventional models and chatbots and things like that, which almost overnight became almost a commodity, basically. Anybody can incorporate in any application. It’s an API call away. And there’s even a number of different options, commercial open source you can use today. So that just happened. That plan (to incorporate AI into Apps) has massive traction. You see it everywhere. But it also is opening the gate to many, many more – I would say more customized, deeper applications on AI that may be built by a few vendors or may be built by a large number of companies instead. It’s not quite clear yet.
We see, on our end, is that it’s going to drive more compute, it’s going to drive more value in the data that is being gathered by companies, it’s going to drive digital transformation, it’s going to drive cloud migration because, again, you can’t actually adopt AI unless you have the data. You can’t actually adopt it without having a modern architecture and an application you can scale up and down and infrastructure you can quickly provision and deprovision. You need to capture all of your – to capture your data, you need to be digitally transformed. So you have data of all your customer interactions and everything that is proper to your business. So in the meantime, we see that as a very clear accelerant to our business. Maybe with a little bit of noise …out of 200 new things, there’s probably only 10 or 20 that will matter to us all now. But it’s hard to know which ones is out today.
So short answer is midterm, a lot more of the current workload – types of workloads we see maybe with different types of technologies. Longer term, I think we can all glimpse at a future where productivity for everybody, including software engineers, increases dramatically. And the way we see that as a business is – our job is to help our customers absorb the complexity of the applications they’ve built, so they can understand them, modify them, run them, secure them. And we think that the more productivity there is, the more people can write in any amount of time. The less they understand the software they produce and the more data, the more value it sends our way. So this is what makes us very confident in the long term.
So the way we imagine the future is companies are going to deliver a lot more functionality to their users a lot faster, they’re going to solve a lot more problems in software, but there won’t be as tight an understanding from the engineering team as to what it is they build and how they built it and what might break and what might be the corner cases that don’t work and things like that. And that’s consistent with what we can see people building with a Copilot today and things like that. These are very, very good for solving a small problem, but they don’t help you build consistent [indiscernible] or they don’t help you build software platforms like that. That stuff is still out of reach. Again, the way we see the future is we’ll feel customers do a lot more and they will still need help to catch up with everything they’re doing and we’ll be the ones to do that for them.
(?) any partial offsets from organizations having greater intelligence and automation at their fingertips? Are there certain workloads that may no longer need to be monitored by an observability platform?
Olivier Pomel-
In the long term future, everything is possible. But I don’t think – today, I don’t think that’s not what we hear or see.
In terms of what customers do today, it’s hard to project the current adoption of AI into what it might look back into the future because, right now, AI is mostly used as an API call for most companies, but we don’t think it’s necessarily going to be the case one to five years from now.
(For the last A of the QnA)
… Obviously, the expectation for some of those(our) products are changing over time too. You know that everyone can see what can be done with AI. We really expect to see a lot more of that. So, I guess we’ll share more on that in the near future.
Me here:
I know that the promise of AI has been blaring out of the mouths of many a Futurist, for literally decades. But, might that have led us to be complacent when there does appear a paradigm shift staring us in the face?
If Olivier Pomel takes that much time in his CC to delineate the changing role of Datadog
going forward, because of Advances in Large Language Models, what companies are going to benefit most and what companies will no longer be relevant?
Best,
Jason