 From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. We're going to use to the phrase higher for longer, referring to the realization that interest rates are expected to remain elevated. This trend appears to have an inverse effect on enterprise tech spending. Prior to the Fed's tightening binge, for example, IT decision makers, ITDMs, expected annual technology spending increased by 7.5%. 11 Fed interest rate hikes later, ITDMs estimate that their 2023 budgets will be up only 2.9% this year with an expectation or perhaps it's a wishful hope that their budgets will be up 3.8% in 2024. Hello and welcome to this week's Cube Insights, powered by ETR. In this our 200th Breaking Analysis, we preview the current spending climate and where AI fits in relation to other sectors. And we'll explain the importance of that statement. We'll also share with you a snapshot of the leaders on the board in terms of spending velocity on their platforms and how their performance compares to their peers and relates to earlier periods. Now, one of the superpowers of ETR's approach is its strict adherence to consistency over time. This allows for an apples to apples comparison that highlight changes at the margin. Let's look at an example. This chart shows the spending expectations from IT decision makers over the past 10 quarters. Shows the survey month and the year at the bottom starting with the September 21 survey all the way through October 2023, the most recent survey. The survey ends, they range from around 1500 to more than 1700 completes, depending on the survey quarter. Now that blue line shows the 2022 outlook starting in the September 2021 survey. So at that time, ITDMs expected their 2022 spending levels to grow by a robust 7.2%. Now that figure jumped as we exited 2021 all the way up to 7.5% entering 2022. Then as we progressed through the year, two significant changes occurred. First, Russia's invasion of Ukraine that February then followed by the Fed's relentless tightening cycle. And you can see the deceleration and spending expectations over the year 2022 as we progressed over time. We exited the year at 4.6%. Now what actually happened in 2022, we've seen numbers from Gartner and IDC that range from flat 0.8% up to even five as high as 5.5 or even 6% up. I think it's reasonable to assume that the market grew that year in the low single digits. Now as we entered 2023, that's the red line. Fed tightening was having the desired effect at least on tech spending, perhaps not so much on the consumer side. And expectations for the year were 4.1% spending growth. And then notice the meaningful deceleration as the Fed actions continued through September and took hold where ITDMs now believe that their 2023 budgets, the growth of those budgets will come in at around 2.9% this year. And they're hopeful that in 2024, we'll see an uptick to 3.8% spending increases. But given the current climate, uncertainty around debt and the latest mess in DC with the speaker of the house and the confusion around the election, the likelihood of more downward revisions is probably higher than not. A part of the issue is that generative AI is in some ways both a blessing and a curse. The excitement potential and magic of LLMs is fantastic. It creates urgency and mobilizes teams to focus on creating new forms of value, new business models, restructuring business processes and the like. But given the economic climate, what we're seeing is generally not a lot of new money coming into ITD budgets, rather there's a reprioritization happening. Here's some data that shows what I'm referring to. This chart shows the nearly 30 sectors in the ETR taxonomy. The vertical axis is net score for the sector, which is ETR's proprietary measure of spending momentum. It essentially represents the percentage of customers that are accelerating spending within a sector. It does this by netting out those customers that are managing spending levels that are flat to down. The horizontal axis shows sector pervasion, which is calculated by the number of customers spending in a particular sector, divided by the total N of the survey, which in this last survey is around 1,725. Think of it as a proxy for market presence on an account basis, importantly, it's not a dollar basis. Now that red line at 40%, that represents a highly elevated spending velocity. Note that during the pandemic, cloud, containers, RPA, and AI were the four sectors that consistently held above 40% on the Y axis. Now notice also that squiggly line on ML AI. Spending levels began decelerating because firms kind of over-rotated a bit on the sector. But then the chat GPT announcement hit, and you can see the accelerated momentum returns. But almost every other sector, not all, but most, started to descend. There was compression on the vertical axis. And that's an indication of robbing Peter to pay for AI Paul. So this is the curse of where AI, the curse of AI where the clear business value is still being sought after. Budgets are tight and so other projects are being delayed and money is being redeployed into what are primarily AI experiments at this time. Now, generally established tech companies are adapting to this new reality. Well, there are many Series B and Series C startups that aren't AI companies and or they haven't nailed their product market fit or their go-to market fit. You know, they may be struggling. Mainstream enterprise tech firms, they've raised a lot of capital during COVID. They have strong balance sheets and they're making moves. Some examples, Cisco Splunk, a $28 billion acquisition. Many like CrowdStrike and Palo Alto Networks are growing. You know, big game species like Dell, they're throwing off cash and promising to return 80% or more of adjusted free cash flow back to investors. So company management's have generally adjusted nicely to this new environment. Here's a chart that shows the leaderboard in terms of spending velocity that net score on their respective platforms. The chart shows the net score methodology in more granular detail with the percent of the citations, the citations, the ends are shown in the last column over on the right. Net score then breaks down by new adoptions. Those customers spending, where spending is increasing, the present of customers spending is flat and then those spending down and replacements or churn. The net score is calculated by subtracting the spend less from the spend mores. And that's what you get in the green column. And again, note that red line at 40%. Anything above that is highly elevated. Now you're probably not surprised, but it's still impressive that OpenAI is shot up to the number one position and is holding that with a 78% net score. They are far ahead of any other player even though their net score is down 10 percentage points from the last quarter survey, but they weren't even in the survey last year at this time. Yet they have an end that's higher than Databricks and Snowflake or Snowflake, not an end. Speaking of which, for years, Snowflake was number one in this metric for many, many, many quarters. I think probably over eight or nine quarters. And they came down to Databricks level. It kind of came down to Earth a couple of quarters ago and they continue to decelerate to the point where now Databricks is ahead of them in terms of spending momentum. Remember, this is an account-based metric. It does not reflect spending levels, the amount of dollars. So ACVs are not a factor here. It's percentage of customers. Now both Snowflake and Hashi have come down to Earth and they each show a meaningful year over year net score decline. But also notice this is largely due to the percent of customers that are shifting into that flat spending bucket, okay? So that flat spending bucket counts as a negative to tracks from net score. So basically the flat spend less and the churn are not included in the net, I mean, they're included in terms of they're subtracted out from that net score. So net score really focuses on new adoptions and those the percent of customers spending more as the metric. So as you shift the percent of customers into that flat spending bucket, the net score declines, okay? So that is largely what's accounting for the declines for those two companies and frankly, these leaders as opposed to spending declines and defections. So churn is pretty low. Now Wiz is the other notable call-out in this chart. Hot company, we saw them at RSA this year and in April, May and a big party, the line-out to get into the party was huge. They got evidently the reporting from the customers we talked to, great sec ops, analyst experience, kind of modern new product, but we're seeing a meaningful change in both year on year and survey to survey and that could be a factor related to security consolidation and maybe some competition from others like CrowdStrike who seem to be doing quite well. Now, I'm somewhat limited in how deep I can go today because ETR is in its quiet period and has two webcasts coming up one next Friday and the other a following Friday to go deep into the spending topic. So if you're an ETR client or a joint client, you can attend those, but I'm limited in what I can share with you on breaking analysis until they go public. So let's wrap up with some thoughts on what to look for in the months and quarters ahead. Now, look, AI initiatives are not creating, as we talked about earlier, incremental funding. They're stealing from other budget buckets. We're seeing a little bit of compression in places like RPA. Cloud is holding pretty well, although it's definitely down. Containers obviously holding well because from an application development standpoint, a lot of the traditional IT infrastructure is again under pressure. There are some examples in some enterprise software segments that are ticking up a little bit, but generally speaking across the board, there's compression in other sectors as projects are delayed and some experimental funding is going to AI because companies are paranoid that they're going to miss the wave and the train's going to leave without them. Speaking of that, while 75% of enterprise customers are actively evaluating gen AI of those, the vast majority, let's call it around 80%, are still in eval mode. And then there's privacy, legal, and compliance concerns presenting other key hurdles before they can get to deployment. Now, until a definitive business value can be realized via labor cost reductions or perhaps revenue generation, but the former is probably the fastest path, the value, it's before, unless that can be shown, it's likely that the spending client is going to remain cautious as higher for longer appears to be the new normal. So right now, prudent guidance from company management is in play. And of course, comparisons are somewhat easier, certainly than they were in 2022 and even 2023, but we'll see. A significant shift in sentiment could bring estimates down. So we'll keep an eye on that. And for now, we're going to leave it there. I want to thank Alex Meyerson, who is on production and can shift men. So they manage the podcast, they do all the in-studio stuff here and in our office, in our studio outside of Boston, Kristen Martin and Cheryl Knight, they help get the word out on social media and in our newsletters. And Rob Hoef is our editor-in-chief over at SiliconAngle.com. He's doing some awesome stuff and great editing and doing a weekly summary of all the news. He helps us with our cube pod. So thank you, Rob. Remember, all these episodes are available as podcasts, wherever you listen. All you got to do is search Breaking Analysis Podcasts. It's really taken off. And thank you very much for listening and subscribing. I publish each week on wikibon.com and siliconangle.com, where you can email me at david.volante at siliconangle.com or DM me at dvolante. You can comment on my LinkedIn posts and do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Volante for theCUBE Insights, powered by ETR. Thanks for watching, everybody. We'll see you next time on Breaking Analysis.