While the Nasdaq seems to reach new highs daily, enterprise tech spending remains persistently cautious at the macro amid continued market uncertainty.
That’s not necessarily bad news, but it can be disconcerting to decision makers. Often during transitional cycles, like the one we’re in now, executives don’t want to over-rotate on capital allocations that deliver outcomes that could potentially be achieved much more cost-effectively with emerging technologies such as artificial intelligence.
We’ve seen similar patterns in previous waves. There was a period of “great softness” in enterprise information technology spending when transitioning from mainframes and minis to the PC era, a huge backlash from overly exuberant Y2K and dotcom spending, and a steady deterioration of traditional enterprise tech momentum during era of the cloud, mobile, software-as-a-service, social and big data.
Each wave is different, but two common themes remain: 1) strong conviction that a new era is here; and 2) fear of disruption, which sometimes leads to hasty decision-making and waste. These countervailing forces seem to be in play today as 87% of enterprises report taking a cautious approach to AI investments. And that caution is showing up in the macro spend environment.
In this Breaking Analysis, we take a look at the quarterly spending data from Enterprise Technology Research and give you our take on what’s happening in the market at the macro and how the spending climate has changed. We’ll quantify the stubborn weight of uncertainty, both technological and policy-driven, and convey how enterprises are responding.
January enthusiasm has turned to caution
Let’s start with a look at tech spending sentiment going back to the boom times in 2021.
This data from ETR shows the quarterly expectations of annual IT spending growth from around 1,800 IT decision-makers. As we’ve discussed in the past, we’re well off the halcyon high single digits from the isolation economy. Calling your attention to 2025 in the above chart, we entered this year with ITDMs expecting a healthy 5.3% increase in annual spending, which declined to 3.4% this spring and has slightly upticked in the July 2025 survey to 3.6%, still well off the January highs. Of greater concern is that large spenders, reflected by the G2000 data, expect a 2.9% increase, with SMBs more optimistic at 4.5%.
The reason for the concern is small and midsized firms are more susceptible to policy shifts such as tariffs, and their ability to shield the hit is lower than large global firms.
Tech spending patterns correlate to Fed actions
It’s interesting to plot the change in tech spending against the two-year treasury as we show here.
The blue line is the price of the two-year, which is basically inversely proportional to the tech spend sentiment. In 2022 we still had the exuberance hangover from COVID, and as interest rates spiked, tech spending expectations slowly waned (from roughly 7% to 6%). But as reality set in, you can see the July 2023 spending trajectory tanked to under 3% and has remained in the mid-3% range since.
In addition to the factors we mentioned at the beginning, cyber risks have escalated along with all the geopolitical gymnastics, leading to this persistent caution that we’ve cited.
Operational resilience is increasingly vital in this era
An interesting data point from a previous ETR Macro Survey is shown here – gauging the degree to which geopolitical and other concerns have affected organizations’ posture toward operational resilience.
We can look at this data as a large percentage of customers are making resilience a high priority. Are the ones on the right more exposed or are they more resilient already? We don’t know from this data other than it conveys resilience has become a top concern, which we’ve already known for quite some time.
Cyberattacks more disruptive than ever
A main reason for this sentiment is with all the changes organizations are facing – geopolitical, the pace of AI, policy uncertainty and the like – cyber risks are cited by ITDMs as their No. 1 concern. And perhaps the most difficult to address. The urgency is shown below in this graphic from a recent study by theCUBE Research, led by Christophe Bertrand, quantifying the time it takes to recover from a cyberattack.
The leftmost data asks 600 organizations to estimate how long it would take to recover from an attack and the rightmost chart asks those who actually experienced a cyberattack how long recovery took. On the right – those who have experienced an attack – only 2% can recover in a matter of hours and only 12% less than a day. The majority of respondents in both data sets face multiple days to recover.
Christophe’s research quantifies the cost of such disruptions and there are many studies that do so as well. But the key points are: 1) the productivity hit of an attack is many days or weeks of lost productivity; and 2) organizations are beginning to understand the realities and are generally realistic about their capabilities.
The other factor to call out is that budgets aren’t unlimited . In addition to keeping the lights on and running the business, organizations have to grow and transform the business. So they have to fund new line-of-business initiatives, fund AI and keep iterating on cyber. All of these factors weigh into the macro spending data we shared earlier.
Methods organizations use to cut
For those organizations feeling the budget pressures and decreasing spend, how are they doing that? The following data depicts the approaches used.
On balance, only 20% of organizations in this study are reducing their IT spending. That’s down from 24% two years ago, which is a positive. And they’re doing it by cutting staff as you see on the left most set of bars. That approach is back to the highs of last fall. And you can see new projects getting delayed. While this is the second most common technique, it is declining in popularity. Further to the right, you can see reducing cloud expenses is picking up steam.
Methods used to accelerate spend
Let’s dig into this a little more deeply and look at how organizations approach increasing their IT spending shown below.
New project acceleration remains the top appraoch. Expansion of cloud resources, while still number two is in decline.
So based on this and the previous data, you may surmise that the cloud is under pressure.
Cloud spend continues to outpace other sectors
The data below shows spending by category – Cloud, Hardware, Outsourcing and SaaS.
Though the previous data could be interpreted as negative, the cloud still far outpaces the other broad areas shown, with 8.1% annual growth, more than double the macro average, and far above hardware, outsourcing and SaaS, which are all in the low or lower single digits.
Remember, cloud revenue for just the big three hyperscalers plus Alibaba will surpass $250 billion this year, growing revenue in the mid 20s for the group – so still very strong and outpacing other sectors. Cloud is being propelled by AI, a strong base of existing workloads that continue to grow and a flywheel of ecosystem vendors the like of which doesn’t exist on-premises.
Anticipating the AI productivity boom
Let’s close with a view on what the outcomes are that organizations are seeing from their AI spend. The vendor narrative at conferences is that we’ve exited the experimentation phase for AI and we’re entering deployment at scale. We don’t quite see it that way. Most customers continue to tell us that their AI is nascent and largely experimental, with uncertain return on investment. This in many ways is good news in that the potential for increased value is enormous – if you believe (as we do) that AI is a durable trend.
The data above shows that AI is most typically being used to enhance productivity and augment mundane human tasks. Supporting better analytics is sort of a no-brainer. When you have good analytic data – for example in Snowflake or Databricks or a cloud warehouse/lakehouse – bringing AI into the mix makes a lot of sense, is less risky and represents low-hanging fruit.
The third bar above – transformation with new revenue streams – will take more time in our view, but the net present values will be much larger than we’re seeing today with early AI experiments. The drop off to No. 4 is notable – headcount avoidance or reductions – but it’s real and tangible that AI will affect jobs. And surprisingly, or not, 10% still are really not going after AI in these stated areas. Our interpretation is they’re waiting for the pioneers to take the arrows.
Imagine a 10% productivity boost in a $100T+ AI economy
Let’s come back to the point on productivity. As we’ve shared in previous Breaking Analysis segments, there have really only been two sustained decade-long productivity booms since Word War II. The first was in the post-war era with a massive increase in manufacturing in the United States. This spurred a consumer buyer flywheel that drove productivity in the late ’50s into the ’60s. The second major uptick was due to the personal computer productivity boom in the 1990s. Many – us included – believe that we are on the cusp of another sustained period of productivity growth, globally.
Let’s think about this for a moment. VC investments in AI were around $50 billion in 2023, more than doubled last year and are on pace to increase this year. The big three hyperscalers plus Meta Platforms will spend well north of $300 billion this year on capital spending. So let’s round way up and say $500 billion is being spent on AI, with – as we said – limited return on capital at the moment. Sounds like a lot, right? Half a trillion dollars for a marginal return?
But let’s step back. The annual global economy is north of $100 trillion. People are talking about 10%, 20%, 30% improvements in productivity. Let’s take 10%, which by the way would be a massive positive to the economy. That’s a $10 trillion annual increase in productivity. So taken in that context, $500 billion maybe isn’t that outrageous and perhaps is even conservative.
The question for you is should you dive in or wait? There are many examples of where fast followers have thrived in these new waves. Facebook wasn’t the first social media company. Dell wasn’t the first PC maker. Google wasn’t the first search engine. So over rotating on capital allocation early in a cycle can be dangerous. At the same time, not participating will almost certainly leave you behind.
So our advice is:
Pick the right spots in your business – i.e. start with business value;
If you have a choice between high value and easy wins – pick the easy wins first;
Get the culture behind the idea that a new wave is coming and they must ride the wave or end up as driftwood
Use this mindset to build muscle memory and then let the distributed/decentralized organization build value throughout the system organically.
Those your organization will know where to deploy the agents. Your job as an executive is to provide the North Star direction, a foundation of a solid data strategy and the tools and resources so they can make it happen.
We’re very excited about the future, as I’m sure you are too. The macro uncertainties are always there – even when things are booming, there are blind spots around every corner. So hopefully our data and your knowledge of the market can help you navigate the unknown. We’re looking forward to being on the journey with you.
Disclaimer: All statements made regarding companies or securities are strictly beliefs, points of view and opinions held by SiliconANGLE Media, Enterprise Technology Research, other guests on theCUBE and guest writers. Such statements are not recommendations by these individuals to buy, sell or hold any security. The content presented does not constitute investment advice and should not be used as the basis for any investment decision. You and only you are responsible for your investment decisions.
Disclosure: Many of the companies cited in Breaking Analysis are sponsors of theCUBE and/or clients of theCUBE Research. None of these firms or other companies have any editorial control over or advanced viewing of what’s published in Breaking Analysis.
Image: theCUBE Research
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This undated photo shows robots on display at the World AI Conference (WAIC) in Shanghai, China. (PHOTO / BLOOMBERG)
A blistering rally in Chinese mainland technology shares accelerated on Wednesday as renewed bets on artificial intelligence sent a key gauge to the highest in nearly four years.
The Hang Seng Tech Index, which tracks the largest tech firms listed in the Hong Kong Special Administrative Region, rose as much as 3.9 percent to hit its highest level since November 2021.
Search engine operator Baidu Inc led gains with a 19 percent jump but multiple tech giants came along for the ride: Shares of Alibaba Group Holding Ltd, Semiconductor Manufacturing International Corp, and JD.com Inc all surged in morning trading.
The index is now set for its seventh consecutive week of gains, helped by hopes that tech companies’ big bets on AI will pay off. The gauge has surged 41 percent this year, trouncing a benchmark of regional peers.
“China tech leaders are visibly re-accelerating AI spend and product rollouts — models, robotaxis, in-house chips — while also proving they can monetize AI faster than many expected,” said Charu Chanana, chief investment strategist at Saxo Markets. “With valuations lagging the US, investors are starting to pay attention again.”
The Hang Seng Tech Index trades at around 20.5 times forward earnings, below its five-year average of 23.3 times earnings and Nasdaq 100 Index’s ratio of 27 times, according to Bloomberg-compiled data.
Brokers are quickly lifting price targets. Goldman Sachs Group Inc has raised its target for Alibaba’s shares, citing a better outlook for its cloud business. Arete Research Services LLP lifted its rating on Baidu’s American depositary receipts to buy from sell on the growth potential for its in-house chip business. Earlier this week, JPMorgan Chase & Co upgraded its rating on battery maker Contemporary Amperex Technology Co.
Other headwinds for the mainland’s internet sector are starting to clear. A local media report citing JD.com chairman Richard Liu saying he was not interested in starting a price war in the hotel sector sent shares of the e-commerce giant surging by more than 6 percent. Rivals including Meituan and Trip.com Group Ltd also jumped.
Big Spending
The mainland’s biggest tech companies are in the middle of a spending spree on AI, as they race against one another and against US firms to conquer a market widely expected to revolutionize how people live and work.
Total capital expenditure from major mainland internet firms such as Alibaba, Tencent Holdings Ltd, Baidu and JD.com is set to hit $32 billion in 2025, more than doubling from $13 billion in 2023, according to a Bloomberg Intelligence report.
That has helped create a funding spree in equity and bond markets. Alibaba raised $3.2 billion from a blockbuster convertible bond offering last week, while Tencent turned to the dim sum bond market for 9 billion yuan ($1.27 billion) on Tuesday, its first bond sale in four years.
The latest news fueling optimism was a state television report Tuesday night that China Unicom’s Sanjiangyuan data center has signed contracts to deploy AI chips from local firms including Alibaba’s chip unit T-Head.
Separately, mainland foundry SMIC’s shares jumped over 6 percent following a report that it is running trials on the mainland’s first domestically produced advanced chipmaking equipment.
The booking funnel is fractured by generation, with younger travelers leaning more on technology and social media.
In July, travel technology company iSeatz commissioned that included responses from 1,000 Americans travelers over the age of 18. The survey, conducted by Talker Research and titled “The Modern Traveler 2025,” revealed that the booking path no longer follows traditional patterns.
“Today’s travelers are not just choosing destinations. They are navigating a digital journey from discovery to booking, and they expect it to feel effortless, intuitive and personalized at every step,” said Kenneth Purcell, founder and CEO of iSeatz. “These rising expectations do not come out of nowhere. Consumers have been conditioned by the digital ease of e-commerce, social media and streaming platforms.”
ISeatz found that travelers are discovering travel opportunities in a more fluid process, which also requires more steps.
“Instead of following a straight path from idea to booking, most travelers now move back and forth between inspiration, research, comparison and planning across a variety of platforms,” iSeatz said in its report.
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This shift is tied to social media for younger generations, while older generations are still relying on friends and family for recommendations.
Generally speaking, 43% of respondents said they are inspired by loved ones. But younger generations are more often inspired by social media: 52% of Gen Z and 46% of millennials said it is their “primary source” for travel inspiration.
The report found that during the research phase, 45% of Gen Z members prefer using social media over traditional search engines. Overall, 43% of travelers still use traditional search engines like Google and Bing, but 27% go to social media first.
Nearly 40% of travelers also said social media influencers had a “significant impact” on how they book and where they travel, with that figure ticking up among younger generations. Sixty-two percent of Gen Z respondents said influencers impact their decisions.
Social media’s influence is further illustrated by Phocuswright research that found almost two thirds of travelers made a trip purchase or visitation based on content they viewed while trip planning.
Considering the survey results, iSeatz said some travel brands are missing the mark.
“There isn’t currently enough technical infrastructure to support discovery-to-booking experiences within social platforms,” iSeatz said. “That’s a missed opportunity: 53% of millennials and 52% of Gen Z say they’d book travel directly from social media if it were secure and seamless.”
But regardless of age or generation, the funnel is still fragmented, according to iSeatz.
“Travelers often jump between social feeds, search engines, review sites and booking engines, which creates both friction and opportunity. Travel brands that can bridge these gaps will be better positioned to capture interest and convert it into action.”
Additional AI findings
The rise of artificial intelligence (AI) is having an impact on traveler behavior too, as other reports have also found.
Around one in five travelers reported regularly using AI, and that percentage ticks up among younger travelers, with 35% of Gen Z and 34% of millennials using AI regularly.
And with AI tools maturing, travelers are anticipating more personalization, iSeatz found.
“Fifty-seven percent of travelers already expect brands to anticipate their preferences and needs based on past behavior,” iSeatz said in its report. “Millennials, in particular, are driving this shift. Seventy-four percent say personalization is a baseline expectation, not a bonus.”
And the majority of travelers are not strongly opposed to sharing their data to make that happen.
“The travel companies that succeed in this new landscape will be the ones that understand their customers deeply and design every touchpoint around what today’s travelers value most,” iSeatz said.
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Agentic AI promises a future where intelligent digital agents handle complex tasks across industries, but significant barriers stand in the way.
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The era of agentic AI is here, or so we are told, bringing super-smart AI assistants capable of carrying out complex tasks on our behalf.
This represents the next generation of AI beyond current chatbots like ChatGPT and Claude, which simply answer questions or generate content.
Those building (and selling) the tech tell us we are on the verge of a fully-automated future where AIs cooperate and access external systems to carry out vast numbers of routine knowledge and decision-making tasks.
But just as emerging concerns around hallucinations, data privacy and copyright have put up barriers to generative AI that some organizations have found insurmountable, agents have their own set of obstacles.
So, here’s my rundown of the challenges that developers of AI agents, organizations wanting to leverage them, and society at large will have to overcome, if we’re going to deliver the promised agentic future.
Trust
The biggie. To achieve the critical mass of adoption needed to fuel mainstream adoption of AI agents, we have to be able to trust them. This is true on several levels; we have to trust them with the sensitive and personal data they need to make decisions on our behalf, and we have to trust that the technology works, our efforts aren’t hampered by specific AI flaws like hallucinations. And if we are trusting it to make serious decisions, such as buying decisions, we have to trust that it will make the right ones and not waste our money.
Agents are far from flawless, and it’s already been shown that it’s possible to trick them. Companies see the benefits but also understand the real risks of breaching customer trust, which can include severe reputational and business damage. Mitigating these risks requires careful planning and compliance, which creates barriers for many.
Lack Of Agentic Infrastructure
Another problem is that agentic AI relies on the ability of agents to interact and operate with third-party systems, and many third-party systems aren’t set up to work with this yet. Computer-using agents (such as OpenAI Operator and Manus AI) circumvent this by using computer vision to understand what’s on a screen. This means they can use many websites and apps just like we can, whether or not they’re programmed to work with them. However, they’re far from perfect, with current benchmarking showing that they’re generally less successful than humans at many tasks.
As agentic frameworks mature, the digital infrastructure of the world is likely to mature around them. Most people reading this will remember that it took a few years from the introduction of smartphones to mobile-friendly websites becoming the norm. However, at this early stage, this creates risk for operators of services like e-commerce or government portals that agents need to interact with. Who is responsible if an agent makes erroneous buying decisions or incorrectly files a legal document? Until issues like this are resolved, operators may shy away from letting agents interact with their systems.
Security Concerns
It doesn’t take much imagination to see that, in principle, AI agents could be a security nightmare. With their broad and trusted access to tools and platforms, as well as our data, they are powerful assistants and also high-value propositions for cybercriminals. If hijacked or exploited, criminals potentially have decision-making access to our lives. Combined with other high-tech attacks, such as deepfake phishing attempts, AI agents will create new and potentially highly problematic avenues of attack for hackers, fraudsters and extortionists. Agents must be deployed by individuals as well as businesses in a way that’s resilient to these types of threats, which not everyone is yet capable of doing.
Cultural And Societal Barriers
Finally, there are wider cultural concerns that go beyond technology. Some people are uncomfortable with the idea of letting AI make decisions for them, regardless of how routine or mundane those decisions may be. Others are nervous about the impact that AI will have on jobs, society or the planet. These are all totally valid and understandable concerns and can’t be dismissed as barriers to be overcome simply through top-down education and messaging.
Unfortunately, there’s no shortcut available here. Addressing this will involve demonstrating that agents can work in a reliable, trustworthy and ethical way. Pulling this off while also building a culture that manages change effectively and shares the benefits of agentic AI inclusively is the key here.
Agents Of Tomorrow
The vision of agentic AI is quite mind-boggling: Millions of intelligent systems around the world interacting to get things done, in ways that make us more efficient and capable.
As we’ve seen, however, the obstacles to this are just as likely to be human as they are technological. As well as solving fundamental issues like AI hallucination, and building infrastructure that enables agents in ways that are trustworthy and accountable, we have to prepare society for a fundamental shift in the way people work with machines.
Accomplishing this will pave the way for AI agents to hit the mainstream in a safe way that enhances our lives rather than exposes us to risks.
