

Make no mistake: We are entering a technology cycle that is completely new.
Massively parallel computing and the generative artificial intelligence awakening is creating an entirely different industry focus that has altered customer spending patterns. Moreover, this new computing paradigm has changed competitive dynamics almost overnight. Decisions whether to devote tens of billions or hundreds of billions to capital spending are being challenged by novel approaches to deploying AI. Geopolitical tensions are higher than at any time in the history of tech.
Though the pace of change appears to be accelerating, causing consternation and confusion, the reality is that broad technology adoption evolves over long periods of time, creating opportunities, risks and tectonic shifts in industry structures.
In this special breaking analysis, we’re pleased to introduce a new predictions episode featuring some of the top analysts at theCUBE Research.
With us today are six analysts from theCUBE Research:
Before we get into it, below we’re showing some survey data from Enterprise Technology Research to demonstrate how much the industry has changed. ETR performs quarterly spending intentions surveys of more than 1,800 information technology decision makers. And we want to show you just how much of an impact the AI wave has had on spending intentions.
The graphic below shows spending by sector. The vertical axis is Net Score or spending momentum within a sector and the horizontal axis is Pervasion in the data set for each sector. It is a measure of account penetration for the sector. This data is based on account penetration, not revenue levels spent. Here we go back to January 2023.
Note the red line at 40% on the vertical axis. It indicates a highly elevated spending velocity and you can see ML/AI (which we’ve boxed) along with containers, cloud and robotics process automation, were on or above that red dotted line two years ago.
Now let’s take a look at how that has changed over the last 24 months.
It won’t shock you but look at both the trajectory of ML/AI over that time period and look at what happens to the other sectors. ML/AI shot to the top. Other sectors are somewhat compressed. This data underscores the transformation of the tech industry and specifically the spending priorities where ETR data tells us that roughly 44% of customers have been stealing from other budgets to fund their gen AI initiatives; and that the return on investment is – let’s say tepid.
Let’s get to the core of our episode today and turn our attention to the 2025 predictions. Below is a quick glance at all our predictions.
We’ll lead things off with some thoughts on DeepSeek. Then Bob Laliberte will follow with his predictions on networking. Scott will talk about the future of large language models, Savannah will share her predictions about the impact of consumer tech on the broader industry, Jackie will get to the heart of the security risks we’re facing and highlight some of the issues posed by recent policy changes from the Trump administration. Then Christophe will follow up with some predictions on the data protection and AI, then Paul will bring us home with a prediction about coding and developer impacts.
[Watch a clip of the DeepSeek prediction from Dave Vellante and Savannah Peterson]
Let’s start things off with the recent impact of DeepSeek.
The DeepSeek innovations, to the extent the information provided is accurate (and we think it largely is) will only serve to expand the market for AI. Value and volume are the two most important metrics here. In other words, the denominator of doing AI (i.e. cost) was just lowered, so value goes up. This will drive further adoption and will result in volume increases.
Winners include: Nvidia Corp., Broadcom Inc., Advanced Micro Devices Inc., infrastructure payers (for example Dell Technologies Inc. and Hewlett Packard Enterprise Co.), hyperscalers (because they get more return for their capital spending), and perhaps the biggest beneficiaries are software companies.
We think the DeepSeek trend is neutral for energy because they can flex capacity up or down as needed. And we see this as negative for closed-source LLMs, especially Anthropic PBC’s. IBM Corp.’s Granite gets a little from DeepSeek in our view and OpenAI, though potentially affected, is still a wait-and-see in our view. OpenAI is still the leader in innovation in AI and its volume is massive. As such, it may benefit from the heightened competition as a forcing function.
How will we measure the accuracy of this prediction in 2026? Watch Nvidia – the prediction is it will continue to thrive. Edge computing revenue takes off. AI projects get less expensive. Enterprise ROI becomes more attainable. Hyperscalers get more bang for the buck from their capital spending.
The following additional analysis from Savanah Peterson is relevant.
Competition in the AI market is intensifying as new players enter the fray with specialized inference solutions and innovative hardware designs. Though much of the early narrative focused on Nvidia’s dominance in training LLMs, it’s clear that real-time, edge-based inference will be critical to making AI “real” for businesses and consumers alike. DeepSeek’s emergence — despite concerns around privacy, energy consumption and total cost — highlights that multiple companies and silicon architectures will compete to deliver efficient, scalable inference for generative AI.
Bottom line:
We believe the spotlight on inference marks an important inflection point in AI adoption, pushing vendors and enterprises to explore diverse hardware and software stacks. Though Nvidia remains a key player, data suggests that the market will broaden, creating new opportunities for emerging chip designs and AI platforms. Ultimately, we expect customers to benefit from increased competition, with the focus shifting from raw model training power to holistic solutions that balance cost, performance and responsible data usage.
[Watch a clip of the networking prediction from Bob LaLiberte and Jackie McGuire]
Bob Laliberte’s prediction is shown below. Networking for AI gets a big boost in 2025.
The following analysis from Bob Laliberte summarizes the prediction with additional analysis contribured by Jackie McGuire.
We predict that networking will evolve from being viewed as basic “plumbing” to a strategic enabler of AI-driven initiatives in 2025. As organizations build AI data centers, move large volumes of data across wide-area networks, and deploy edge computing solutions, networking vendors and telecom providers will play a more critical — and more visible — role than ever before.
Bottom line:
We believe that AI’s rise will elevate networking to a mission-critical function, reshaping everything from the data center core to the edge. Ethernet is poised to gain traction in AI environments, and WAN solutions will become more specialized to handle the surge in data traffic. Meanwhile, the convergence of Wi-Fi and private 5G will enable more flexible, scalable edge deployments. Ultimately, success in AI will hinge on robust, high-performing networks that can seamlessly connect and protect all stages of the data lifecycle.
[Watch a clip of the AI and LLM prediction from Scott Hebner and Savannah Peterson]
Scott Hebner is up next with a prediction around LLMs. They alone won’t get the job done in 2025 – they need help.
The following provides context from Scott’s prediction with additional analysis from Savannah Peterson.
We predict that large language models alone will not be sufficient to generate meaningful enterprise ROI in 2025. Instead, organizations will incorporate a mix of AI techniques — beyond basic language models — to address critical issues such as accuracy, explainability and trust. Early experimentation with LLMs has revealed limitations in correlation-based models, driving enterprises to explore causal and symbolic methods to achieve higher-value outcomes.
Bottom line:
We believe enterprises will move beyond reliance on a single, large-scale LLM, integrating multiple AI methods to improve explainability, trust and real-world ROI. As the technology matures, the ability to combine generative, predictive, and causal models within unified architectures will prove essential for unlocking agentic AI’s true business value.
[Watch a clip of the consumer tech prediction from Savannah Peterson and Jackie McGuire]
Next up is Savannah Peterson, who is fresh off the CES consumer electronics show. Below we show Savannah’s intriguing prediction that we will finally move past the browser as our path to knowledge, replaced by new experiences driven from consumer innovations that will seep into enterprise computing.
The following summarizes Savannah’s prediction with additional analysis from Jackie McGuire.
We anticipate 2025 will mark a pivotal year in bringing AI directly to consumers through hardware — particularly in personal computing devices. While current AI interactions are largely confined to browser-based chatbots, emerging “AI PCs” and mobile devices equipped with on-device inference capabilities will shift the center of gravity from centralized data centers to the edge. This democratization of AI opens new possibilities for real-time decision-making and creation, enhancing privacy and expanding AI’s impact beyond traditional use cases.
Bottom line:
We believe 2025 will witness a breakthrough in “AI at the edge,” driven by AI-optimized PCs, phones and other consumer devices. This shift from cloud-dominated AI to local processing promises more immediate, human-centric experiences while opening up new avenues for privacy and innovation. As consumers embrace AI hardware, enterprises and developers will be forced to rethink user experience, data protection, and the very definition of “real-time” insights.
[Watch a clip of the cyber risk prediction from Jackie McGuire and Christophe Bertrand]
Jackie McGuire’s prediction zeroes right in on recent Trump administration policy changes and points out some potential unintended consequences of recent moves. The quest for efficiency could spell trouble. Jackie predicts that gutting some agencies and shifting certain authority to others are ill-advised and risk critical infrastructure and exposes other vulnerabilities.
The following analysis summarizes Jackie’s prediction with additional insight from Christophe Bertrand.
We predict that the U.S. government’s recent moves to restructure or eliminate certain cybersecurity agencies — such as the Cyber Safety Review Board — will create unintended consequences for national security and the private sector, particularly with regard to insurance and financial stability. By weakening public oversight and collaboration mechanisms, these policy shifts exacerbate the systemic risk of a large-scale cyberattack on critical infrastructure and financial institutions, potentially burdening insurers and prompting bailout scenarios similar to the 2008 financial crisis.
Bottom line:
We believe dismantling established cybersecurity oversight structures introduces real systemic risks, especially if insurers cannot absorb the shock of major, coordinated attacks. Without robust public-private collaboration, the U.S. could face a scenario where a cyber event cripples both critical infrastructure and the insurance market, forcing emergency government interventions. For 2025, organizations should intensify their own cyber preparedness strategies and closely monitor federal policy shifts that may affect national and economic security.
Christophe Bertrand is fresh off theCUBE Research’s Cyber Resiliency Summit held in our Palo Alto offices. Christophe learned a lot from the summit and below we show his prediction that AI will be the next battleground in data protection. Moreover, regulations will create more havoc for enterprises trying to keep up and that in itself creates challenges and risks.
The following summarizes Christophe’s prediction with additional analysis contributed by Paul Nashawaty.
We predict that AI workloads will become a major battleground for data protection in 2025, sparking a new wave of solutions from backup, storage, and cyber-resiliency vendors. As organizations ramp up AI initiatives, they face two critical, interlinked challenges: first, how to secure and recover fast-growing AI infrastructures; and second, how to comply with an expanding patchwork of data sovereignty and privacy regulations.
Bottom line:
We believe the surge in AI deployments will force the data protection ecosystem to evolve rapidly, prioritizing comprehensive backup and governance across increasingly complex infrastructure. While AI-driven automation promises new levels of resilience, the overlapping web of regulations will demand proactive compliance strategies. For enterprises, success will hinge on adopting data protection solutions that can scale with AI’s growth — while navigating a regulatory landscape that shows no signs of simplification.
[Watch a clip of the App/Dev and coder predictions from Paul Nashawaty and Savannah Peterson]
The last prediction comes from Paul Nashawaty. As we show below, Paul is predicting that companies are going to try to replace portions of their developer workforce with AI but it might not be so straightforward. He further predicts that at least one organization is going to try to cut half its developers and replace them with AI systems but might not be so successful.
The following summarizes Paul’s prediction with additional analysis from Savannah Peterson.
We predict that despite growing interest in substituting AI for human developers, attempts to dramatically reduce developer headcount — by as much as half — will fall short in 2025. While AI-driven tooling can streamline repetitive or mundane coding tasks, organizations still need skilled developers for complex problem-solving, innovation and collaboration. Simultaneously, the development ecosystem is shifting toward integrated platforms that reduce operational overhead and improve developer experience, with low-code/no-code solutions empowering “citizen developers” — all while governance, security and compliance requirements mount.
Bottom line:
We believe that though AI will play a growing role in accelerating and enhancing development processes, it is unlikely to replace large segments of the developer workforce in the near term. Instead, AI-driven tools will free developers to innovate and solve bigger problems, while low-code/no-code solutions expand the pool of participants in software creation. Organizations that consolidate tooling, invest in platform-based approaches, and maintain strict compliance controls will be better positioned to deliver software faster and more securely in 2025.
Thanks to the six analysts from theCUBE Research who crafted these predictions. We’d love to hear your thoughts. As always, reach out any time and share your data and opinions.
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