The loudest AI conversation right now is about agents, and it is a distraction from a harder one. Frontier models are becoming expensive to run at scale, and very few leaders have worked out where their own line sits between renting that capability and owning it.
The question nobody is costing
Ask most leaders how they will keep AI from quietly draining the company as usage grows, and there is no answer, because the conversation has been about capability, not cost. As demand moves from occasional to constant, running everything on a third-party frontier model can become the single largest line nobody planned for. This is the waste I pointed at in Gartner’s trillion-dollar IT spend forecast, most of it wasted, and the reflex to simply cut the budget, which I argued against in AI Budgets Are Being Cut. This Is the Moment to Move, is the wrong response to a real problem.
There is a crossover point, and most companies have not found theirs
Let me be precise, because this is where the argument is usually made badly. It is not true that every company should stop using the frontier labs and build a data centre. That advice gets dismantled by the first competent CTO, and rightly. Renting frontier models is cheap and correct at low or spiky volume. Owning the platform wins at heavy, steady, around-the-clock inference, running many models in parallel where the company profits from every second of use.
So the real question is a crossover calculation: at what volume does owning become cheaper than renting for us? Most companies have never done that maths, which is why the decision is driven by fashion in both directions, either overspending at the frontier or refusing to look at ownership at all.
The honest cost of owning it
If you do cross that line, be honest about the full bill, not the flattering version. It is not only hardware, electricity and cooling. The GPUs cost real money and depreciate fast. The talent to run the platform is scarce and expensive. There is security to manage, and there are model updates to keep pace with. And self-hosted open models may still fall short of the frontier on the hardest tasks, though they are now good enough for a large and growing share of what a business actually runs day to day. This is the open-source pressure I wrote about in AI Bubble: Navigating the Future of AI.
I am not arguing this from a slide. As my own demand has grown to a genuine around-the-clock load, I have started pricing hardware to run open models locally, and even against that full cost, at my volume it still comes out ahead. That is the test each leader has to run for themselves, not assume.
Operational maturity is the real dividing line
Here is the part that ties everything together. The companies that will handle the cost wall well are the same ones that will be calm about regulation and steady under pressure. It is not the cleverness of the AI that separates them. It is operational maturity: the discipline to do the boring maths, to plan the infrastructure, to know their own numbers. That single trait decides who thrives and who is quietly overrun, more than any amount of spending without direction, which is the failure I described in You Didn’t Lead Your AI Project. You Just Bought Shovels.
Agents are the conversation everyone is having. The cost wall is the one that will decide who can still afford to run the company in a few years.
If you are leading your organisation through this, I work with a limited number of senior leaders each quarter. Get in touch at Anglero.com.
Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant.
Frequently asked questions
Should companies stop using frontier AI models and self-host instead?
Not as a rule. Renting frontier models is cheap and correct at low or spiky volume. Owning the platform only wins at heavy, steady, around-the-clock use. The right answer depends on where your crossover point sits.
What is the crossover point for AI infrastructure?
The volume at which owning and running your own models becomes cheaper than renting them from a frontier lab. Most companies have never calculated theirs, so the decision gets driven by fashion in both directions.
What are the hidden costs of self-hosting AI?
Beyond hardware, electricity and cooling, there is fast GPU depreciation, scarce and expensive talent, security, and constant model updates. Self-hosted open models may also fall short of the frontier on the hardest tasks.
What actually separates companies that handle AI cost well?
Operational maturity. The discipline to do the boring maths, plan the infrastructure and know their own numbers is what decides who thrives, and it is the same trait that keeps a company calm about regulation.
Gartner now expects worldwide IT spending to reach $6.31 trillion in 2026, a rise of 13.5% on 2025, raised from $6.15 trillion earlier in the year. Almost all of the new money is going into AI infrastructure.
If there is an AI line in your budget this year, you are part of that $6.31 trillion. Here is the uncomfortable part. Most of it buys infrastructure that changes nothing about how your business actually works, and the leaders who confuse the two will spend a fortune and have nothing to show their people for it.
A forecast that keeps climbing
When I wrote about Gartner’s outlook in late 2024, the open question was whether enterprises would pull back. Through 2025 many did, in what Gartner described as an uncertainty pause, a deliberate hold on net-new initiatives. Spending rose anyway, and the 2026 number has since been revised upward twice. The growth is concentrated where the AI build-out is loudest: data centre systems, where Gartner expects spending to grow by more than 55% as hyperscalers race to stand up AI-optimised compute, and software and services, where generative AI is quietly lifting the cost of tools companies already run. On Gartner’s figures, IT services alone will pass $1.87 trillion and software will approach $1.44 trillion.
Spending is up while expectations have come down
The detail most leaders skip past is the split Gartner keeps naming. Expectations for generative AI have slid into what it calls the trough of disillusionment, even as spending on it rises. The hard numbers explain the mood: MIT’s 2025 report, The GenAI Divide: State of AI in Business, found that about 95% of enterprise generative AI pilots had delivered no measurable return. Read that again. Organisations are putting more money into AI in the same year they have quietly lowered what they expect it to do. That is not a contradiction. It is exactly what a real technology shift looks like from the inside: after the launch excitement, before the compounding returns.
Buying infrastructure is not the same as getting value
The overwhelming share of this $6.31 trillion is infrastructure: servers, data centres, memory, the plumbing of AI. I have led enterprise transformation from inside this market, at IBM and as Cognizant’s Nordic CTO and Innovation Officer, and the pattern never changes. The organisations that see a return are not the ones that bought the most technology. They are the ones that did the human work alongside it. AI is a culture project before it is a procurement line. The hardware arrives in weeks; the change in how people decide, trust and work takes leadership, and that is the line item that never shows up in Gartner’s table.
What this means for you
If you sit on a board or lead a team, three things follow from this forecast. First, separate the infrastructure decision from the value decision. Knowing the market will spend more than $6 trillion tells you nothing about what your people should do on Monday morning. Second, treat lowered AI expectations as the buying signal, not the warning. The noise is leaving and the real work is starting, and this is the window in which moving first compounds. Third, budget for the human side as deliberately as the technical side, because that is where the return on all this spending is won or lost.
The number will keep rising, and the next wave, the move to embodied AI and robotics, will raise the stakes again. The leaders who benefit will be the ones who invest in capability and culture, not only in compute. If the human side is the part you are wrestling with, that is the conversation I have with leadership teams, and it is worth starting before the budget is spent, not after.
Frequently asked questions
How much will global IT spending reach in 2026?
Gartner expects worldwide IT spending to reach $6.31 trillion in 2026, up 13.5% on 2025, and the figure has been revised upward twice. Almost all of the new money is going into AI infrastructure, with data centre systems alone forecast to grow by more than 55%.
If spending is rising, why does Gartner say expectations have fallen?
Because the two move independently. Expectations for generative AI have slipped into what Gartner calls the trough of disillusionment even as spending climbs. MIT’s 2025 report found about 95% of enterprise generative AI pilots delivered no measurable return, which explains the mood, and it is exactly what a real technology shift looks like after the launch excitement and before the compounding returns.
Why will most of the $6.31 trillion be wasted?
Because the overwhelming share buys infrastructure, servers, data centres, memory, that changes nothing about how a business actually works. The organisations that see a return are not the ones that bought the most technology, but the ones that did the human work alongside it. AI is a culture project before it is a procurement line.
What should a leader actually do in response to this forecast?
Three things: separate the infrastructure decision from the value decision, treat lowered AI expectations as the buying signal rather than the warning, and budget for the human side as deliberately as the technical side. That human line item never appears in Gartner’s table, but it is where the return is won or lost.
Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant.
The AI industry shows classic bubble signals: overvalued startups, hype outpacing capability and little differentiation between products. Open-source models are the most likely trigger for a correction, because they erode the advantage of proprietary systems. AI itself is not a bubble; the way it is currently valued and monetised is.
In recent years, the tech world has been abuzz with excitement over artificial intelligence (AI), with companies large and small rushing to integrate AI into their products and services.
However, as with any rapidly growing technology sector, questions about sustainability and long-term viability inevitably arise. Is the current AI boom sustainable, or are we witnessing an AI bubble that’s bound to burst? I shared my opinion in this recent interview.
The Current AI Landscape
The AI industry has seen unprecedented growth, with billions of dollars pouring into startups and established tech giants alike. Large Language Models (LLMs) like GPT-3 and BERT have captured the public imagination, demonstrating capabilities that seemed like science fiction just a few years ago. This has led to a gold rush mentality, with investors and companies scrambling to stake their claim in the AI frontier.
Signs of an Impending Bubble
Despite the enthusiasm, there are several indicators that suggest we might be heading towards an AI bubble:
- Overvaluation of AI Companies: Many AI startups are receiving astronomical valuations based on potential rather than proven results or sustainable business models.
- Hype Outpacing Reality: While AI has made significant strides, the gap between public expectations and current capabilities remains substantial.
- Lack of Differentiation: As AI tools become more commonplace, many companies struggle to differentiate their offerings in a crowded market.
- Regulatory Uncertainties: Increasing scrutiny from regulators regarding AI ethics, bias, and data privacy could impact the industry’s growth trajectory.
The Open-Source Revolution
One of the most significant factors that could contribute to the bursting of the AI bubble is the rise of open-source LLMs. Companies like Meta (formerly Facebook) are leading the charge in this area, releasing powerful models to the public domain. This trend has several implications:
- Democratisation of AI: Open-source models make advanced AI capabilities accessible to a wider range of developers and organisations, potentially levelling the playing field.
- Reduced Barriers to Entry: As powerful AI tools become freely available, the competitive advantage of proprietary models may diminish.
- Acceleration of Innovation: Open collaboration could lead to faster advancements in AI technology, potentially outpacing closed, proprietary development.
The OpenAI Pivot: A Sign of the Times?
OpenAI’s recent shift from a non-profit to a for-profit model and discussions about a potential IPO can be seen as a strategic response to the changing landscape. This move suggests that even leading AI companies are feeling the pressure to capitalise on their current market position before open-source alternatives gain more ground.
The race to monetise may indicate a recognition that the window of opportunity for proprietary AI models could be closing. As open-source alternatives improve, the unique value proposition of companies like OpenAI may diminish, unless they can continually stay ahead of the curve.
The Future of AI: Open Source Dominance?
While it’s too early to definitively predict the future of the AI industry, the trend towards open-source solutions is undeniable. This shift could have several long-term effects:
- Commoditisation of Basic AI Capabilities: As open-source models improve, basic AI functionalities may become commoditised, forcing companies to find new ways to add value.
- Focus on Specialised Applications: To remain competitive, AI companies may need to focus on developing specialised, industry-specific solutions rather than general-purpose AI.
- Emphasis on Data and Implementation: With the algorithms becoming more accessible, the true value may lie in data quality and effective implementation rather than the AI models themselves.
- Collaborative Ecosystem: An open-source dominated landscape could foster a more collaborative AI ecosystem, potentially accelerating overall progress in the field.
Navigating the AI Bubble
For businesses and investors looking to navigate the potential AI bubble, consider the following strategies:
- Focus on Sustainable Value: Prioritise AI applications that solve real-world problems and deliver measurable value.
- Embrace Open Source: Consider how open-source AI tools can be leveraged to create unique solutions without reinventing the wheel.
- Invest in Data and Expertise: High-quality data and AI implementation expertise will likely remain valuable even if basic AI capabilities become commoditised.
- Stay Agile: Be prepared to pivot strategies as the AI landscape evolves, keeping an eye on emerging trends and technologies.
Conclusion
While the AI industry is undoubtedly experiencing a period of hype and potentially unsustainable growth, it’s important to recognise that AI itself is not a bubble.
The technology will continue to play a crucial role in shaping our future. However, the way we develop, deploy, and monetise AI is likely to undergo significant changes.
The rise of open-source AI models may indeed lead to a recalibration of the industry, potentially bursting the bubble of overvalued proprietary AI companies.
However, this shift could also usher in a new era of innovation and accessibility in AI technology.
As we move forward, it will be crucial for businesses, investors, and technologists to stay informed and adaptable.
The AI bubble may burst, but from its aftermath, a more sustainable and impactful AI ecosystem is likely to emerge.
What are your thoughts on the future of AI? Do you see open-source models as the key to long-term progress in the field?
Frequently asked questions
Is AI a bubble that is about to burst?
AI itself is not a bubble, but the way it is currently valued and monetised is. The industry shows classic bubble signals, overvalued startups, hype outpacing capability and little differentiation between products, so a correction is plausible even though the technology remains genuinely important.
What is most likely to trigger a correction in the AI market?
The rise of open-source large language models. As powerful models become freely available, the competitive advantage of proprietary systems erodes, basic AI capabilities commoditise, and the premium valuations attached to closed models become harder to justify.
If AI capabilities become commoditised, where does the value go?
Into data quality and implementation. Once the algorithms are widely accessible, the durable advantage lies less in the models themselves and more in proprietary data, specialised industry-specific applications, and the expertise to deploy AI effectively.
How should a business or investor navigate a possible AI bubble?
Focus on AI applications that solve real problems and deliver measurable value, leverage open-source tools rather than reinventing them, invest in data quality and implementation expertise, and stay agile enough to pivot as the landscape shifts.
Work with Thomas as a Strategic AI Advisor
Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organisations including IBM, the WHO, the World Government Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant.