Companies Are Drowning in Too Much AI
IT sellers are rolling out an avalanche of new generative AI features, leaving CIOs overwhelmed and workers confused
3 min
Businesses are facing an influx of new artificial-intelligence tools, many of which overlap and cause confusion for employees, as corporate-technology sellers race to capitalise on the generative AI trend.
“Since the ChatGPT excitement, I must have had at least 20 to 25 vendors in my portfolio reach out to me saying, ‘Hey, let us tell you about our generative AI co-pilot strategy,’” said Milind Wagle, chief information officer at Equinix, one of the world’s biggest data-centre landlords.
Generative AI features, which can respond to user prompts by generating images or text, often come in the forms of co-pilots, or virtual assistants that work in tandem with an IT seller’s offerings, sometimes automating certain tasks within that platform. Wagle said Equinix is drowning in a flood of co-pilots—and he is trying to figure out how, if at all, they should coexist.
“I feel like there’s a co-pilot war that needs to sort of happen,” he said, adding that to humanize AI in a meaningful way, there needs to be more coherence in how these tools are deployed.
The co-pilot proliferation is leading to confusion for employees who are looking for a single common interface to accomplish certain tasks, Wagle said. It can also create potential governance risks if there is a possibility that private data from a company that interacts with the co-pilots could make its way into public training models for generative AI tools.
Gartner analyst Arun Chandrasekaran said IT sellers are feeling pressure to move into the generative AI space or risk falling behind, meaning some half-baked features will be rushed out without the proper privacy and security guardrails in place. Established IT sellers also need to consider security concerns, including whether a customer’s data can be fed back to train the model, because this is uncharted territory, he added.
Chandrasekaran estimates that a fifth of independent software vendors have stepped into the generative AI space since ChatGPT was launched about seven months ago—a huge amount of growth in a short time, he said.
“I don’t think I’ve had a partner or vendor meeting this year where I wasn’t pitched a generative AI play,” said Brian Woodring, CIO of Rocket Mortgage, a nonbank mortgage provider.
Sometimes the co-pilots appear in system updates as a freebie, and sometimes they cost extra, Woodring said. He added that in some cases, the generative AI features are tacked on, despite not being compelling additions or the best tool for the job.
“Everyone’s trying to fit it in everywhere,” he said, adding, “It’s not something you can just spread around like peanut butter. It’s not a coat of paint you put on your product afterwards and say, now it’s AI.”
In other instances, the features are things that Rocket Mortgage could confidently and more cheaply build in house, Woodring said. For example, a number of tools on the market pull and analyse data from phone calls, a feature that Rocket Mortgage was able to build itself, he said.
Tech executives said they are looking critically at new generative AI tools to distinguish between the truly compelling ones and the ones that are just paying lip service to the hype. How well the tools will be able to integrate with each other is another consideration.
“We want clarity on how we can connect every single one,” said Noé Angel, CIO at agriculture company NatureSweet. When tools are too fragmented, it ends up creating more work for those who have to manage them, he said.
Jim Stratton, chief technology officer of Workday, a provider of enterprise cloud applications for finance and human resources, said that longer term, he expects consolidation and clearer winners to emerge when it comes to certain AI capabilities, which could simplify things for companies.
But nearer term, navigating the complexity of the landscape remains a challenge. “There’s still a lot of noise at the moment,” he said.
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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