Media vendors divided on what an AI market correction would mean for broadcast workflows

The question of whether artificial intelligence investment has reached bubble territory is not new. But inside the broadcast and media industry, the answers vary enough to suggest the industry has not settled on what the risk actually is – or where it would land if valuations reset.

Across our recent Industry Insights roundtable on AI and media workflows, contributors were asked directly whether AI represents a speculative bubble and what a market correction would mean for workflows built on platforms like OpenAI. The responses differed not just in their conclusions but also in how each contributor defined the problem.

Bubble in what sense?

“The AI bubble refers to the market valuation of public and private companies that are creating AI,” said Charlie Dunn, executive vice president, products, Telestream. “We believe that there is certainly the possibility for prices to get reset when the companies making the large investments have to start showing returns that match the speculation. We don’t believe that a reset will lead to an overall collapse of the use of AI based on the impact of the technology.”

That framing – valuation risk distinct from technology risk – was echoed by other contributors to the roundtable. The concern is not that AI stops working. It is possible that the companies currently providing it may be priced beyond what their near-term returns can support.

“Speculative bubbles are a feature not a bug of new technological paradigms, this phenomenon is pretty well studied. Past technological driven bubbles always had an overcapacity building phase. So yes, it might be considered a bubble but that doesn’t mean it’s useless or that it will never deliver the expected value,” said Miguel Coutinho, head, NDI.

Clara Aler, head of marketing, Knox Media Hub, pointed to an earlier parallel. The current moment, she said, was already being compared to the early web era and the dot-com bubble: the technology persists even as the landscape reshapes around it.

Her expectation was that within a few years, only a small percentage of current AI vendors would remain relevant or independent.

Dependency as the real risk

The more immediate concern is not whether a bubble exists but what media organizations have built on top of platforms they do not control.

Advertisement

“AI itself is not the bubble; dependency on monolithic, opaque providers is the risk,” said Aitor Falcó, sales manager, Knox Media Hub. “Media organizations mitigate this by abstracting AI services behind a modular API-first orchestration layer, allowing models or vendors to be swapped without redesigning workflows. When AI is treated as a replaceable service rather than a core system, operational resilience is maintained even if individual providers may change or fail.”

The architectural argument here is practical: organizations that have embedded a specific platform deeply into their production infrastructure face greater disruption if that platform changes pricing, restricts access or exits the market than those that have built abstraction layers between their workflows and the underlying models.

That distinction carries weight in broadcast environments where workflow stability is an operational requirement. A MAM or automation system that depends on a single AI provider for core functions faces a different risk profile than one designed to swap providers at the orchestration layer.

Adoption as a counterargument

“From a personal delivery experience, working with customers, working with teams, and working with the technology itself, we’re only just scratching the surface on what AI can do,” said Michael Chan, VP, delivery operations in corporate, Accedo. “I think adoption is the leading indicator, and you will see an increasing focus on AI transformation this year.”

The bubble framing may not always be the most useful lens.

Chan’s position reflected a view held by several contributors: that growing operational adoption across transcription, metadata, localization and production workflows represents a different kind of signal than market valuation, and a more reliable one for assessing long-term durability.

Derek Barrilleaux, CEO, Projective, was the most direct in saying a bubble exists, but shifted the focus to how organizations are responding to it.

“I’m utterly convinced that there is a bubble, but bubbles burst in unexpected ways. These new technologies should be seen as an augmentation of existing capabilities, not a replacement for staff. Trimming staff to save short-term costs only increases your long-term risk,” Barrilleaux said.

The point connects the valuation debate to decisions already being made inside media organizations.

If AI investment contracts and the promised efficiency gains do not materialize at the scale used to justify workforce reductions, organizations that cut headcount ahead of proven returns face a compounded problem – fewer people to manage workflows that may suddenly require more human intervention.

No contributor in the roundtable predicted an outright collapse of AI in broadcast and media operations. The underlying technology, as several noted, is unlikely to disappear even if the companies building on top of it consolidate or fail.

The more immediate danger is narrower but no less disruptive: the connective glue holding production workflows together (the APIs, orchestration layers and third-party model integrations that have been quietly woven into MAM systems, automation platforms and editorial tools) breaking or requiring rapid replacement..

Advertisement

The real disagreement among contributors was over how much of the current investment is sustainable, how concentrated the dependency risk has become and whether the industry has built its AI infrastructure on ground stable enough to hold if the market shifts.

For broadcast organizations running live workflows, those questions are not abstract.