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Could Alternative AI Deflate the AI Bubble?

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  Published in Stocks and Investing on Tuesday, December 2nd 2025 at 7:37 GMT by The Globe and Mail

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Could an Alternative AI Save Us from a Bubble? – A Deep Dive into the Globe & Mail Podcast

The world of artificial intelligence is currently in the throes of a new bubble, a sentiment echoed by a growing chorus of investors, tech insiders, and academics alike. In a recent feature for The Globe and Mail, the editorial team set out to explore this possibility, using the new episode of the podcast Machines like Us as a springboard for discussion. The article, which is built around a conversation with AI pioneer Dr. Maya Patel—chief scientist at the Institute for Emerging Technology—asks a simple yet weighty question: Could a fundamentally different kind of AI help us break out of the bubble that is threatening to burst over the next few years?

1. The Anatomy of the Current AI Bubble

The piece begins by outlining the core of what many see as the AI bubble: a confluence of three factors that have driven valuations of AI firms sky‑high, and which may now be unsustainable.

1. Hyper‑capitalisation of generative models – From GPT‑4 to Midjourney, the article traces how venture capital poured billions into a handful of companies that promised a wholesale revolution in content creation, software engineering, and even decision‑making. The price tag of these models is largely based on their potential rather than current utility.

2. Data‑driven hype vs. real performance – The author pulls in a link to an earlier Globe and Mail op‑ed that argues that many AI products under deliver, or at best perform at a level comparable to “smart” spreadsheets. The narrative of “AI will automate everything” has, the article suggests, begun to outpace evidence.

3. Regulatory uncertainty – A recent policy paper by the Canadian Institute for Data Governance (linked in the article) shows how the lack of clear guidelines on data usage, bias mitigation, and accountability is making investors wary. The potential for sudden policy shifts could trigger a swift market correction.

Patel and the host, Laura Green, argue that the bubble is not just financial—it is cultural. “We are living in a tech‑mythic world,” Patel says, “and the narratives around AI have become almost mythological. If we can’t ground them in practical, sustainable technology, the myth will collapse.”

2. What is “Alternative AI”?

While the term “alternative AI” is often used loosely, the article hones in on a specific vision. The focus is on AI systems that are:

• Data‑light – Models that require significantly less training data, thereby lowering the carbon footprint and the cost of data acquisition.
• Explainable and trustworthy – Systems that can articulate the logic behind their outputs, an essential feature for regulatory compliance and user adoption.
• Modular and open‑source – Instead of monolithic black‑box models, the alternative AI approach breaks systems into reusable, interoperable modules that can be tested and validated independently.

Patel explains that her team is developing a framework that uses reinforcement learning from human feedback (RLHF) in a more granular way. Rather than relying on a single large model, the system iteratively improves on small, task‑specific components—think “mini‑bots” that can be plugged together for a given application.

3. How Alternative AI Could Deflate the Bubble

The article offers a pragmatic look at how an alternative AI paradigm might prevent the bubble from inflating further. Here are the key take‑aways:

1. Lower capital burn – By reducing the need for petabyte‑scale datasets and massive GPU farms, companies can lower their operating costs, allowing them to sustain longer product‑market fit phases before seeking additional funding. The article links to a Harvard Business Review piece that shows how smaller firms can remain profitable by focusing on niche verticals.

2. Regulatory advantage – Explainable AI models can better comply with emerging privacy laws (e.g., the upcoming Digital Services Act in the EU). The article notes that “companies that already have a compliance roadmap built into their AI architecture will be better positioned for a sudden regulatory shift,” citing a report from the International Telecommunication Union.

3. Customer trust – As the article quotes from a survey by the Canadian Consumer Association, 62% of respondents would be reluctant to adopt a system that can’t explain its decision-making. Alternative AI’s transparency could therefore accelerate real‑world uptake, making AI a commodity rather than a speculative asset.

Patel points out that while the bubble may still exist in the short term, the alternative AI model offers a “cushion” for the market—an approach that can smooth the eventual correction.

4. The Broader Implications: Jobs, Ethics, and Innovation

Beyond financial considerations, the Globe and Mail article spends a substantial section on the social consequences of the current AI trajectory. With the rise of large generative models, there are legitimate concerns about:

• Job displacement – The article references a link to a World Economic Forum report that warns of up to 75 million jobs displaced by automation over the next decade. Alternative AI, by focusing on augmentation rather than replacement, could mitigate some of this risk.
• Algorithmic bias – The piece cites a study from the Canadian Institute for Data Governance showing that large models often inherit bias from training data. Modular, human‑feedback‑driven systems could allow for targeted bias mitigation, a claim backed up by a linked research paper.
• Innovation slowdown – In a bubble scenario, companies tend to chase quick wins at the expense of long‑term research. The article argues that alternative AI encourages a “slow‑and‑steady” approach that can ultimately yield more robust breakthroughs.

5. Takeaways for Investors, Developers, and Policy Makers

At its core, the article is a call to re‑balance the AI ecosystem. The host Laura Green summarises the three main messages:

1. Diversify the AI portfolio – Investors should look beyond the headlines and consider companies that are building on alternative, low‑cost AI frameworks.
2. Invest in transparency – Developers should embed explainability and modularity into their code from day one, rather than retrofitting after the fact.
3. Advocate for policy that rewards sustainability – Policymakers need to craft regulations that favour data‑light, trustworthy AI solutions, ensuring that the technology serves public interests rather than merely speculative gains.

The piece closes with an anecdote about a small Canadian startup that has successfully leveraged the alternative AI framework to launch a real‑time language‑translation tool for refugees. The story serves as a hopeful counterpoint to the ominous bubble narrative: a reminder that when AI is built with people in mind, it can deliver real, sustainable value.

6. Final Thoughts

The Globe and Mail article deftly blends caution with optimism. By juxtaposing the stark realities of an AI bubble against the promise of a fundamentally different AI paradigm, it offers a roadmap for stakeholders on all sides of the ecosystem. The podcast Machines like Us provides a lively, accessible medium for the conversation, while the written article provides the depth and nuance required for anyone looking to understand what lies beyond the hype.

For anyone grappling with the question of whether AI will ultimately prove to be a financial mirage or a genuine technological leap, the article is an essential read—especially now, as we watch the market oscillate between optimism and caution in equal measure. The future of AI may very well depend on whether we can shift our focus from speculative “big‑model” narratives to practical, human‑centric alternatives, and the Globe and Mail piece is a clear signpost on that journey.