Artificial intelligence’s gold rush moment: when innovation meets inflation

Introduction

The current buzz around artificial intelligence (AI) feels a lot like déjà vu, and it’s eerily similar to the dot-com bubble back in the late ’90s. Just like then, there’s money pouring into startups claiming they’ll revolutionize everything from how we work to how we create, generating both enthusiasm and fear. However, these promises rest more on big dreams than solid proof. In what follows, I’ll dig into why this moment might be another case of the industry getting ahead of itself, excited about technology that’s incredibly promising, but perhaps being oversold once again. This may be a moment to approach these developments with caution. For clarity, the opinion piece is structured into short subsections, each addressing a distinct conceptual aspect of the current generative AI landscape, including technical capability, economic constraints, and real-world deployment. In this context, terms such as ‘understanding’ and ‘cognition’ are used in an operational sense, referring to the ability of a system to generalize, reason across contexts, and maintain internally consistent representations, rather than implying human-like awareness or intent.

Websites to ‘AI strategies’

Back around the year 2000, companies scrambled to build websites because it seemed like everyone needed one and had to have one. Today, that same rush is happening again, but this time it is the race of governments and businesses to roll out ‘AI strategies’ and pin their futures on large generative models (LGMs). The message put out there is loud and clear: embrace AI or get left behind and risk falling behind. The parallels with the dot-com era are striking, from sky-high valuations to massive spending on infrastructure, all wrapped up in the belief that technology alone will drive unstoppable growth. However, what people tend to forget is that what came after the first internet boom wasn’t the end of the world, but a brutal, sometimes painful reality check. And right now, I’m seeing many of those same warning signs bubbling up again. At the same time, there are important counterpoints. Unlike earlier technological cycles, current generative AI systems have demonstrated measurable utility in domains such as software development and computational biology. In addition, adoption in many sectors is constrained not only by technological limitations but also by structural factors, including limited digital infrastructure, regulatory requirements, and cultural resistance within organizations. These factors may delay or obscure the realization of value, independent of the underlying capability of technology.

The power problem

Data centers in general already use a big chunk of the world’s electricity: about 415 terawatt-hours in 2024. And absent of any major policy or efficiency shifts, the International Energy Agency predicts this could even more than double to around 945 terawatt-hours by 2030 because of growing AI demand (1). Just to put it in perspective: a typical AI data center that uses about 30 megawatts (MW) of IT power, a realistic size for many modern AI setups, actually draws about 36 MW total when you factor in extra cooling and support systems. Over a year, that roughly adds up to 315 gigawatt-hours of electricity. At typical industrial electricity prices (around 8.13 cents per kilowatt-hour in USA), the electricity bill alone could be ~$25.6 million every year. Therefore, for companies running AI training and inference constantly, this is a substantial ongoing expense and burden (2).

And that’s merely the electricity. Building the infrastructure (i.e., the data center itself) is also costly. Industry estimates say it costs about $10 to $15 million per megawatt of information technology (IT) power capacity just to build the facility. This would mean that a 30 MW data center could cost between $300 and $400 million before even buying the servers and AI hardware (e.g., accelerators). Due to these massive costs, the biggest AI compute setups nowadays tend to be owned by companies with deep pockets and long-term, low-cost power deals (3).

Free tools, all take, no pay

While many users and organizations do pay for access to these systems, the broader question remains whether the high computational and infrastructure costs can be sustainably justified across different domains and use cases. Thus, those big energy and infrastructure bills fall on just a handful of the former large companies. However, it’s not just electricity and capex that add up. There are a lot of other hidden expenses that make running these AI data centers expensive and complicated. These expenses include hiring specialized engineers and operations staff, dealing with long wait times and extra costs for high-density equipment like racks and cooling systems, paying fees for upgrading power lines and grid connections, and handling legal and compliance work around data and licensing. All these costs push the minimum size and investment needed to make a project efficient, which benefits big players who can buy power ahead of time, strike deals on power transmission, or set up shop in cheaper locations. Ultimately, moving towards a ‘oligopoly’ of these new technologies.

Industry experts say modern, large-scale data center projects often run into hundreds of millions of dollars, with some big U.S. sites costing more than $200 million just to build (3). The hardware itself makes this even trickier. Powerful AI training chips, like NVIDIA’s latest H100 graphics processing units (GPUs), can use up to 700 watts each under load. So, racks full of these chips need very strong power supplies, backup batteries, and big cooling systems to keep everything running smoothly. When thousands of these chips are running together, the aggregate power demand and infrastructure costs become very large (4).

Limits of large language models (LLMs)

Another important factor that should be taken into account is that although LLMs are amazingly fluent at producing plausible text, fluency is not understanding (5-7). This is well aside from their substantial training costs (8). Earlier models such as Long Short-Term Memory (LSTM) network were capable of capturing longer-range dependencies compared to simpler architectures, but were still limited in effective context and lacked the global attention mechanisms introduced by Transformer-based models. An additional limitation relates to epistemic differences between human and model-based reasoning. Recent work suggests that LLMs and humans arrive at judgments through fundamentally different processes, with models relying on statistical pattern approximation rather than grounded reasoning. This distinction may have implications for reliability, particularly in high-stakes domains such as medicine, where the ability to justify and interpret decisions is essential.

Now, LLMs can read the whole context at once, making them extremely powerful and game changing. They do this by predicting word sequences from vast corpora. However, what most people miss is that they do not yet demonstrate sustained memory, independent intent, or robust conceptual invention. That gap explains why these systems hallucinate facts, display brittle reasoning, and reproduce biases embedded in their training data. Hallucination is a known failure mode of current generative models, in which outputs may be fluent but not grounded in verifiable information. It does not represent the primary mechanism of these systems, but rather a limitation that becomes more pronounced in certain contexts. Scaling them generates more polished phrasing, however it does not, by itself, create deeper cognition. In short, they lack creativity.

This is becoming an emerging problem as the newest models have scooped up much of the available data online (9). They sometimes make stuff up (hallucinate facts), struggle with complex reasoning, and, to make things worse, they might repeat biases found in their training data. In other words, simply expanding the size of the models doesn’t lead to deeper cognition but only helps them sound nicer and more polished, not understanding or deeper thinking. On a separate note, even now we still don’t really know what ‘understanding’ really means. There is no consensus. Some researchers think models like Generative Pre-trained Transformer (GPT) just imitate it, matching patterns without truly grasping anything. Others believe that may be, at some point, this kind of pattern-finding starts to be understanding. It’s a question we haven’t settled yet.

Artificial general intelligence or artificial generative consensus

The industry’s shorthand, AGI, or Artificial General Intelligence, is seductive but often misleading. A more accurate term for today’s AI systems, which I’d like to coin, is Artificial Consensus Synthetic Synthesis (ACSS). These are statistical machines/engines that compress, synthesize, and re-express the full spectrum of human-generated content: from expert writing to casual media, creating polished outputs that reflect human knowledge but lack genuine creativity or independent insight. As mentioned previously, ACSS can amplify human ideas and automate many routine tasks, but it’s important to remember that persuasive AI output is not the same as original discovery or true understanding.

Quantum computing or wishful thinking

Some proponents believe quantum computing will ultimately solve today’s energy and scaling problems inherent with these emerging electricity hungry technologies, and make massive computing power widely available, in other words, democratize compute. Although real progress has been made, with experiments and roadmaps in place, there are still substantial technical hurdles and challenges to overcome. Things like quantum error correction, fault tolerance, material science, cryogenics (ultra-cold environments), and complex system engineering all create major obstacles for scaling up. Experts estimate that truly practical, general-purpose quantum computers that can reliably work on a large scale are still years or even decades away (10). Therefore, thinking of quantum computing as an immediate fix risks falling into the same hype cycle, again offering a hopeful future solution instead of facing today’s economic, energy, and regulatory challenges head-on.

Prediction horizon and error bounds

Paul Samuelson joked that economists have predicted nine of the last five recessions. We could potentially argue the same about generative AI, everyone’s sure it’ll either save the world or destroy it, but nobody really knows which. It is really tricky. In the non-expert AI community, such as podcasts and social media, many people confidently predict where AI will be in 10 or 20 years, often assuming that progress will keep speeding up like it did with Moore’s Law. But Moore’s Law is slowing down, and hardware improvements are hitting limits. Also bear in mind that algorithm advances don’t happen in a steady, predictable way either. Technically speaking, from any formal forecasting perspective, uncertainty itself compounds with horizon: error bounds widen nonlinearly, so what looks plausible five years out becomes highly speculative two decades out. In other words, it all could be pure guesswork two decades ahead. However, this does not mean we shouldn’t plan, but it’s a reminder for all of us to remain realistic and avoid hubris: policymakers and investors should prepare for a wide range of possible futures instead of betting everything on a single optimistic scenario. Even evolutionarily, the latter strategy wouldn’t make sense.

Regulation and survivors

A collateral of this rapid AI surge is the flood of regulatory pressure hitting the industry at the same time. Copyright lawsuits, scrutiny over data use, and new AI laws are driving up compliance costs and making it harder to assemble training datasets. These hurdles will again favor companies that can clearly demonstrate safety, accountability, and solid economics. If a market correction eventually happens, history tells us cycles of boom and bust are likely, AI won’t disappear. Instead, investment and talent will gravitate toward firms that can turn broad agreement into real, repeatable value in fields like medicine, logistics, materials science, and education. Meanwhile, ventures built mainly on scale and slick marketing will fade, leaving some data centers underused and the market more cautious after paying the price for overreach. Even now, these substantial budgets, along with the expensive data centers and specialized skilled workers needed, help explain why only a few well-funded companies can afford to develop the most advanced AI capabilities.

Implications for medicine

The economic and technical dynamics described above have direct implications for medical AI. In diagnostic imaging, for example, AI systems are increasingly used to assist radiologists in detection and triage tasks, where performance, interpretability, and integration into clinical workflows are critical. Similarly, in drug discovery and protein modeling, large-scale models have demonstrated utility, albeit within research-intensive and resource-heavy environments. In clinical documentation, generative models are being deployed to assist with note generation and administrative burden reduction. These applications highlight a key distinction: while some domains justify high infrastructure costs due to clear clinical or economic value, others, particularly less specialized or consumer-facing tools, may not. This reinforces the need to evaluate AI not only by technical capability but also by domain-specific utility and sustainability.

Conclusions

In medicine, the implications of these trends are heterogeneous. Clinician-facing systems, such as diagnostic imaging support tools and automated clinical documentation, are typically integrated within institutional workflows and subject to validation and regulatory oversight. In contrast, patient-facing applications are often direct-to-consumer, with different risk profiles and lower barriers to adoption. This distinction is critical, as the economic and technical constraints discussed above may affect these groups differently, particularly in terms of reliability, accountability, and willingness-to-pay.

AI is real; the myth of its inevitability is not. The demands of scale, energy, and intellectual limits make endless exponential growth unlikely without careful economic, regulatory, and environmental checks. Will these echo chambers of recycled consensus ultimately achieve cognition or not, also remain unclear, as human consciousness itself remains a mystery. We are in the process of building computational empires; which will survive is anyone’s guess, history says none last. It would be wise to see through the hype and beware of confident multi-decade predictions you hear all around and remember as the forecast horizon extends, uncertainty grows, and such projections should not be taken as guarantees for long-term policy or investment decisions.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Medical Artificial Intelligence. The article has undergone external peer review.

Peer Review File: Available at https://jmai.amegroups.com/article/view/10.21037/jmai-2026-1-0006/prf

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://jmai.amegroups.com/article/view/10.21037/jmai-2026-1-0006/coif). The author has no conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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