As much as we feel the pace of AI development has been accelerating, I’d argue that things have been stagnant since the release of ChatGPT. I recognize this as a somewhat flippant statement given that the pace of product developments has grown exponentially, but the underlying technologies haven’t followed the same trajectory.
I started working in this field of AI more than a decade ago — at that time we just called it machine learning and, before that, statistics. I’ve had the fortune of building, and teaching students to build, these technologies as both a data scientist in the industry and now as a professor at Utah State University.
So, as we’re coming off weeks of <a href="https://darioamodei.com/post/we-must-pace-the-frontier” rel=”nofollow noopener” target=”_blank”>statement, after statement, after <a href="https://www.anthropic.com/threat-intelligence-report-september-2026″ rel=”nofollow noopener” target=”_blank”>statement proclaiming why we should all fear for our existence and why we have a moral imperative to slow AI development, I have to disagree. I believe we need to change what we’re accelerating towards.
First, allow me a brief aside on my opinions over recent statements. The organizations building “frontier models” (OpenAI, Anthropic, etc.) benefit from a public slowdown. Doing so would reduce the threat of diminishing returns on a product that, under the current paradigm, will continue to cost more to improve upon. Those same industry leaders would also benefit from the effects of regulatory capture, using government regulation (i.e. a slowdown) to limit the ability of future competitors to match or exceed their AI capabilities.
Now, in their view, if we don’t slow down, they have no choice but to continue building the tool that they “earnestly believe could kill all humans.” (Side note: I can’t think of a time in the past when a company publicly stated that their product could end humanity and they need us to slow them down. I’d suggest just stop doing that.)
The question I believe is most important to answer in this discussion is this: What do we hope to achieve by slowing down? Will slowing down replenish and revitalize the Great Salt Lake or dwindling water supplies of reservoirs? Will it solve housing affordability? Will it ensure that the students we teach everyday in our classrooms find gainful employment in the field of their choosing?
Maybe.
I’d argue, however, that the only way we’ve ever solved our great problems or taken strides in advancing quality-of-life have come from speeding up our efforts. Don’t mistake this as saying that I’d like to blindly increase compute resources across our state or nation. Instead, I’d like us to speed up our efforts and investments in human ingenuity and curiosity.
AI will likely be a part of that — it may even be foundational — but what I’d like us to do is reframe the goal to better align with our actual desires.
The discussion of slowing AI development is based on the argument that AI development is optimizing towards replicating the capabilities of the human mind generally (hence the term Artificial General Intelligence, or AGI). But, barring a shift in approach, LLMs are bounded by the availability of novel data and sufficient compute.
Concretely, we need to center the experience and impacts on humans in our AI development efforts. If we shift the optimization towards increasing human flourishing, we may find alternative and more efficient solutions.
If my goal is to reduce the harmful impacts of a drying Great Salt Lake, for example, developing AGI that can possibly help mitigate those issues at the expense of incalculable resources is one approach.
Alternatively, I could support the research and implementation efforts of many great people across our state’s public and private environmental sectors to address the multifaceted and constantly evolving situation, using all tools available.
People that have the ability to think creatively, outside of the bounds of existing data and methods to come up with new solutions on a modicum of equivalent compute resources (human brain runs on around 10-20W while state-of-the-art GPUs peak around 600W or more), with the added bonus of providing those humans meaning in their lives and an income to spend in our economy.
The present and potential harms of any technology can typically be traced back to a lack of understanding either on the creator, consumer or both. We all have a moral imperative to remain an educated populace. Blindly calling for the elimination of a technology that you struggle to even define or boosting a technology that calls for the end of human critical thinking is detrimental to our society.
The encouraging news is that the single best thing we can do to counter these outcomes is the same thing we’ve always done as humans: learn.
We need people to remain intellectually curious, and we need to support educators and our systems of education in the pursuit of that effort. I’m of the opinion that the success of education in the U.S. is not because we’re the best at teaching facts or using the newest tools, but because we’re the best at making people curious. What I fear the most in this era is us losing that.
Sharad Jones, Ph.D.,is a Professional Practice Associate Professor in Utah State University’s Data Analytics and Information Systems Department and Executive Director at the Analytics Solutions Center. Through teaching courses in Machine Learning, AI, and ethical Data Science, along with mentoring in the Analytics Solutions Center, he helps connect students with businesses to address real-world technical challenges. With previous roles as a research Data Scientist at Carvana, Maxar Technologies, and Striveworks, as well as co-founder and CTO of Scribbi, Sharad is passionate about developing new technologies responsibly and educating others on the opportunities in a data-informed world.
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