Ben Evans' post "The Deep Research Problem" argues that while AI can impressively synthesize existing information and accelerate certain research tasks, it fundamentally lacks the capacity for original scientific discovery. AI excels at pattern recognition and prediction within established frameworks, but genuine breakthroughs require formulating new questions, designing experiments to test novel hypotheses, and interpreting results with creative insight – abilities that remain uniquely human. Evans highlights the crucial role of tacit knowledge, intuition, and the iterative, often messy process of scientific exploration, which are difficult to codify and therefore beyond the current capabilities of AI. He concludes that AI will be a powerful tool to augment researchers, but it's unlikely to replace the core human element of scientific advancement.
The blog post "AI Is Stifling Tech Adoption" argues that the current hype around AI, specifically large language models (LLMs), is hindering the adoption of other promising technologies. The author contends that the immense resources—financial, talent, and attention—being poured into AI are diverting from other areas like bioinformatics, robotics, and renewable energy, which could offer significant societal benefits. This overemphasis on LLMs creates a distorted perception of technological progress, leading to a neglect of potentially more impactful innovations. The author calls for a more balanced approach to tech development, advocating for diversification of resources and a more critical evaluation of AI's true potential versus its current hype.
Hacker News commenters largely disagree with the premise that AI is stifling tech adoption. Several argue the opposite, that AI is driving adoption by making complex tools easier to use and automating tedious tasks. Some believe the real culprit hindering adoption is poor UX, complex setup processes, and lack of clear value propositions. A few acknowledge the potential negative impact of AI hallucinations and misleading information but believe these are surmountable challenges. Others suggest the author is conflating AI with existing problematic trends in tech development. The overall sentiment leans towards viewing AI as a tool with the potential to enhance rather than hinder adoption, depending on its implementation.
Summary of Comments ( 94 )
https://news.ycombinator.com/item?id=43133207
HN commenters generally agree with Evans' premise that large language models (LLMs) struggle with deep research, especially in scientific domains. Several point out that LLMs excel at synthesizing existing knowledge and generating plausible-sounding text, but lack the ability to formulate novel hypotheses, design experiments, or critically evaluate evidence. Some suggest that LLMs could be valuable tools for researchers, helping with literature reviews or generating code, but won't replace the core skills of scientific inquiry. One commenter highlights the importance of "negative results" in research, something LLMs are ill-equipped to handle since they are trained on successful outcomes. Others discuss the limitations of current benchmarks for evaluating LLMs, arguing that they don't adequately capture the complexities of deep research. The potential for LLMs to accelerate "shallow" research and exacerbate the "publish or perish" problem is also raised. Finally, several commenters express skepticism about the feasibility of artificial general intelligence (AGI) altogether, suggesting that the limitations of LLMs in deep research reflect fundamental differences between human and machine cognition.
The Hacker News post titled "The Deep Research problem" (linking to a Ben Evans article of the same name) has generated a moderate discussion with several insightful comments. The central theme of the comments revolves around the increasing difficulty and cost of performing deep research, particularly in semiconductor manufacturing, and its implications for future innovation.
Several commenters agree with Evans' central premise. One commenter highlights the rising capital expenditures (CAPEX) in semiconductor fabrication, specifically mentioning TSMC's recent fab in Arizona projected to cost $40 billion. They link this escalating cost to the immense complexity of advanced nodes and the diminishing returns on investment, making it increasingly challenging for smaller players to compete. This reinforces Evans' point about the consolidation of research efforts within a handful of giant companies.
Another commenter expands on this by drawing parallels to the aerospace industry, where similar consolidation has occurred due to the massive research and development costs involved. They argue that this trend is natural in industries with high barriers to entry and suggest that we might see a similar pattern emerge in other deep tech sectors.
A different perspective is offered by a commenter who points out that while research might be consolidating in some areas, it's simultaneously exploding in others, particularly in software and AI. They contend that the barriers to entry in these fields are significantly lower, enabling smaller companies and even individuals to make significant contributions. This suggests a nuanced picture where deep research is becoming more concentrated in hardware-centric industries while remaining more distributed in software-driven fields.
Another commenter raises the point that the sheer volume of information necessary for deep research is growing exponentially, requiring increasingly specialized expertise. They suggest that this complexity necessitates larger teams and more sophisticated tools, further contributing to the rising costs and the trend toward consolidation.
One commenter questions the long-term implications of this trend, expressing concern about potential stagnation if innovation becomes confined to a few large entities. They suggest the need for alternative models of funding and collaboration to ensure continued progress in critical areas.
Finally, a comment highlights the increasing importance of software in even traditionally hardware-driven fields like semiconductors. They argue that as complexity increases, software becomes crucial for design, simulation, and optimization, potentially offering new avenues for innovation and perhaps even mitigating some of the escalating costs associated with hardware research.
Overall, the comments on Hacker News reflect a general agreement with Evans' observations about the growing challenges of deep research. They explore the various facets of this issue, from rising costs and consolidation to the shifting landscape of innovation and the increasing importance of software. The discussion highlights the complex and multifaceted nature of the problem and the need for further exploration and potential solutions.