In the year 2025, widespread misapprehensions surrounding artificial intelligence (AI) have emerged as the technology continues to evolve and integrate into everyday life at an accelerating pace. Among these misunderstandings, three notable themes dominate public discourse: the assumption that AI development is hitting a plateau, skepticism about the safety of autonomous vehicles, and doubts about AI's ability to create genuinely new knowledge. This analysis explores each of these issues in detail, grounded in the latest developments and expert opinions from the AI research community.
AI Progress: Is There a Ceiling?
When OpenAI released GPT-5 in May 2025, initial reactions included speculation that the AI’s impressive numbering might be more nominal than indicative of significant advancement. A feature article in The New Yorker, headlined "What if A.I. Doesn’t Get Much Better Than This?", argued that GPT-5 represented a plateau in progress for large language models (LLMs), suggesting substantial innovation could be stagnating.
However, it later became clear that the GPT-5 release largely focused on optimizing cost-efficiency rather than delivering dramatic performance enhancements. Over the following months, key industry leaders such as OpenAI, Google, and Anthropic introduced new models that demonstrated meaningful strides in capabilities, especially in tasks with high economic value.
Google DeepMind’s lead of the deep learning team, Oriol Vinyals, emphasized the continued impactful development by noting that the performance uplift in the Gemini 3 model was "as big as we've ever seen," directly countering the notion that scaling improvements have ceased. "No walls in sight," Vinyals concluded about future prospects.
Despite this optimism, challenges remain where training data are difficult or costly to acquire. For example, AI applications functioning as personal shoppers may experience slower progress due to limited data availability. Helen Toner, interim executive director at the Center for Security and Emerging Technology, remarked that AI improvements might continue unevenly—"maybe AI will keep getting better and maybe AI will keep sucking in important ways." Still, the overarching evidence does not support the idea that AI advancement has stalled.
Self-Driving Cars: Assessing Safety Versus Public Perception
The safety of autonomous vehicles is another area beset by public doubt, often fueled by concerns about accidents resulting from AI errors that can have fatal consequences. Unlike simpler AI tasks—such as chatbot responses, which might cause harmless errors like miscounting letters—the stakes with autonomous driving are substantially higher.
Public comfort with driverless cars remains low in certain regions. Surveys conducted in the U.K. reveal that only 22% of 2,000 adults reported feeling comfortable traveling in a driverless vehicle, while the figure is even lower in the U.S. at 13%. Negative incidents, such as a self-driving car operated by Waymo fatally striking a cat in San Francisco during October 2025, have intensified scrutiny and public concern.
Nonetheless, quantitative data from Waymo paints a different picture regarding the relative safety of autonomous vehicles. An analysis covering 100 million miles driven without human input revealed that Waymo’s cars were involved in nearly five times fewer injury-causing crashes compared to human drivers. Additionally, incidents causing serious injuries or worse occurred 11 times less frequently with Waymo’s autonomous technology.
AI and the Generation of New Knowledge
Another prevailing belief is that AI systems, including advanced large language models such as GPT-5, lack the capacity for producing original insights and solely reproduce information drawn from their training data. This critique, sometimes summarized by the disparaging term "stochastic parrots," suggests that AI “creativity” is merely illusionary replication. Supporting this pessimistic view, a June 2025 paper from Apple argued that any reasoning apparent in AI behavior is illusory rather than genuine.
However, recent examples challenge this assertion. Sébastien Bubeck, a mathematician currently with OpenAI, recounted his experience with a longstanding open problem in graph theory originally published in 2013. After more than a decade without resolution, he utilized an AI system based on GPT-5, allowing it to process the problem over two days. The AI demonstrated the discovery of a "miraculous identity" that effectively solved the difficult question.
These outcomes highlight the nuanced nature of AI problem-solving capabilities. While AI models sometimes struggle with straightforward tasks such as interpreting simple diagrams, they simultaneously achieve remarkable success by winning top awards in mathematics and programming competitions and discovering original mathematical structures. Dan Hendrycks, executive director of the Center for AI Safety, acknowledged that while the processes LLMs use may differ from traditional human reasoning, "LLMs can certainly execute sequences of logical steps to solve problems requiring deduction and induction." He noted that labeling this cognitive function as 'reasoning' is a matter of semantics.
Conclusion
The rapid evolution of artificial intelligence in 2025 brings both opportunity and complexity, and with it, a slew of common misconceptions. Contrary to widespread beliefs, AI progress is ongoing and marked by significant improvements, particularly in economically relevant applications. Although public confidence in autonomous vehicles remains tentative, statistical evidence points to notable safety advantages compared to human drivers. Finally, the presumption that AI cannot generate new knowledge does not hold up to scrutiny, as demonstrated by examples of AI-assisted problem-solving advancing academic challenges.
As AI technology continues to develop, dispelling these misunderstandings is vital for informed discourse, policy formulation, and public adoption strategies.