This article is intended as a response to a recent blog post by UESCA, available at https://uesca.com/the-value-of-a-human-coach-in-an-ai-world/.
Rick’s recent UESCA article, “The Value of a Human Coach in an AI World,” makes several worthwhile points about the limitations of artificial intelligence in coaching. However, it also perpetuates some misunderstandings that need addressing, as well as incorrect or misleading information about the capabilities of AI when it comes to coaching.
Garbage In, Garbage Out: True for AI… and Human Coaches Too
Rick correctly identifies the principle that AI outputs are only as good as the inputs they receive. If an athlete asks an incorrect question, AI will indeed provide misleading answers. However, this criticism applies equally to human coaching. A coach responding to incomplete or incorrect information from an athlete also risks providing poor advice.
For example, if an athlete working with a human coach experiences declining performance but neglects to mention key factors such as calf stiffness or significant personal stress, the coach’s ability to provide effective guidance will inevitably be compromised. While a coach might occasionally detect subtle issues through pointed questions or intuition, they aren’t mind-readers and are ultimately limited by the athlete’s willingness or ability to communicate essential context.
Similarly, the effectiveness of AI hinges significantly on the athlete’s initial understanding of what to ask and their ability to iteratively refine their queries based on the responses they receive. For instance, an athlete asking about sodium intake when they should instead investigate pacing strategies highlights the importance of approaching AI interactions thoughtfully and iteratively. Unlike human coaches, AI cannot intuitively sense or prompt unreported issues, making it particularly valuable for athletes who possess the self-awareness and knowledge to guide these interactions effectively. This dynamic positions AI as especially beneficial for informed, experienced athletes capable of clearly articulating their needs and challenges.
AI Isn’t an Expert? Neither Are Many Coaches
Rick argues that AI lacks the years of experience and nuanced judgment of human coaches. Yet, let’s face an uncomfortable truth: neither do many human coaches. Spend any amount of time in professional coaching forums like UESCA’s own Facebook group, and you will quickly see evidence of coaches frequently asking basic questions that a qualified expert should already know. If an athlete is paying for expertise that amounts to generic or outdated advice, that athlete may indeed be better served by consulting AI directly.
The uncomfortable reality is this: AI can quickly synthesize current research, coaching methodologies, and best practices, often more accurately and reliably than a mediocre human coach relying solely on personal experience or outdated sources.
The Myth of Coaching Philosophy as a Unique Human Trait
Rick notes that AI is based on human-generated algorithms that inherently contain the biases of the creators. This is true, yet it fails to recognize that human coaches similarly follow philosophies they learned elsewhere. Most coaching philosophies are not original but adaptations of well-known frameworks, such as Daniels, Canova, Maffetone, or others. What AI uniquely offers is transparency and flexibility. With AI, athletes can access multiple coaching “philosophies” instantly and even blend them according to their individual needs. If an athlete wants to incorporate elements from Jack Daniels’s training philosophy one week and Pete Pfitzinger’s the next, an AI can adapt immediately and seamlessly, precisely because it does not hold personal views or biases.
The RPE Analogy: Missing the Mark
Rick analogizes human coaching to rate of perceived exertion (RPE), which is subjective, nuanced, and context-dependent, and contrasts it with AI’s supposedly rigid data dependence. This perspective misunderstands modern AI’s ability to incorporate subjective feedback effectively. Modern AI systems do handle subjective variables when prompted clearly by informed users. Athletes capable of clearly articulating their subjective experiences (such as intermediate or advanced runners) are uniquely well-positioned to benefit enormously from AI-driven analysis and guidance.
“You Can’t Talk to AI”: The Most Problematic Assertion
The assertion that “you can’t talk to AI” is perhaps the most incorrect and outdated point in Rick’s entire article. The modern generation of AI, especially conversational models like ChatGPT, excel precisely because of their interactive capabilities. Athletes and coaches alike regularly have nuanced conversations with AI, refining inputs and obtaining tailored advice in a dynamic, iterative process. Indeed, I’ve personally provided AI-generated responses within coaching forums, including the UESCA Facebook group, that received higher praise than human-generated responses. Clearly, AI is fully capable of sophisticated dialogue and providing accurate, tailored information.
AI Is Always Available and Ego-Free
Two additional significant advantages of AI are its constant availability and complete lack of ego. AI doesn’t get overwhelmed by having multiple athletes or other life stressors. It scales effortlessly, providing consistent and immediate responses regardless of demand. Additionally, AI has no ego to bruise; it doesn’t become defensive if its advice is challenged or if an athlete suggests something isn’t working. In contrast, human coaches can sometimes struggle with ego, leading them to double down rather than adapt when athletes report issues.
Cost vs. Value: The $100 Coach vs. the $20 Assistant
One critical area Rick doesn’t address is cost, which highlights a major advantage of AI-driven coaching. Athletes often pay over $100 monthly for coaching packages that offer limited communication, restricted email exchanges, and only a few scheduled calls. In contrast, AI-driven platforms like ChatGPT offer virtually unlimited, immediate access to tailored coaching guidance for around $20 per month. For athletes who are self-motivated and already possess a solid understanding of their training needs, AI provides a cost-effective, efficient alternative that maximizes accessibility and responsiveness without sacrificing quality or detail.
AI and the Self-Motivated Intermediate Athlete
Rick suggests AI is primarily beneficial for novices, but the opposite might be closer to the truth. In practice, AI often works best for intermediate athletes who are self-motivated and knowledgeable enough to effectively communicate their specific needs and feedback. For example, an experienced athlete might say, “My goal race is a hilly marathon in 16 weeks. I have a history of plantar fasciitis, and my last training block left me feeling overtrained. My current weekly mileage is 40. I want to run 5 days a week and can’t run on Saturdays. Generate a plan that prioritizes injury prevention and includes a conservative peak volume.” That level of detail allows the AI to generate a highly customized response. In contrast, a novice might simply say, “I want to run a 5K in 8 weeks. Give me a plan.” This is a vague prompt that lacks essential context like current fitness, preferred training frequency, or injury history. As a result, the output is more generic and less effective. The takeaway is clear: the more informed the athlete, the more value they can extract from AI. For these self-directed runners, AI becomes a powerful tool, a kind of assistant coach, helping them optimize performance while maintaining control and autonomy.
Conclusion
In an evolving coaching landscape, the ultimate winners will be athletes who increasingly demand higher-quality, personalized coaching. AI sets a new minimum standard. Coaches must now deliver value significantly beyond what a well-formulated AI query can produce. This will become increasingly true as people become more AI literate, learn how to prompt more effectively, and AI itself becomes more advanced at providing helpful answers with less explicit prompting. The market doesn’t care about a coach’s feelings, credentials, or effort. It rewards value. Coaches who can’t compete with AI in delivering meaningful insights and genuine results will inevitably lose relevance. Athletes themselves, however, should feel excited. They will increasingly have access to better information, greater choice, and coaching that’s truly worthy of their investment.