A Reddit user in r/learnprogramming asks whether Human-Computer Interaction (HCI) courses will remain relevant as AI agents become more dominant in the tech industry. The post sparks a substantive discussion, with commenters largely agreeing that HCI is becoming more relevant, not less, but acknowledging that the field faces significant challenges in terms of recognition and job prospects.
Several key perspectives emerge. One commenter argues that HCI is foundational to innovation, connecting computer science to society and addressing critical questions about technology's impact — from social media addiction to brain-computer interfaces. A PhD candidate in HCI offers a more sobering view, noting that HCI jobs tend to get "cannibalized" by adjacent engineering fields once experimental technologies mature, leaving HCI practitioners without credit or clear career paths. Despite this, they emphasize HCI's adaptability and enduring relevance.
Practical career advice is also shared, stressing that standing out in the competitive HCI landscape requires real-world experience, meaningful internships, original projects, and relevant open-source contributions — not just coursework. The consensus is that HCI is evolving from traditional UI design toward designing human interactions with AI systems, focusing on intent, trust, control, and meaningful human autonomy in an AI-driven world.
A Reddit discussion in r/MachineLearning explores whether starting an independent AI consulting or freelancing venture is viable in today's market. The original post was removed, but the comments offer rich, experience-based perspectives from practitioners across different markets. A co-founder of two AI consultancies in the German/DACH market shares a cautionary tale: the work was less glamorous than expected (mostly data engineering and reports), client acquisition was grueling, and the market quickly became saturated by major players like BCG Gamma and McKinsey's digital branch. However, a counterpoint from a Netherlands-based freelancer claims €100K profit in 2020 from deep learning projects, emphasizing that sales skills — not just technical ability — are the real differentiator.
Several commenters converge on a central theme: solving customer problems matters far more than selling "AI" as a service. One experienced consultant advises learning an industry and providing better solutions rather than leading with AI as a buzzword. Others note that most companies either want off-the-shelf solutions, prefer to build in-house, or have unrealistic expectations about AI capabilities. An independent NLP/ML consultant reports success working with small companies lacking internal AI expertise, while another freelancer stresses that vanilla software development keeps the lights on, with AI projects representing only about 10% of client demand — but at higher margins. The consensus is clear: technical skill alone is insufficient; sales ability, personal networks, and domain expertise are essential for building a sustainable AI consulting business.
A product manager with 3–4 years of experience in AI products shares a candid critique of the "AI Product Manager" role as it exists today, particularly in the Indian startup ecosystem. The author argues that many positions marketed as AI PM roles are actually implementation and customer success roles disguised with an AI label. In these roles, the core AI platform is already built by engineering teams, and the PM's job reduces to configuring prompts, designing conversation flows, and deploying the system for enterprise clients — not shaping the product's roadmap or working on core AI capabilities.
The post highlights a frustrating career trap: after spending one or two years in such a role, professionals find themselves stuck. They lack the ML engineering background that companies building serious AI infrastructure demand, yet their experience is too AI-focused for generic PM roles. The author also challenges the prevailing hype, estimating that AI improves enterprise workflow efficiency by roughly 20–25 percent — far from the transformational claims commonly made. Many startups, the author notes, are essentially API wrappers built on top of OpenAI or Anthropic, yet hiring expectations still reflect an older era when AI products required deep ML research expertise.
Commenters largely validate the observations, with several noting that the implementation-vs-core-product distinction is a longstanding pattern in PM hiring that AI has merely amplified. Others draw parallels to previous hype cycles around mobile, blockchain, and growth PM titles, predicting that "AI PM" will eventually dissolve into standard product management as the technology matures. The consensus advice: before accepting an AI PM role, carefully evaluate whether the position involves building and evolving the core product or merely deploying it for clients.
Benjamin Todd argues that AI is most likely to automate entry-level white collar jobs first, leading to increasingly top-heavy organizations where traditional career training pipelines shrink. This poses a significant challenge for early-career professionals who may find fewer opportunities to develop skills through conventional junior roles.
He offers practical advice for those early in their careers, suggesting they should seek out mentorship on leadership skills as soon as possible, since these higher-level competencies are likely to remain valuable and harder to automate. The post implies a broader shift in how career development needs to be approached in an AI-driven economy.