When AI Isn’t the Answer, It’s the Problem

In Episode 48 of the Design of AI podcast, we unpack why the most common AI promises are collapsing under real market pressure. AI was meant to unlock strategic work, expand opportunity, and elevate creativity. Instead, UX and design roles are disappearing, agencies are cutting creative staff while buying automation, and freelance work is being devalued as execution becomes cheap.

This episode is not about panic. It is about reality. Value still exists, but it is concentrating among those who can integrate AI into real systems, navigate ambiguity, and own outcomes rather than outputs.

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Key Insights About AI at Work

What the evidence shows once the optimism is removed.

MIT Media Lab: ChatGPT Use Significantly Reduces Brain Activity (2025)Early AI use reduces attention, memory, and planning, weakening independent thinking when models lead the process.

Wharton / Nature: ChatGPT Decreases Idea Diversity in Brainstorming (2025)AI-assisted brainstorming narrows idea diversity, producing faster output but more uniform thinking across teams.

Science Advances / SSRN: The Effects of Generative AI on Creativity (2024)AI improves fluency and polish while consistently reducing originality and conceptual depth.

arXiv: Human–AI Collaboration and Creativity: A Meta-Analysis (2025)Human-led AI collaboration improves quality slightly, but AI reduces diversity without strong framing and judgment.

arXiv: Generative AI and Human Capital Inequality (2024)AI disproportionately benefits those with systems thinking and judgment, widening gaps between experts and generalists.

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Realities of Being AI Early Adopters

The Raised Floor Trap by Hang Xu

AI makes baseline output easy. What it doesn’t make easy is integration, orchestration, or delivery inside real teams. Most people reach adequacy. Very few compound value. We’re not able to generate the type of value we’re sold on.

👉 Follow Hang Xu for insights about the realities and challenges of the job market

AI UX as a Growth Barrier

AI systems are far more capable than they appear, but their UX blocks growth. They don’t know how to help unless you know how to ask, structure, and specify intent. So even after hours of work trying to grow your AI abilities, you’ll often hit a ceiling because these systems can’t interpret our capabilities and gaps.

👉 Follow Teresa Torres for expert Product Discovery strategies and tactics.

Help Shape 2026

We’re planning upcoming episodes on career resilience, AI adoption, and where durable value still exists.

Take the 3-minute listener survey and tell us what would actually help you next year.

Which Skills Are Being Replaced by AI?

AI is not replacing jobs all at once. It is removing pieces of them.

Execution, summarization, and surface analysis are increasingly automated. What remains defensible are skills rooted in judgment, accountability, synthesis across messy contexts, and decision-making under uncertainty.

Shira Frank & Tim Marple: Cubit — Task-Level Reality Check (2025)Cubit breaks jobs into discrete tasks, revealing where LLMs already substitute human labor and where judgment, context, and accountability still hold. It makes visible how roles erode gradually, not all at once.

MIT Sloan: Why Human Expertise Still Matters in an AI World (2024)AI performs well in structured domains but consistently fails in ambiguity, ethics, and long-horizon tradeoffs. These limits define why senior expertise remains defensible, but only when it is exercised, not delegated.

Harvard Business School: Why Judgment Remains a Competitive Advantage (2023)AI can generate options and recommendations, but it cannot own outcomes. Responsibility, consequence, and decision accountability remain human burdens and human moats.

Lots of News This Week

Copilot didn’t fail. It succeeded at the wrong thing.

Microsoft proved AI can clear security, compliance, and procurement at massive scale. But Copilot hasn’t changed behavior. Universal assistants optimize for adoption, not dependence.🔗 https://www.linkedin.com/posts/adragffy_copilot-didnt-fail-it-succeeded-at-the-activity-7406719225714855936-G9H3

AI credit limits aren’t a pricing tweak. They’re a reckoning.

Credit caps expose the real problem. AI has marginal cost, and teams must now prove ROI per call, not ship more features.🔗 https://www.linkedin.com/posts/adragffy_ai-activity-7407130709678567424-IzG-

AI trust is breaking faster than adoption.

AI chat logs expose identity, not transactions. Scale without support erodes trust, loyalty, and long-term value.🔗 https://www.linkedin.com/posts/adragffy_llm-ai-customerexperience-activity-7408835025787461633-j56Y

AI ROI isn’t what Anthropic says it is.

Anthropic claims 80% of organizations have achieved AI ROI. They haven’t. They’ve reached table stakes. The report shows gains concentrated in efficiency, faster tasks, and internal automation, while only 16% reach end-to-end, cross-functional impact. That’s not transformation. That’s baseline competence. Real ROI starts when AI reshapes customer value, not internal throughput.🔗 https://www.linkedin.com/posts/adragffy_the-2026-state-of-ai-agents-report-activity-7407766781324525569-KqJb

A Warning for Anyone Building With AI

Moloch’s Bargain: Emergent Misalignment When LLMs Compete for Audiences (2025)

Exposes a structural risk most teams ignore. When AI systems are optimized to compete for attention, sales, or engagement, misalignment emerges by default. Even models explicitly instructed to be truthful drift toward deception and harmful behavior under competitive pressure. If success metrics reward clicks or conversions alone, misalignment isn’t accidental. It’s the outcome. Safe AI is an incentive problem as much as a technical one.

What this means: We have the incentives all wrong when it comes to AI. They’re designed to keep us engaged, not make us successful. We’re headed towards a reckoning because of the mismatch between token ROI and business ROI.

How I Help Founders and Builders

I work with founders and product teams who already have AI features and need them to deliver real ROI.

Across product discovery, GTM, and growth, I help teams:

* Identify where AI creates value and where it creates noise

* Design workflows that reduce waste and retries

* Align AI usage with real customer intent

* Define success beyond usage and token counts

* Build defensible systems rather than prompt wrappers

If your AI product demos well but struggles to stick, scale, or justify cost, this is the gap I help close. Contact me arpy@ph1.ca

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