Comparing AI and Human Problem-Solving

In various papers with Matthias Holweg Mari Sako Jessica Hullman - we've argued that: → AI is fundamentally data-driven and backward-looking → Human cognition is theory-driven and forward-looking A new benchmark (ARC-AGI-3) offers a concrete test: how do humans versus AI deal with genuinely new tasks and problems? The results: Humans solve ~100% of the tasks Frontier AI systems: <1% Yes, less than 1%. (Gemini, GPT, Claude, Grok.) What’s going on? These environments require agents to: • explore • infer goals (without being told) • build models of the environment • plan efficiently In short: AI struggles with the unknown. This is exactly where a purely data-driven, backward-looking system should struggle—and where theory-driven, forward-looking reasoning becomes critical. Not a “gotcha” - but a useful reminder: We may be over-indexing on prediction—and underestimating the role of theory, causality, and forward-looking reasoning in intelligence.

𝐓𝐡𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐨𝐟𝐭𝐞𝐧 𝐟𝐫𝐚𝐦𝐞𝐝 𝐚𝐬 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐯𝐬 𝐡𝐮𝐦𝐚𝐧 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬. But real organizations don’t choose one over the other - they design how both work together. This framework breaks down the fundamental differences between AI agents (digital workers) and human employees, not to rank them, but to show where each creates the most value. AI agents excel at execution. They follow predefined workflows with speed, consistency, and precision, operating 24/7 across systems without fatigue. They scale instantly, process massive volumes in parallel, retrieve information perfectly, and enforce rules exactly as designed. Humans excel at judgment. They apply strategic thinking, contextual understanding, intuition, and experience to navigate ambiguity, set priorities, and decide when rules should bend in service of outcomes. The contrast becomes clearer across dimensions: AI agents thrive on structure, clearly defined inputs, and guardrails. Humans thrive in uncertainty, trade-offs, and situations where context matters more than instructions. AI delivers consistency under load. Humans adapt when reality doesn’t follow the plan. AI executes decisions. Humans own decisions. AI can generate outputs at scale. Humans define vision, ethics, accountability, and long-term direction. The pattern is not replacement - it’s elevation. As AI agents take over repetitive, high-volume, rules-driven execution, human roles shift upward: From operators → reviewers → decision-makers → strategists. The strongest organizations don’t ask “Where can AI replace people?” They ask “Which work should never require human effort again - and where is human judgment irreplaceable?” That’s how AI agents and humans create leverage together. ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

Stop Asking AI for the Answer. Ask It for the Argument. A neuroscientist's experiment reveals which human-AI teams actually win... and why most get it exactly wrong. Neuroscientist Vivienne Ming recently ran an experiment: humans, #AI, and human-AI hybrid teams each had one hour to forecast real-world outcomes, measured against the collective intelligence of a prediction market. The AI performed well. The humans did not. The hybrid teams? It depends on what you mean by hybrid. Most took the AI's answer and called it their own. They performed no better than the AI working alone. Others fed their own predictions back to the AI asking it to validate them, #ConfirmationBias with a high-tech veneer. They performed worse than AI working solo. But 5-10% of teams did something different. They pushed back. They demanded evidence. When the AI expressed confidence, they questioned it. When they had a strong intuition, they asked the AI to argue against it. They were the only group to consistently rival the prediction market. On certain questions, they beat it. The lesson isn't that AI is a resource to deploy. It's that AI is a sparring partner to engage. Before accepting an AI's output, ask it for the strongest argument against itself. When it hedges, pay attention, that's usually where the real uncertainty lives. Think of it as a brilliant colleague who has read everything and understands nothing: useful precisely because it's different from you, not because it will agree with you. Engage in #debate not acceptance. The uncomfortable finding is that the human qualities that matter most in this collaboration aren't confidence or decisiveness. They're intellectual humility, the willingness to be wrong in public and stay curious anyway. We celebrate the former. We rarely practice the latter. The question isn't whether AI will replace human judgment. It's whether we're willing to do the work that makes human judgment worth keeping. #RetailStrategy #Leadership #FutureOfWork #inspiration #neuroscience The Wall Street Journal #gemini #copilot #claude

𝗖𝗮𝗻 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝘁 𝗮𝗹𝗹 𝗯𝗲 𝗰𝗼𝗺𝗽𝗮𝗿𝗲𝗱 𝗮𝗴𝗮𝗶𝗻𝘀𝘁 𝗛𝘂𝗺𝗮𝗻 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? I keep reading about AI excelling beyond human performance, but that view in isolation is short-sighted. If our worth were measured solely in terms of speed, volume, and efficiency — as machines are — what would that say about our humanity? Reality is not one-dimensional: "𝗔𝗜 𝗿𝘂𝗻𝘀 𝗼𝗻 𝗰𝗼𝗱𝗲, 𝗯𝘂𝘁 𝗵𝘂𝗺𝗮𝗻𝘀 𝘁𝗵𝗿𝗶𝘃𝗲 𝗼𝗻 𝘁𝗵𝗲 𝘂𝗻𝘄𝗿𝗶𝘁𝘁𝗲𝗻." The 2024 AI Index Report from Stanford University does show that in tasks like image classification and language comprehension, AI has sprinted ahead. In these areas, AI can process and analyse data at speeds and accuracies far beyond human capabilities. Who would have guessed a decade ago that machines could outpace us in areas we thought of being uniquely human? It's an awesome progress and indication of more to come. But does this mean that AI will overtake the full spectrum of human intelligence and smartness across the board? Given the complexity of human performance and the environment we are living in, this would be a real challenge. While AI may be better at: ▪️ Conducting high-speed, high-volume data analysis. ▪️ Recognizing and categorizing images faster than the human eye. ▪️ Performing complex calculations and predictive tasks with precision. Human Performance is still excelling better in: ▪️ Understanding cultural differences and social nuances. ▪️ Adapting swiftly to new, unanticipated scenarios. ▪️ Applying creativity and innovation in problem-solving. Looking at the professional world, it's quite obvious that the value-add of humans is the determining factor for purposeful work: 🔹A Procurement Professional doesn't just follow a sourcing script but navigates market dynamics and long-term goals to create win-wins with suppliers. 🔹An Auditor's work goes beyond compiling figures or recording deviations but considers sound, ethical principles to adjust measures to risks and severity. 🔹A Marketer is not only coming up with keywords and catchphrases but is able to weave narratives that are not bland but touch hearts and minds. 📍We are more than task performers in controlled settings. We sense, feel, and intuitively navigate the complex rhythms of life and work. The narrative of AI beating human performance on intellectual tasks is short-sighted and technical. In isolation, it's a theory without a practical meaning, causing anxiety for some rather than inspiration. 📍I know i am an idealist here but wouldn't it be a better measure to benchmark AI on its ability to augment Human intelligence in real-world circumstances? Curious to read your thoughts on this. ❓In your view, is human performance really comparable and where does a combination make most sense #artificialintelligence #aiindex2024 #procurement #ai

AI vs Human? You’re Completely Missing the Point. Too many people frame AI as a competitor: • “AI writes better than humans.” • “AI outperforms doctors at diagnosis.” • “AI can code faster than developers.” This isn’t just flawed thinking—it’s actively harmful. 🔥 Here’s why: When we see AI through the lens of individualistic competition, we overlook its true power: • AI alone: Efficient but uncreative. • Humans alone: Creative but limited in speed and scope. • AI + Humans: Unstoppable combination of creativity, speed, and strategic insight. ✅ The Winning Shift: It’s about collaboration, not competition. Think of it like Iron Man: Tony Stark (human) + Jarvis (AI). Individually capable—together, invincible. 🔴 Old Mindset: 1. Fear AI replacing jobs. 2. Compete directly against AI. 3. Resist the inevitable. 🟢 New Mindset: 1. Embrace human + AI collaboration. 2. Amplify human strengths (judgment, empathy, creativity). 3. Accelerate productivity and innovation exponentially. The true revolution isn’t about AI becoming better than you. It’s about you becoming exponentially better with AI. So, ask yourself: Are you still competing against AI, or are you ready to join forces and redefine what’s possible?

𝗔𝗜 𝗦𝗵𝗼𝘂𝗹𝗱 𝗗𝗼 𝘁𝗵𝗲 𝗖𝗵𝗼𝗿𝗲𝘀.  𝗛𝘂𝗺𝗮𝗻𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗗𝗼 𝘁𝗵𝗲 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴. This image captures the most practical view on #AI adoption. AI works best when assigned routine, repeatable tasks. Humans work best when focused on judgment, #creativity, and relationships. The problem starts when teams reverse this logic. Many organizations use AI - to write, decide, and speak. Then they leave people stuck - with admin work, coordination, and clean up. This approach drains value instead of creating it. A better operating principle for the future of work looks like this. • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. - Use AI for scheduling, reporting, data cleanup, and documentation. - Free your time for decisions, conversations, and problem solving. • 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗵𝘂𝗺𝗮𝗻 𝘄𝗼𝗿𝗸. - Writing, design, leadership, and strategy shape trust and meaning. - When machines replace these too early, quality drops and ownership fades. • 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗶𝗺𝗲 𝗿𝗲𝘁𝘂𝗿𝗻𝗲𝗱, 𝗻𝗼𝘁 𝘁𝗼𝗼𝗹𝘀 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱. The real ROI of AI shows up as fewer hours lost to low value work. Track time saved per role, per week. • 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝗿𝗼𝗹𝗲𝘀, 𝗻𝗼𝘁 𝘁𝗮𝘀𝗸𝘀. When AI removes busywork, redefine expectations. Raise the bar on thinking, not output volume. The future belongs to teams who assign work with intent. #Machines handle #repetition. #People handle #responsibility.

Recently, Mark Zuckerberg claimed AI will replace mid-level engineers by 2025. Bold claim … but it's misleading. There's no doubt that AI tools like ChatGPT and Copilot can reshape workflows. They can generate boilerplate code, debug simple issues, and automate repetitive tasks. But AI tools can't replace human engineers (and they aren't designed to). Mid-level engineers are the backbone of engineering teams. They debug complex issues, mentor juniors, and build scalable systems that actually work. AI, on the other hand, can only predict and calculate. It can't: → Weigh trade-offs or handle edge cases → Solve nuanced problems creatively → Adapt strategically when things go awry In fact, AI just makes human skills like problem-solving, collaboration, and judgment even more valuable. Only humans can review AI-generated solutions for accuracy, spot edge cases AI can't identify, and turn automated outputs into scalable, reliable systems. The future of development isn't fewer engineers … it's engineers who know how to work with AI and fill in where machines fall short. AI and human ingenuity together will drive innovation. And mid-level engineers will always be at the heart of that collaboration. #FutureOfWork #AI #SoftwareEngineering #Meta

🫀While everyone's obsessing over AI strategy, the smartest leaders are doubling down on humanity As AI handles more tasks, human skills become the sustainable competitive advantage. AI can optimize processes, but it can't navigate messy, emotional reality of leading humans through rapid change. At least not yet. Case from my practice: * Series C fintech CEO frustrated that AI implementation didn't deliver productivity promised. His team used tools but were disengaged, and customer scores declined despite faster response times. x Problem: He focused entirely on optimizing processes but neglected human elements that drive performance. + Shift: Designed "AI + Human" strategy leveraging technology while amplifying human capabilities. ⚡Framework for human-centered AI leadership-AMPLIFY A-Acknowledge human fears: Address AI anxiety directly M-Meaning creation: Help team understand how AI enhances their value P-Personal connection: Increase face-to-face interactions L-Learning investment: Develop uniquely human skills I-Individual recognition: Celebrate human contributions F-Future visioning: Co-create vision where humans + AI thrive together Y-Yes to humanity: Consciously choose human approaches even with AI options Double down on humanity: 🤖AI handles data analysis, routine communications, process optimization 🫀Humans focus on strategic thinking, creativity, relationships, meaning-making 🤖AI provides instant responses, scaled efficiency 🫀Humans provide: EQ, contextual judgment, innovation, authentic connection 🤖AI optimizes existing processes + known patterns 🫀Humans create new possibilities + breakthrough solutions Human skills that are MORE valuable with AI: *EQ, Reading between lines, understanding unspoken needs *Creative solutioning: Connecting disparate ideas in novel ways *Adaptive thinking: Navigating ambiguity *Relationships: Creating psychological safety *Meaning-making: Helping people understand purpose behind change *Ethical judgment: Making decisions that consider broader human impact 💡Humanity-first AI leadership questions: Where do we choose efficiency over humanity, and what's the real cost? What uniquely human capabilities should we develop intentionally? What human skill does this free people to develop? Plot twist: Companies treating AI as a human replacement will lose to companies that treat AI as human amplification. The future belongs to leaders who can blend technological capability with deep humanity. 🚀 Bottom line: AI will make you faster. Humanity will make you better. Leaders who master both will build the companies that actually matter. What's one way you're intentionally choosing humanity over efficiency in your AI implementations? Rooting for you (and humanity), CoachSK

AI won't replace you. But someone using AI will. Here's how I divided labor between human intelligence and artificial intelligence: The Mistake: Treating AI as a replacement. AI isn't about replacing humans... it's about optimal division of labor. The Framework: Comparative Advantage Humans: Strategy, creativity, relationship building AI: Volume, consistency, pattern recognition Play to strengths. What Humans Do Best: 1. High-stakes decisions (client relationships, strategic pivots) 2. Creative breakthroughs (new frameworks, original insights) 3. Emotional intelligence (sales calls, negotiations) 4. Context interpretation (reading between the lines) What AI Does Best: 1. Content generation (drafts, outlines, variations) 2. Research synthesis (summarizing reports, extracting data) 3. Repetitive tasks (formatting, scheduling, data entry) 4. Pattern matching (content recommendations, trend analysis) My Division of Labor: HUMAN (Me): - Client calls and relationship building - Strategic content direction - Final approval on all outputs - High-value problem solving AI (OpenAI, Anthropic): - First drafts and content variations - Research and data synthesis - Scheduling and distribution - Template generation The Workflow: 1. Human sets strategic direction 2. AI generates options/drafts 3. Human reviews and refines 4. AI handles distribution 5. Human monitors performance 6. LOOP Real Example - Content Creation: Human: Define topic + key message + target audience AI: Generate 5 hook variations + thread outline Human: Select best hook + edit for voice AI: Format for X, LinkedIn, Threads Human: Final approval AI: Schedule and publish The 10x Multiplier: Without AI: 1 hour = 1 post With AI: 1 hour = 10 posts Same strategic thinking. 10x the output. Common Mistakes: ❌ Letting AI make strategic decisions ❌ Using AI without human oversight ❌ Copying AI outputs verbatim ❌ Trying to do everything manually ✅ Human strategy + AI execution Tools I Use: ChatGPT - Research, drafting Claude - Long-form content Notion - Knowledge management Typefully - Cross-platform distribution Eleven Labs - Voice cloning HeyGen - Video generation Your Move: Audit your weekly tasks. Which require human judgment? Which are repetitive/scalable? Delegate the latter to AI. Keep the former for yourself.

Some good takeaways for AI founders in this article AI agents successfully solved 9 out of 10 lab challenges modeled on real-world vulnerabilities when given specific targets. Their core strengths lie in exceptional pattern recognition—identifying tech stacks from subtle clues—and executing complex, multi-step exploits. Economically, the cost per successful exploit in these focused tasks is remarkably low, often less than $10. 1) Broad Scope Performance Degradation: When agents were given a wide scope without a specific target, performance dropped noticeably. 2) Proficiency in Multi-Step Reasoning: AI agents proved adept at executing complex, multi-step attack chains. 3) Exceptional Pattern Recognition: A standout capability is the agents' speed and accuracy in pattern matching, which is described as "fast and not human-like." 4)  Encyclopedic Knowledge: The agents possess a comprehensive, built-in knowledge of cybersecurity methods and known attacks, which they can systematically apply to a target. Despite their strengths, the agents exhibited significant weaknesses that mirror the gap between theoretical knowledge and practical, creative application. 1) Ineffective Tool Utilization and Lack of Creativity: Agents struggled with challenges that required using specialized tools or thinking beyond direct attacks. 2)  Strategic Deficiencies: Iteration vs. Pivoting. A core difference between AI and human testers emerged in their response to failure. • AI Iterates: When an approach failed, AI agents tended to try minor variations of the same method. • Humans Pivot: Human testers recognized dead ends more quickly and would pivot to entirely different strategies. 3) Poor Prioritization (Depth vs. Breadth): As seen in the broad scope scenario, agents struggle to prioritize targets and investigative paths. They fail to narrow their focus on promising signals, leading to lower efficiency compared to a human tester who concentrates effort where it is most likely to yield results.