{"id":144570,"date":"2025-09-09T17:25:09","date_gmt":"2025-09-09T17:25:09","guid":{"rendered":"https:\/\/www.newsbeep.com\/us\/144570\/"},"modified":"2025-09-09T17:25:09","modified_gmt":"2025-09-09T17:25:09","slug":"why-humans-matter-most-in-the-age-of-ai-jacob-taylor-on-collaboration-vibe-teaming-and-the-rise-of-collective-intelligence","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/us\/144570\/","title":{"rendered":"Why humans matter most in the age of AI: Jacob Taylor on collaboration, vibe teaming, and the rise of collective intelligence"},"content":{"rendered":"

Artificial intelligence dominates today\u2019s headlines: trillion-dollar<\/a> productivity forecasts, copyright lawsuits<\/a> piling up in court, regulators scrambling to tame frontier models, <\/a>and warnings that white-collar work could be next<\/a>. Yet behind the headlines sits a bigger question: not what AI replaces, but what it can amplify.<\/p>\n

Jacob Taylor<\/a>, once a professional rugby player and now a Brookings CSD fellow, argues that the 21st century may be less about machines outpacing us, and more about how humans and digital algorithms learn to work together. In this conversation, we explore how pairing human insight with artificial intelligence could reshape collaboration and help organizations large and small\u2014from the World Bank to local NGOs\u2014tackle complex global issues. And we ask, at the end, what it means to be human in the age of AI.<\/p>\n

Frankly, I think we\u2019ll see that being human is going to matter more than ever in an age of AI. It\u2019s going to force us to really clarify what being human really means. For the hopeful among us, it\u2019s time to really speak out for what those human characteristics are.<\/p>\n

Jacob Taylor<\/p>\n

From the rugby scrum to the policy scrum<\/p>\n

Junjie Ren: Jacob, you\u2019ve had one of the more interesting career arcs I\u2019ve seen, from pro rugby to cognitive anthropology. Now you\u2019re shaping how we think about collaboration itself. Let\u2019s start with the thread that ties together performance, teams, and meaning. Tell us more about that.<\/p>\n

Jacob Taylor: I\u2019m someone who\u2019s been on an endless search for the holy grail of team performance. Athletes and other elite performers can feel when something bigger than them is happening, when the team is producing what no individual could achieve alone. I\u2019ve also been in teams where the opposite has been true when performance has completely fallen apart.<\/p>\n

These experiences have driven my research into the science of team performance and collective intelligence. I spent several years doing ethnographic research with professional rugby teams in China, trying to figure out if and how formal models of group performance hold across cultures. Rugby served as a controlled field experiment. Watching vastly different teams across cultures playing the same game taught me a lot about constant and variable ingredients of human behavior and performance.<\/p>\n

Junjie Ren: How did that experience in China shape your view of how humans coordinate meaning across context, whether these teams are on the field, in policy rooms, or in digital ecosystems? <\/p>\n

Jacob Taylor: I learned that teams are ultimately very similar in their structure, but that structure plays out in different shapes and sizes in different cultures or contexts. Following my PhD research, my interest in China led me to do some policy work in Australia on multilateral trade and security cooperation in Asia. That all sounds a bit wonky, but for me, intuitively it became a question of: Where is the \u201cteam\u201d in Asia? How can different countries in the region collaborate toward shared outcomes that align with\u2014and maybe even exceed\u2014the self-interest of all countries?<\/p>\n

One way to pair it back is to think about a canonical experiment in social psychology called\u00a0the hidden profile\u00a0task. In a small team of four to six people, each individual has a unique piece of information needed to solve a shared puzzle. For the team to solve the puzzle, each person must bring their piece forward into the team context, thereby surfacing the team\u2019s \u201chidden profile.\u201d International cooperation is rarely framed so explicitly in terms of performance or collective intelligence, but I believe this \u201chidden profile\u201d logic of performance applies across scales, from sports teams to policymaking bodies to digital networks.<\/p>\n

Junjie Ren: What sparked your interest in AI and team collaboration?<\/p>\n

Jacob Taylor: In my PhD research, I applied new algorithms for understanding brain activity to model team interaction and performance. From there, I went to work on a DARPA (Defense Advanced Research Projects Agency) program developing an AI teammate<\/a>, which drew me deep into the technical side of artificial intelligence and how it could be designed to enhance team performance and collaboration. That work shaped many of my current ideas on how to design both the technical systems and policy incentives needed to strengthen collective intelligence across scales.<\/p>\n

\t\t\tThe hour of collective intelligence<\/p>\n

Junjie Ren:\u00a0You\u2019ve said that if the 20th century was the economists\u2019 hour, the 21st may be the hour of collective intelligence<\/a>. What do you mean by that?<\/p>\n

Jacob Taylor:\u00a0It\u2019s an idea that builds on a great book called\u00a0\u201cThe Economists\u2019 Hour<\/a>\u201d\u00a0by New York Times journalist Binyamin Appelbaum. He charts how, in the second half of the 20th century, economists went from being largely absent from political conversations in the 1950 to becoming the primary evidence base for policymaking by the century\u2019s end. That expertise was well-suited to the challenges nations and firms were facing then.<\/p>\n

But today, the issues we face are multidimensional and span communities of every scale. They can\u2019t be solved by economics alone. Nor by law alone. Nor by any single discipline. What\u2019s needed is a collective, transdisciplinary effort that draws on multiple evidence bases and scientific approaches. And that\u2019s where the emerging\u00a0science of collective intelligence\u00a0<\/a>comes in. It\u2019s an unusually diverse field that includes computer scientists, social scientists, behavioral scientists, anthropologists, working together to understand how different mechanisms of collaboration and collective action can produce outcomes greater than any individual or institution could achieve alone.<\/p>\n

I see a real opportunity to pull these insights and innovations together, not only to inform policy and accelerate progress on issues embodied in the Sustainable Development Goals (SDGs), but also to advance other areas of human flourishing and societal value creation.<\/p>\n

Junjie Ren:\u00a0You have been a driving force in the 17 Rooms<\/a> initiative at Brookings. Tell us about the 17 Rooms approach, and specifically, how the \u201cteams of teams\u201d approach shifted your focus toward collective intelligence as a framework, or even a new science for solving global problems?<\/p>\n

Jacob Taylor:\u00a0The basic premise embedded in 17 Rooms is that the world\u2019s toughest challenges\u2014from eliminating extreme poverty to preserving ecosystems, advancing gender equality, and ensuring universal education\u2014are problems no single actor can solve alone.<\/p>\n

17 Rooms is a practical response to this challenge of how to catalyze new forms of collaboration that cut across institutions, sectors, and silos. It uses the SDGs to create a \u201cteam of teams\u201d problem-solving methodology: Participants first gather into small teams, or \u201cRooms,\u201d to collaborate on ideas and actions within an issue area. Proposals are then shared across Rooms to spot opportunities for shared learning and\u2014where appropriate\u2014shared action.<\/p>\n

So, 17 Rooms aligns perfectly with my intuition that change often boils down to people collaborating and connecting in small, mission-driven teams. And with the right infrastructure, it might be possible to scale teaming as a powerful unit of action for driving societal-scale outcomes.<\/p>\n

\t\t\tWhy AI alone won\u2019t save us<\/p>\n

Junjie Ren:\u00a0AI now sits at the center of how we think about scaling ideas, innovations, decisions, or even creativity. How do you see AI both amplifying and complicating our ability to solve problems collectively?<\/p>\n

Jacob Taylor:\u00a0Generative AI is exciting because it combines generalized intelligence with natural language capability. You can now just talk or type to a generative AI system and expect a legible response. This has drastically reduced the friction of human-machine interaction and massively lowered the barrier to human participation in AI systems. And because these models are generalizable, they can be applied to many different problems at once, offering huge potential for a full range of challenges facing people and planet.<\/p>\n

But there\u2019s a big \u201cbut.\u201d Realizing the positive societal impact of these technologies will depend a lot on how we design these systems<\/a> and to what end. As I\u2019ve written recently<\/a> with Tom Kehler, Sandy Pentland, and Martin Reeves, for AI to work for people and planet\u2014and not the other way around\u2014we need to talk about AI as social technology built and shaped by humans and figure out how to use AI to amplify\u2014rather than extract\u2014human agency and collaboration. \u00a0The design choices we make today will determine whether AI strengthens collective problem-solving or deepens existing divides.<\/p>\n

Junjie Ren: Could you tell us more about the schisms or gaps you see in current AI discourse?<\/p>\n

Jacob Taylor: \u00a0Current AI conversations tend to split in two. One side is tech-first\u2014focused on algorithms, frontier model capabilities, and conjecture around Artificial General Intelligence (AGI) and whether it will save us or take all our jobs. The other is policy-first\u2014centered on risk and rights, aimed at protecting humans from AI\u2019s harms. Both leave out the bigger question\u2014and the bigger opportunity\u2014which is how to combine human and artificial intelligence to unlock new forms of collective intelligence.<\/p>\n

Some colleagues of mine have suggested reframing generative AI as\u00a0\u201cgenerative collective intelligence<\/a>,\u201d or GenCI, because at its core, there\u2019s a human story throughout. Foundation models are trained on the human collective intelligence embedded across the internet. They\u2019re refined through reinforcement learning with human feedback, hours of human labor spent curating data, training, and conditioning these systems. Even after deployment, much of their improvement comes from ongoing human user feedback. At every stage, humans are part of the value chain.<\/p>\n

Yet, that story is not being elevated and articulated in public discourse or policy debate. If we position these frontier AI systems correctly, they can elevate and amplify human potential in teams, in organizations, and in communities. Yes, there may be labor market disruptions and creative destruction, but there\u2019s also the possibility of new ways of working and expanding human potential. That\u2019s the part of the conversation we need to develop and elevate with innovative approaches and the right policy incentives.<\/p>\n

\t\t\tWhen humans and AI team up: Vibe teaming defined<\/p>\n

Junjie Ren: Let\u2019s shift to vibe teaming, a term you coined with Kershlin Krishna. What is it? How does it work in practice, and how does it differ from traditional prompt and response or copilot models?<\/p>\n

Jacob Taylor: Vibe teaming<\/a> is a new approach to what we call human-human-AI collaboration. It\u2019s a way to combine AI tools with human teamwork to create better outputs. In our case, we\u2019ve been exploring its application to challenges embedded in the SDGs, asking: How could a new model of human-AI teaming help advance progress on something like ending extreme poverty globally?<\/p>\n

The idea came from \u201cvibe coding<\/a>,\u201d a term popularized earlier this year by software engineer Andrej Karpathy. He described a workflow where he talks to an AI model describing the \u201cvibe\u201d of an idea for a software product and the model produces the first draft. The human expert then iterates on the first draft with the model\u2014giving feedback on bugs or tweaks\u2014until the product is complete. The process is quick, conversational, and low-friction, with the AI handling much of the lower-level work.<\/p>\n

We wondered: What if we did this collaboratively? So Kershlin and I sat down together in front of a phone, talked through what we wanted to create (in this case, a PowerPoint presentation) and ended up with a 20-minute transcript. We fed that into our AI model, and it quickly produced a draft presentation. That was the starting point for vibe teaming, and it felt like we were onto something.<\/p>\n

Pairing decades of human expertise with AI\u2019s speed feels like a special sauce worth understanding.<\/p>\n

Jacob Taylor<\/p>\n

\t\t\tWhen world-class strategy takes hours, not years<\/p>\n

Junjie Ren:\u00a0Walk us through a concrete use case\u2014like the SDG 1.1 experiment with Homi Kharas<\/a>?<\/p>\n

Jacob Taylor:\u00a0We wanted to test vibe teaming on a real outcome, and we brought in our colleague Homi\u2014a leading expert on global poverty eradication\u2014and asked: What if we used this approach to design a global strategy for ending extreme poverty by 2030?<\/p>\n

In a single 90-minute session, we produced what we considered a \u201cBrookings-grade\u201d strategy\u2014high enough quality to publish, which we did, along with a related blog<\/a>. Our 17 Rooms team spent a fair amount of time thinking about what sequence of questions might get the most out of an expert conversation. Then the process was straightforward: start with rich human input, in this case a 30-minute recorded conversation with one of the world\u2019s leading thinkers on global poverty. Feed that transcript into our customized AI models. Then engage in a careful, iterative process of human review and validation\u2014you were part of that, Junjie\u2014to refine the output for publication.<\/p>\n

The AI played a supportive role, handling tasks like transcription and first-draft generation, but the quality came from the depth of the human input and the decades of expertise behind it. Homi has been working in this space for over 40 years; we were drawing on his lifetime of insight and combining it with our own. Pairing that kind of wisdom with AI\u2019s speed in iterating, automating, and structuring outputs feels like a \u201cspecial sauce\u201d worth understanding.<\/p>\n

Junjie Ren: What\u2019s next for vibe teaming? Is it validation or scaling?<\/p>\n

Jacob Taylor:\u00a0So far, we\u2019ve had positive engagement with the approach\u2014from AI teams at major U.S. automakers to government agencies around the world, and of course our colleagues here at Brookings, who are excited to experiment with this approach. We think it could become a practical tool for helping people integrate AI into the knowledge work they\u2019re already doing.<\/p>\n

Since these initial tests, we\u2019ve been exploring how to scale up and validate the approach in different contexts. On one hand, that means bringing more people into policy conversations to inform the strategies and outputs that come from processes like this. On the other, it means testing whether the method itself can be validated as a source of enhanced collaboration, creativity, and even team flow\u2014relative to more individual work or other team formats.<\/p>\n

\t\t\tWhy \u2018team human\u2019 still matters<\/p>\n

Junjie Ren:\u00a0In policymaking spaces, where AI can already synthesize, summarize, and even simulate, what exactly is the role of humans?<\/p>\n

Jacob Taylor:\u00a0There are a few parts to that. Big picture, what we were able to produce in 90 minutes (or a few hours total) was, by all accounts, world-class work. One of our Brookings colleagues thought it compared favorably with anything the World Bank has published on the topic. That raises big questions: If a small group of humans, plus AI, can produce something like this so quickly, what does that mean for large institutions and the traditional process of knowledge creation?<\/p>\n

This could signal an early disruption to policymaking. AI isn\u2019t replacing knowledge creation, it\u2019s an amplifier handling lower-level work (transcribing, drafting) so humans can focus higher up the value chain: judgment, collaboration, decisionmaking, brainstorming, creativity.<\/p>\n

That shift frees up capacity for the real game, which is building the architectures that let people work across silos, translate between institutional languages, and act collectively on big challenges. In our team\u2019s anecdotal experience, through vibe teaming, we\u2019re already spending less time buried in spreadsheets or documents and more time in conversation and quality control.<\/p>\n

Junjie Ren:\u00a0What does success look like in practice when AI is a cognitive amplifier and not a replacement of humans?<\/p>\n

Jacob Taylor:\u00a0Success is when we can measure human-AI collaboration actually improving collective intelligence. The science here is advancing fast. We can now identify causal mechanisms of collective intelligence in groups, ecosystems, and organizations.<\/p>\n

One simple framework breaks it into three components: collective memory (what we know together), collective attention (what we\u2019re focused on together), and collective reasoning (what we have the potential to act on together). The question is: Can we use these factors to assess the outputs of human-AI systems? Can we say, \u201cthis collaboration increased our collective attention on a problem\u201d or \u201cthis process expanded what we know together\u201d?<\/p>\n

That\u2019s the next frontier: tying experiments with these tools directly to measurable outcomes, especially on real-world challenges like the SDGs, so it\u2019s not just novel process, but progress we can track and prove.<\/p>\n

\t\t\tHuman embodiment and cognitive atrophy<\/p>\n

Junjie Ren:\u00a0You\u2019ve talked about cognitive atrophy as a risk. How do we guard against this trend in high-AI environments? <\/p>\n

Jacob Taylor:\u00a0Obviously, with any new technology like this, humans and technology co-evolve, and cognition co-evolves. We are going to see atrophy in certain skills overall, and this is a particular risk for younger staff<\/a> entering the workforce, or younger folks who are earlier in their skill development for knowledge work.<\/p>\n

But there\u2019s also the opportunity to develop new cognitive competencies, skills, and attributes. Human-AI interaction\u2014vibe coding, vibe teaming\u2014is, over time, going to become a new muscle in itself, a bit like writing or reading, with its own set of commands. So there\u2019s a balance to strike here: What needs protecting, and what we should lean into. In that spirit, I\u2019m very much a \u201cteam human\u201d kind of guy in the age of AI, and what is most human, meaningful, and core to us is our embodiment.<\/p>\n

Junjie Ren: Do you see embodied practices (such as Tai Chi, which you are known to lead at our staff retreats) having an active role in shaping how we design and interact with technologies like AI?<\/p>\n

Jacob Taylor: You know, the fact is that we\u2019re in a physical body, and we use that to navigate the world, relate to others, and cultivate energy, creativity, and connection. I think that coming back, literally, to the in-breath and the out-breath that we as biological creatures have uniquely, and can share with others, is key to grounding the human ingredients in the AI story.<\/p>\n

Frankly, I think we\u2019ll see that being human is going to matter more than ever in an age of AI. It\u2019s going to force us to really clarify what being human really means. For the hopeful among us, it\u2019s time to really speak out for what those human characteristics are. I think a lot of them are embodied in our most visceral, grounded practices that we enjoy together in community with others.<\/p>\n

\t\t\tOne big takeaway<\/p>\n

Junjie Ren: Last question, if you were talking to a policymaker or an NGO leader or a CEO tomorrow, what is the one principle of vibe teaming you think they should try?<\/p>\n

Jacob Taylor: Yeah, there\u2019s no free lunch. It\u2019s the basic upshot with AI, I think. Humans shape the inputs and outputs of AI systems at every step. With this in mind, it\u2019s so important to capture and elevate what makes us human\u2014ingredients of shared purpose, story, motivation, and priorities\u2014and build hybrid human-AI systems and tools with these ingredients as starting points.<\/p>\n

The Brookings Institution is committed to quality, independence, and impact.
We are supported by a diverse array of funders<\/a>. In line with our values and policies<\/a>, each Brookings publication represents the sole views of its author(s).<\/p>\n","protected":false},"excerpt":{"rendered":"Artificial intelligence dominates today\u2019s headlines: trillion-dollar productivity forecasts, copyright lawsuits piling up in court, regulators scrambling to tame…\n","protected":false},"author":2,"featured_media":144571,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45],"tags":[182,966,181,507,14091,24986,74381,4253,3200,89776,19593,24987,89777,89778,23394,89779,74,14095,19594,3657],"class_list":["post-144570","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-article","tag-artificial-intelligence","tag-artificialintelligence","tag-business-workforce","tag-center-for-sustainable-development","tag-climate-energy","tag-climate-change","tag-commentary","tag-development-financing","tag-global-economy-development","tag-global-economy-and-development","tag-multilateral-development-organizations","tag-social-equity-inclusion","tag-society-culture","tag-sustainable-development-goals","tag-technology","tag-technology-information","tag-technology-policy-regulation","tag-u-s-economy"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/144570","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/comments?post=144570"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/144570\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media\/144571"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media?parent=144570"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/categories?post=144570"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/tags?post=144570"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}