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<\/p>\nThe most dangerous thing about today’s AI isn’t that it gives wrong answers \u2014 it’s that it gives safe ones. Users who regularly accept AI writing suggestions, according to Amazon AGI Lab cognitive scientist Danielle Perszyk, often begin with one argument and end up defending the opposite position, “below their threshold of awareness.” The shift happens cumulatively, invisibly, as the model nudges them toward what she describes as “regression to the mean, safest answers.” Speaking on the Latent Space podcast, Perszyk delivered a sweeping critique of the AI industry’s current trajectory \u2014 and a detailed alternative that Amazon is quietly funding inside a startup-style lab insulated from product deadlines.<\/p>\n
The Invisible Cost of AI Assistance<\/p>\n
Perszyk’s argument is not that AI fails at its stated tasks. It succeeds too well \u2014 and that’s the problem. When AI optimizes for “getting the right answer,” it consistently shrinks the space of human thought rather than expanding it.<\/p>\n
The evidence she marshals falls into two domains. First, writing. Studies now show that users who accept AI-generated suggestions experience a drift in their own positions. The model offers the statistically safest, most consensus-friendly phrasing, and cumulative acceptance leads to an argument the user never consciously chose. Second, and more strikingly, science. At a Northwestern University workshop earlier in 2026, researchers reported a troubling pattern. As host Swyx summarized: “Individual scientists who are using AI tools are benefiting because they’re producing more papers, they’re getting more grants accepted, but science as a whole is narrowing.”<\/p>\n
The mechanism is the same across both cases. Models trained on compressed, monocultural internet data impose a single way of thinking. The result is a three-way squeeze on human originality.<\/p>\n
DomainObserved EffectMechanismWritingUser’s argument silently shifts to AI-neutral outputCumulative acceptance of safe suggestions below awarenessScienceHigher individual output, but field-wide homogenizationModels trained on compressed, monocultural internet dataGeneral knowledge workCurrent AI products built for engineers, not diverse usersFast feedback loops on verifiable tasks reinforce narrow capabilities<\/p>\n
The diagnosis cuts deeper than a product complaint. Perszyk argues the industry has made a category error at the level of what the system is trying to achieve. Optimizing for task completion \u2014 getting the right answer \u2014 is trivially reward-hacked. As Goodhart’s law predicts, the measure becomes the target, and the target drifts away from what actually matters.<\/p>\n
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Why Intelligence Is a Team Sport<\/p>\n
“If the AI is optimized to reconcile the errors between how it understands the topic and how you do, it will be motivated to help you understand. It will not let you get away with automatic offloading.”<\/p>\n
Perszyk grounds her alternative in a concept from anthropology: the collective brain. No human survives alone, and intelligence \u2014 real, generalizable intelligence \u2014 doesn’t exist inside a single skull. “There’s this category error,” she noted. “It doesn’t exist in individual humans. It emerges from our interactions.” Innovation depends on population diversity and interconnectivity. Variation drives progress.<\/p>\n
This reframes the entire AI enterprise. Instead of building monolithic models that converge on the same safe answers, Perszyk argues for diverse societies of AIs \u2014 systems with different biases and perspectives, whose interactions produce emergent norms rather than pre-programmed behavior. The developmental analogy is crucial: human infants don’t optimize for task completion. They optimize for inferring and aligning with other minds. That foundational drive, she argues, is what makes human intelligence generalizable across arbitrary environments.<\/p>\n
Swyx pushed back with an important caveat: “Maybe we don’t want to grow these AIs exactly like human.” Perszyk agreed, drawing on David Marr’s levels of analysis to clarify. She’s not arguing for neural plausibility at the implementation level. She’s arguing for changing the computational goal \u2014 what the system is fundamentally trying to do. That goal should be “aligning its representations with ours,” from which flexible reasoning and generalization can emerge. “In a sense,” she said, “alignment is the solution, not the problem, for building AI that gives us more agency.”<\/p>\n
Redefining Reliability: From Clicking Buttons to Modeling Minds<\/p>\n
The most concrete illustration of this shift comes from Amazon’s own product evolution. The lab’s first public output was Nova Act, an SDK for atomic computer manipulations \u2014 clicking, scrolling, navigating interfaces. It was, Perszyk acknowledged, a pragmatic step: “meet the models where they are.”<\/p>\n
But the lab has already moved beyond that narrow framing. As Swyx put it: “Ultimately reliability has less to do with clicking in the same place and scrolling and more to do with modeling the user’s mind. And that shift is everything.” The insight is that human goals aren’t static targets a computer can hit with pixel-perfect precision. “The interaction with the computer is shaping and refining the way we are thinking about the goal itself,” Perszyk explained. “So the goal is unfolding over time.”<\/p>\n
This is a fundamentally different conception of what an AI agent should be. A perception agent \u2014 Perszyk’s term \u2014 doesn’t just parse a command and execute it. It tracks how the user’s intentions evolve during the interaction itself. It models not just what you asked for, but what you’re actually trying to accomplish, and adjusts as that understanding sharpens. The distinction sounds subtle. In practice, it’s the difference between a tool that follows orders and one that helps you figure out what you really want.<\/p>\n
The Whack-a-Mole Problem<\/p>\n
Why hasn’t the industry already built this? Because the dominant training paradigm \u2014 reinforcement learning on narrowly defined tasks \u2014 actively works against it.<\/p>\n
“You can use reinforcement learning to get models really good at specific tasks,” Perszyk observed, “but then it is not good at another task or it doesn’t generalize. It’s kind of like whack-a-mole.” Each optimization for one capability degrades performance elsewhere. The model becomes a collection of brittle specializations rather than a system that understands what it’s doing.<\/p>\n
This is not a temporary limitation. It’s a structural consequence of optimizing for correctness on verifiable, narrow tasks \u2014 coding benchmarks, Q&A accuracy, math problems. Those tasks produce fast feedback loops, which makes them attractive for product development. But they don’t require the system to model another mind. They reward answer-matching, not understanding. And the reward, over time, produces systems that are increasingly competent at a shrinking set of things.<\/p>\n
Four Technical Bets That Point in a Different Direction<\/p>\n
Perszyk’s lab is investing in four research areas that most of the industry isn’t prioritizing.<\/p>\n
First, real-time, full-duplex interaction. Today’s AI is stuck in turn-taking \u2014 you type, it responds, you type again. Humans constantly update their understanding in real time, listening and planning simultaneously. Perszyk pointed to early work from Thinking Machines, Q Tai Moshi, Flamingo, and her own lab as evidence that full-duplex is technically feasible. The challenge is making it the default, not a demo.<\/p>\n
Second, memory that goes beyond storage. Current AI treats memory as retrieval from a database. Humans have episodic memory integrated at multiple timescales \u2014 it’s core to learning, simulating the future, and taking perspective. The lab is working on agent architectures that incorporate different memory types, including inference-time contextualization that isn’t simply weight updates.<\/p>\n
Third, social world models. In AI research, “world models” usually means 3D-video generation \u2014 predicting what the physical world will look like. Perszyk means something different: models that, from the start, are about inferring how another mind interprets the world. This social grounding, she argues, is what allows humans to generalize across arbitrary environments.<\/p>\n
Fourth, multi-agent emergence. Current multi-agent systems use “precise orchestration, delegation, structured hand-offs” or game-theoretic cooperation and competition. These miss what makes human groups effective: roles that fluidly shift, negotiated meaning, emergent strategy, and durable culture. Open-source systems like OpenClaw show “nothing durable, no cumulative culture,” Perszyk noted. The missing ingredient is motivation \u2014 not to complete a task, but to affect other agents and change the state of the system itself.<\/p>\n
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Education: The Proof Case<\/p>\n
Perszyk’s most concrete illustration of what this approach unlocks comes from education. She invokes Bloom’s two-sigma problem \u2014 the persistent gap between classroom instruction and one-on-one tutoring. An AI optimized to align its representations with a student’s mind, she argues, would naturally resist passive offloading. If a student repeatedly asks for answers without understanding, the system \u2014 driven to reconcile the gap between its understanding and the learner’s \u2014 would spontaneously employ the Socratic method, probing comprehension rather than providing answers.<\/p>\n
This stands in stark contrast to current AI education products, which she characterizes as essentially advanced search engines. The Oxford tutorial system, where a tutor builds a model of a student’s understanding over an entire semester, provides the template. In Perszyk’s vision, students would produce “rich multimodal interactive experiences” as artifacts of learning, and other students would build on them. The cognitive friction that current AI smooths away \u2014 which is essential for encoding knowledge \u2014 would be restored.<\/p>\n
The broader point is that education isn’t just a nice application. It’s the test case for whether AI can be built to augment human agency rather than erode it. A system that helps you think more clearly is categorically different from one that thinks for you.<\/p>\n
The open questions are significant. How do you measure “representation alignment” in a way that replaces benchmark scores? Can emergent culture be engineered without reproducing human dysfunctions? And perhaps most pressing: does Amazon have the patience to fund a long-horizon research lab while competitors ship products and capture market share?<\/p>\n
The company is certainly spending like it believes the answer is yes. Amazon recently raised $25 billion in bonds to fund its AI infrastructure, part of a broader Big Tech borrowing spree that has pushed AI-linked debt past $220 billion this year. AWS just posted 28% revenue growth to $37.6 billion in the first quarter of 2026, with operating margins of 37.7%. Jeff Bezos, in a CNBC appearance, described AI as a “bulldozer” handed to someone digging a basement with a shovel \u2014 a tool for augmentation, not replacement. That framing aligns with Perszyk’s thesis, even as the company quietly winds down Mechanical Turk, the original “artificial artificial intelligence” that paid humans for tasks machines couldn’t yet handle. The shovel, symbolically, is being retired. The question Perszyk’s work raises is whether the bulldozer will be built to follow the operator’s intentions \u2014 or to drive itself.<\/p>\n","protected":false},"excerpt":{"rendered":"The most dangerous thing about today’s AI isn’t that it gives wrong answers \u2014 it’s that it gives…\n","protected":false},"author":2,"featured_media":102952,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[53411,6744,321,53410,3013,322,53415,53409,29827,364,53414,53412,58,157,53413],"class_list":["post-102951","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agi","tag-adept","tag-agi","tag-amazon","tag-amazon-agi-lab","tag-artificial-general-intelligence","tag-aws","tag-blooms-two-sigma-problem","tag-danielle-perszyk","tag-goodharts-law","tag-jeff-bezos","tag-mechanical-turk","tag-nova-act","tag-nvidia","tag-openai","tag-peter-desantis"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/102951","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=102951"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/102951\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/102952"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=102951"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=102951"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=102951"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}