{"id":"3f17ff28-3e4d-4a6b-adc1-523db484ec6f","arxiv_id":"2502.07828","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":2.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper contends that AI cannot reach AGI while humans supply the problem structure, and benchmarks cannot prove generality because correct answers do not reveal the solver's method.","lead":"A position paper argues that current and foreseeable generative AI cannot reach artificial general intelligence because humans still supply the problem structure, architecture, and training data. It warns that benchmarks cannot certify generality, because passing a test does not reveal the method used to pass.","discovery_kind":"review","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The paper's key premise that insight problems cannot be solved by step-by-step procedures is asserted without proof and is contradicted by its own mutilated checkerboard example, which admits algorithmic solution.","rationale":"The paper's central claim is that current and foreseeable GenAI models cannot achieve AGI because of anthropogenic debt and an inability to solve insight problems. The most concrete, testable assumption is the claim about insight problems. The reader flagged this same assumption. I agree. The paper states it as a general fact but provides no formal argument; its own mutilated checkerboard example is known to be decidable by brute-force/SAT algorithms (Heule et al., 2019), which are step-by-step procedures. Thus the key premise is empirically false under the paper's own definition. Without this premise, the argument reduces to an unsupported prediction about future inventions. The paper's benchmark critique (affirming the consequent) is logically sound and valuable, but it does not establish the negative thesis. Therefore the REJECT verdict stands. I recommend UNCHANGED because my concern reinforces the reader's rejection rather than changing it.","tokens_in":13515,"tokens_out":4607,"duration_ms":39502,"concrete_test":"Implement a program that, given any checkerboard size n and any pair of opposite-color missing squares, decides tiling existence by exact cover or SAT and produces a certificate (e.g., a DRAT proof) without using the coloring invariant. Run it on instances larger than humans solve by insight. If it succeeds for all instances, the premise that insight problems cannot be solved by step-by-step procedures is false. A secondary check: present a novel insight problem to a current LLM with no web-visible solution and give it access to a search tool; if it can switch strategies and solve the problem, the 'cannot switch' claim also fails. The SAT check is decisive for the load-bearing premise.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing premise is the assertion, in the discussion of insight problems, that 'insight problems cannot be solved by a step-by-step procedure, like an algorithm.' The paper relies on this to argue that current GenAI models, which operate by gradient descent and pattern completion, cannot handle a necessary class of human problems and therefore cannot achieve AGI. But the assertion is not proven and is contradicted by the paper's own example. The mutilated checkerboard can be decided by a brute-force enumeration of domino tilings or by an exact-cover/SAT solver; Heule, Kiesl, and Biere (2019), cited in the paper, give clausal proofs for exactly this problem. That is a step-by-step, algorithmic solution. The paper tries to distinguish 'specific instances' from 'general solution,' but if the general solution means a compact insight like the coloring argument, that is a statement about human problem-solving style, not about computability. No formal problem class, complexity bound, or impossibility theorem is offered. The dichotomy between 'insight problems' and 'algorithmically solvable problems' is therefore unsupported. Since this dichotomy is the main technical support for the negative AGI thesis, the central claim fails.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper argues that current and foreseeable generative AI models cannot achieve artificial general intelligence (AGI) because they carry 'anthropogenic debt': humans supply problem structure, representations, training data, prompts, and evaluation criteria, leaving only parameter adjustment to the model. It further claims that a necessary class of human problems, 'insight problems,' cannot be solved by step-by-step algorithmic procedures, and that benchmarks cannot provide evidence for general intelligence because success on a test does not reveal the method of solution. The author concludes that AGI will require as-yet-undiscovered inventions, that current risk assessments are overstated, and that a theory of general intelligence is needed.","tokens_in":13739,"tokens_out":4736,"duration_ms":43929,"significance":"The paper contains one genuinely sound and useful logical point: observing benchmark success does not license an inference about the mechanism that produced it, so claims of emergent reasoning from language models require additional evidence. The discussion of the Goh et al. (2024) diagnostic study is a good case study of how human prompt-engineering can be conflated with model competence. However, the central negative thesis—that no foreseeable GenAI model can achieve AGI—is not established. It rests on an unsupported and in fact contradicted dichotomy between insight problems and algorithmic solvability, plus a definitional autonomy criterion that makes part of the conclusion true by stipulation. The paper offers no formal theorem, no impossibility proof, and no machine-checked argument; it is an opinion essay with illustrative examples. Because the load-bearing supporting claims fail, the main contribution is not substantiated.","major_comments":[{"comment":"The assertion that 'insight problems cannot be solved by a step-by-step procedure, like an algorithm' is load-bearing, since it supports the claim that a necessary class of human problems lies beyond current computational methods. But the author's own example undermines it: the mutilated checkerboard is decidable by brute-force enumeration of tilings or by an exact-cover/SAT solver, and the author himself cites Heule, Kiesl, and Biere (2019), who provide clausal proofs for exactly this problem. The distinction between 'specific instances' solvable by brute force and 'general solutions' requiring insight does not repair the argument: if 'general solution' means a compact insight such as the coloring argument, that is a claim about the style of human problem-solving, not a claim about computability. No formal problem class, complexity bound, or impossibility theorem is offered, so the central dichotomy remains unsupported.","section":"Insight problems and the mutilated checkerboard"},{"comment":"The argument moves from 'current GenAI models depend on human-provided structure' to 'they cannot achieve AGI' without a justifying principle. The author acknowledges that AGI 'may be achievable' with future inventions, which is compatible with the possibility that future GenAI-based systems overcome anthropogenic debt. To establish impossibility, the paper would need to show that such debt is ineliminable in principle for any foreseeable model; the marathon metaphor and the Domingos equation illustrate current dependence but do not supply such a proof. The leap from 'humans currently solve the hard parts' to 'models cannot ever learn to solve those parts' is a non sequitur.","section":"Current AI models suffer from anthropogenic debt"},{"comment":"Part of the conclusion follows from the paper's definitional choices rather than from empirical evidence. The claim that models 'cannot be autonomous, cannot escape control, and cannot be generally intelligent' assumes that general intelligence requires autonomy from all human-provided structure. Since the author defines the goal as covering 'the full range of human problem solving,' a model that fails the autonomy criterion fails his definition by construction. The paper should explicitly separate the definitional claim from the empirical claim and argue why this particular autonomy criterion is the correct one for AGI, rather than treating it as self-evident.","section":"Definition of general intelligence and autonomy"},{"comment":"The account of affirming the consequent in evaluating benchmarks is logically correct, but the conclusion that benchmarks 'are of no value' is overly strong. A benchmark can provide evidence about performance while remaining agnostic about mechanism; the fallacy arises only when one additionally asserts a mechanism of success. This overstatement weakens an otherwise valid epistemological point and makes the section vulnerable to the obvious reply that progress on well-designed held-out benchmarks still offers weak but non-zero evidence. The argument as written conflates 'cannot prove mechanism' with 'cannot indicate progress.'","section":"Benchmarks are unsuitable measures of general intelligence"}],"minor_comments":[{"comment":"There are numerous typographical errors, including 'Hoftsatadter' for Hofstadter, 'provied' for provided, 'develope' for develop, 'Ardilla' for Ardila, 'Modei' for Amodei, and 'Dominos' for dominoes.","section":"Throughout"},{"comment":"The text refers to 'Figure 1' as an illustration of ARC-AGI tasks, but no actual figure is included; the reference is only a URL. The figure should be inserted or the citation should be made inline.","section":"Figure 1 reference"},{"comment":"Several reference entries are incomplete or inconsistently formatted; for example, the Heule, Kiesl, and Biere entry lacks full publication details, and the Morris et al. entry is cited as 2024 in the text but 2025 in the reference list.","section":"References"},{"comment":"The author writes that 'a full checkerboard has over 12 million ways to arrange the dominos'; the exact number is 12,988,816, so the statement is true, but a precise citation or exact count would be more scholarly.","section":"Mutilated checkerboard statistics"}],"recommendation":"reject","confidential_remarks":"The manuscript is an opinion essay rather than a technical research contribution. Its strongest point, the critique of benchmark-based inference, is valid but not new. The paper's central claim—that no foreseeable GenAI can achieve AGI—rests on an unsupported assertion about insight problems that is contradicted by the author's own cited literature, and on a definitional autonomy criterion. Given the load-bearing nature of these flaws, I do not see a revision path within the manuscript's current scope that would establish the advertised thesis. If the journal publishes perspective pieces, a substantially rewritten, more modest essay might be considered, but the present version does not meet the bar."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Thanks for the report. I read the paper with the stress-test in hand. My take: it is a plainly argued position paper that does a good job assembling existing critiques, and the benchmark point—success does not identify method—is valid and worth restating. But the negative AGI thesis is not established; it leans on an assertion about insight problems that the paper neither proves nor defends against the algorithmic counterexamples it itself cites.\n\nWhat the paper does well: the affirming-the-consequent argument is clean. Passing a test does not tell you how the test was passed, and the discussions of Winogrande and ARC-AGI illustrate that fairly. The 'anthropogenic debt' framing is a useful label for human scaffolding in current models, though it comes from his own earlier work and is not new. The paper is also honest in scope: it explicitly says a full theory of general intelligence is missing and that progress may be discontinuous. No empirical or formal result is claimed.\n\nThe soft spot is load-bearing. The premise that 'insight problems cannot be solved by a step-by-step procedure, like an algorithm' is asserted, then supported only by the mutilated checkerboard example. As your stress-test notes, that problem has known algorithmic solutions, including the clausal proofs of Heule, Kiesl, and Biere (2019), which the author cites. The paper's reply—distinguishing brute-force instance solving from a general insight—is a distinction about how humans prefer to solve problems, not a computability boundary. No complexity bound, no formal problem class, no impossibility theorem is offered. 'There is no known method' is not 'cannot be solved by an algorithm.' So the central argument fails as stated.\n\nThere is also some definitional slippage: AGI is defined as autonomy from human-provided structure, and then models that rely on human structure are concluded to be not AGI. That is partly true by stipulation, not discovery.\n\nThat said, this is a serious, well-written opinion piece. The benchmark critique is sound and the paper engages the literature thoughtfully. It is not incoherent on its own terms. I would not desk-reject it: the topic is important enough and the benchmark argument sharp enough to deserve referee time. But I would expect a referee to identify the unsupported insight-problem claim as a major obstacle, and the paper would likely need substantial revision or a narrowed scope (a critique of benchmarks rather than a proof of impossibility) before publication. My own verdict would be reject as a research contribution, accept as a viewpoint piece with caveats.","headline":"A coherent synthesis of familiar AI-evaluation critiques whose central claim that GenAI cannot reach AGI rests on an unsupported distinction about insight problems.","tokens_in":14233,"tokens_out":2596,"would_cite":false,"duration_ms":24618,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Current and foreseeable generative AI models cannot achieve general intelligence, because humans still solve the hard parts of every problem.","keywords":["artificial general intelligence","anthropogenic debt","large language models","insight problems","ill-structured problems","benchmarks","affirming the consequent","problem solving"],"falsifier":"A concrete experiment would give a current model a brand-new insight problem whose solution does not appear anywhere in its training data—for example, a mutilated checkerboard variant where the two removed corner squares are the same color, so that the parity/coloring argument must be discovered rather than retrieved—and require the model to produce the correct 'no tiling' answer with a justification. If the model solves it without any human-provided hint, the paper's claim that GenAI models cannot solve insight problems fails; if it fails, the claim is supported.","tokens_in":13319,"feed_emoji":"🤖","tokens_out":5831,"duration_ms":48965,"temperature":0.7,"pith_summary":"The paper argues that current and foreseeable generative AI models are not on a path to artificial general intelligence because they carry what it calls 'anthropogenic debt': humans supply the problem framing, representation, architecture, training data, prompts, and evaluation criteria, leaving the model only parameter adjustment by gradient descent. Because models recast every task as a language-pattern prediction problem, they cannot autonomously handle ill-structured problems or 'insight problems' whose solutions require recognizing a new way to organize the problem rather than following a step-by-step procedure. The paper also claims that standard benchmarks cannot settle whether a model is generally intelligent, since observing a correct answer does not reveal the method that produced it, and inferring a method from success is the logical fallacy of affirming the consequent. A sympathetic reader would care because if the claim is right, scaling current models and passing benchmarks will not deliver AGI; new inventions and discoveries would be required, and safety regulations and investments based on imminent AGI would be aimed at the wrong target.","feed_headline":"AI can't reach general intelligence by scaling alone","feed_subtitle":"Anthropogenic debt is the hidden human labor behind every AI benchmark score.","key_machinery":"The argument runs on the distinction between well-structured and ill-structured problems, taken from Simon's analysis of problem solving. A well-structured problem specifies a test for solutions, a problem space of states and moves, and a goal; these are the problems current AI can solve once a human supplies the structure. Ill-structured and 'insight' problems, illustrated by the mutilated checkerboard, have no predefined space or stepwise test, and solving them requires restructuring one's approach (the 'coloring argument' that each domino covers one square of each color). The paper's central mechanism is anthropogenic debt: a ledger of all the human contributions—representations, architecture, training tasks, prompts, labels, evaluation metrics—that remain invisible when a model's success is credited to the model. Because these contributions are not part of the model's own computation, the paper argues, benchmark scores cannot distinguish model intelligence from human intelligence embedded in the setup.","core_discovery":"The central claim is that 'current and foreseeable GenAI models are not capable of achieving artificial general intelligence because they are burdened with anthropogenic debt.' The paper maintains that nearly all conceptually difficult parts of problem solving—defining the problem, choosing representations, designing the network, curating training data, writing prompts, and deciding what counts as success—are done by humans, while the model contributes only parameter adjustments. As a result, a model's apparent competence is largely a measure of how much structure humans have already imposed. General intelligence, by contrast, requires autonomy: the system must frame ill-structured problems, create its own problem space, discover insight-style solutions that cannot be reached by stepwise search, and verify its own answers. The paper further contends that no test or benchmark can certify such generality, because success on a test is compatible with many mechanisms—memorization, classification, or genuine reasoning—and inferring the mechanism from the outcome is affirming the consequent.","pith_inferences":["One testable extension of the paper's argument is to measure 'anthropogenic debt' quantitatively, for example by degrading the human-supplied components—prompt quality, problem framing, representation choices—and observing how much model performance drops; the paper predicts steep drops on ill-structured tasks.","The affirming-the-consequent critique applies beyond benchmarks: it also undercuts inferences from 'emergent abilities' in large language models, since observed behavior alone cannot distinguish a learned general capacity from training-set memorization.","If insight problems truly resist step-by-step methods, then hybrid systems that combine language models with external search, theorem provers, or symbolic planners would still inherit the debt of having the insight supplied by the tool designer; the paper's argument implies the models would need to generate new representations themselves.","A practical corollary for safety research: if the paper is right, the near-term risk profile shifts from autonomous superintelligent systems to humans over-trusting systems that are 'stupid' in ways the benchmarks conceal."],"forward_implications":["Scaling today's language models, training data, and compute will not by itself produce artificial general intelligence; overcoming anthropogenic debt requires new inventions and discoveries.","Benchmark results, including high scores on ARC-AGI, Winogrande, and similar tests, do not establish general intelligence, because the same score could be produced by memorization, task-specific shortcuts, or human-structured problem simplification.","Current models cannot be considered autonomous or able to 'escape control' in the way AGI alarm scenarios assume, because they depend on humans for the difficult parts of each problem.","Human intelligence tests and aptitude tests are invalid for assessing machine intelligence, since the model's 'experience' and vocabulary are designed by its creators rather than acquired in a human-like way.","Progress toward AGI is unlikely to be measurable on a smooth scale; it may come discontinuously through insights that cannot be predicted in advance."],"supporting_citations":[{"why":"Provides the well-structured/ill-structured problem distinction that carries the argument that general intelligence must handle ill-structured problems.","marker":"Simon, 1973"},{"why":"Supplies the 'search for a light switch' metaphor and the analysis of insight problems as requiring restructuring rather than stepwise search.","marker":"Kaplan and Simon, 1990"},{"why":"Supplies the mutilated checkerboard example and the coloring argument used to show that insight problems have non-algorithmic solutions.","marker":"Black, 1946; Heule, Kiesl, & Biere, 2019"},{"why":"Gives the Learning = Representation + Evaluation + Optimization formula used to identify which parts of machine learning are human-supplied.","marker":"Domingos, 2012"},{"why":"Is the ARC-AGI benchmark that the paper uses to show benchmark success cannot certify general reasoning because the same score could come from human labeling.","marker":"Chollet, 2019"},{"why":"Argues that producing correct conversational behavior is not sufficient to establish intelligence, grounding the paper's critique of behavioral tests.","marker":"Block, 1981"},{"why":"Shows how Bongard problems are simplified into closed classification tasks, illustrating that the intelligence in the solution often comes from the developer.","marker":"Kharagorgiev, 2018"},{"why":"Documents the many definitions of intelligence, supporting the paper's claim that no plausible theory of general intelligence currently exists.","marker":"Legg and Hutter, 2007"}],"fun_headline_variants":["AI's hidden human labor blocks AGI","Anthropogenic debt: the real barrier to AGI","GenAI can't achieve AGI: humans do the hard thinking","AGI impossible: models only adjust what humans design","Benchmark scores hide AI's lack of autonomy"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The argument rests on the assumption that insight problems cannot be solved by a step-by-step procedure, such as an algorithm; if even one such problem can be solved by systematic search, the claim that a necessary class of human problems lies beyond current computational methods loses its main support.","fun_headline_variants_meta":{"raw":{"variants":["AI's hidden human labor blocks AGI","Anthropogenic debt: the real barrier to AGI","GenAI can't achieve AGI: humans do the hard thinking","AGI impossible: models only adjust what humans design","Benchmark scores hide AI's lack of autonomy"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000181,"raw_usage":{"total_tokens":1291,"prompt_tokens":912,"completion_tokens":379,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":528,"completion_tokens_details":{"reasoning_tokens":302}},"tokens_in":528,"tokens_out":379,"duration_ms":3905,"temperature":1.0,"reasoning_tokens":302,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-08T14:03:56.088476+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A concrete experiment would give a current model a brand-new insight problem whose solution does not appear anywhere in its training data—for example, a mutilated checkerboard variant where the two removed corner squares are the same color, so that the parity/coloring argument must be discovered rather than retrieved—and require the model to produce the correct 'no tiling' answer with a justification. If the model solves it without any human-provided hint, the paper's claim that GenAI models cannot solve insight problems fails; if it fails, the claim is supported.","supporting_citations":[],"review_version":1}