{"id":"7467b8ac-b656-46e3-8ed4-137e032c5fe7","arxiv_id":"2501.14776","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A controlled benchmark of five languages and seven ML algorithms shows compiled languages (C++, Java) consume far less energy than interpreted ones, but implementation choice can outweigh language choice.","lead":"This study measured the energy consumed by seven common AI algorithms in five programming languages during both training and inference. It found that compiled languages like C++ and Java generally use far less energy than interpreted ones, though the specific algorithm implementation often matters more than the language itself.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The language ranking conflates language with the single library chosen per language; the paper's own per-algorithm data show the ordering is not stable across aggregation choices.","rationale":"The reader's weakest-assumption analysis already identifies the load-bearing issue: one library per language conflates language with implementation, and the paper's own Section V-C acknowledges that the construct is really implementation-level. My stress-test pass agrees with that reading and adds a concrete consequence from Table I: the aggregate training ranking is not robust even within the paper's own data, since Java beats C++ in more individual algorithms while C++ wins on the summed total. This confirms that the headline language ordering is sensitive to the arbitrary basket of algorithms and to the single-library-per-language choice. The study still has independent value as an honest benchmark of seven popular off-the-shelf AI libraries, and the per-algorithm table is the most reliable artifact. The conditional verdict is appropriate: the authors should restate the RQ answer as a statement about these specific library implementations rather than about programming languages in general, and should report variance or significance information alongside the descriptive medians. I do not see grounds to reject the paper outright, because the compiled-versus-interpreted trend is consistent across most algorithm cells and the authors explicitly flag the construct validity threat and promise a replication package.","tokens_in":10430,"tokens_out":6998,"duration_ms":72162,"concrete_test":"Run the same seven algorithms in Python with a second mainstream library (e.g., XGBoost or mlxtend) and in R with a second library (e.g., ranger for random forest and e1071 for SVC), keeping the same datasets, hyperparameter settings, and 30-run median protocol. If the within-language spread between libraries is of the same order as the reported Java-vs-C++ language gap, then the language-level ranking in Table I is not recoverable and the central claim must be restricted to 'these libraries.' As a cheaper analytical check, recompute the Table I language ranking by taking the median of the seven per-algorithm medians instead of summing them; if the training order changes, the headline ordering is an artifact of the chosen algorithm basket.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that programming languages have a considerable impact on AI energy consumption is not identifiable from this design, because each language is represented by exactly one library: scikit-learn for Python, mlpack for C++, WEKA for Java, caret for R, and the MATLAB Statistics Toolbox. The authors themselves concede in Section V-C that a more correct scoping would be 'which algorithm implementation choices in different programming languages are more energy efficient.' This is not merely a caveat: Table I shows that in training, Java is the most efficient implementation in 4 of 7 algorithms while C++ is most efficient in only 3, yet C++ is reported as 'far more efficient than Java' overall because the language totals are sums over an arbitrary basket of seven algorithm implementations. Single implementations dominate the aggregate: R logistic regression accounts for 71.1% of R's training total, Python SVC for 63.4%, and Java random forest for 52.5%. With exactly one library per language, any measured difference conflates language with that library's implementation quality, algorithmic defaults, and numerical routines. The paper's repeated framing of 'language X consumes less' is therefore not supported as a causal language effect; at best the data support a benchmark of these seven specific off-the-shelf implementations.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper reports a controlled empirical benchmark of the energy consumption of seven classical machine-learning algorithms (KNN, SVC, AdaBoost, decision tree, logistic regression, naive Bayes, random forest) as implemented in five languages (C++ via mlpack with AdaBoost from XGBoost, Java via WEKA with naive Bayes from OpenCV, Python via scikit-learn, MATLAB via the Statistics and Machine Learning Toolbox, and R via caret), across three UCI datasets (Iris, Breast Cancer, Wine Quality), for the training and inference phases. Each configuration is run 30 times on an Apple M2 and measured with CodeCarbon; medians are reported and totals are aggregated across datasets and algorithms. The authors report cumulative rankings (training: C++ < Java < MATLAB < Python < R, with R 37x above C++; inference: Java < C++ < Python < R < MATLAB, with a 54x maximum gap) and note that no language keeps its rank across phases. They further observe that single algorithm cells dominate language totals (e.g., R's logistic regression is 71.1% of R's training energy) and conjecture, as their most important finding, that algorithm implementation, rather than language per se, may be the dominant factor. The design, however, uses exactly one library per language, so the 'language' factor is confounded with the library and its implementation; the authors acknowledge this in Section V-C.","tokens_in":10580,"tokens_out":15138,"duration_ms":125477,"significance":"If the central claims could be supported, the paper would be a useful contribution to Green AI: it would quantify the spread among off-the-shelf AI implementations, show that the training-versus-inference phase changes the efficiency ordering, and provide an empirically grounded warning against holistic 'language X is most efficient' claims. The study has real strengths: a systematic design with 30 repetitions in a controlled environment, direct energy measurement through a kernel-level tool, per-algorithm and per-phase results in Table I, a replication package, and an honestly flagged construct-validity threat (Section V-C), with the headline insight explicitly labeled a conjecture (Section IV). The weaknesses are equally clear: the language effect is not identifiable with one library per language, the cumulative rankings are driven by a few outlier cells, no variance or inferential statistics accompany the medians, and some abstract/conclusion statements ('consistently consume less') are contradicted by the paper's own Table I.","major_comments":[{"comment":"The focal independent variable 'programming language' is confounded with the single library chosen to represent it: Python is represented only by scikit-learn, C++ only by mlpack (AdaBoost from XGBoost), Java only by WEKA (naive Bayes from OpenCV), R only by caret, and MATLAB only by its Statistics and Machine Learning Toolbox. Any measured difference therefore conflates the language with the implementation's algorithmic defaults, numerical routines, and optimization effort. The paper's own Section V-C states that a more correct scoping would be to understand which algorithm implementation choices in different programming languages are more energy efficient, which concedes the point. Yet the abstract and the answers to RQ1, RQ1.1, and RQ1.2 in Sections III and IV are phrased as causal language effects ('C++ is far more efficient than Java'; 'programming languages have a considerable impact on AI energy consumption'). The central claim of the paper is not identifiable from this design as stated. Please reframe the research question and headline claims to the level of the benchmarked language/library combinations, or include a second library per language for at least a few algorithms so the language effect can be separated from the implementation effect.","section":"§II-D, §V-C"},{"comment":"The cumulative language rankings are not robust to the choice of aggregation. In the training phase, Java is the most efficient implementation in 4 of 7 algorithms (KNN, SVC, decision tree, naive Bayes) while C++ leads in only 3, yet C++ is declared the overall winner because the cumulative sums are dominated by single outlier cells: R logistic regression contributes 71.1% of R's training total, Python SVC contributes 63.4%, and Java random forest contributes 52.5%. A majority-of-algorithms vote would invert the C++/Java ordering, so statements such as 'C++ is far more efficient than Java' are an artifact of the particular basket of algorithms. The paper should either present per-algorithm paired comparisons (languages compared within each algorithm and dataset) as the primary analysis, or add robustness checks such as per-algorithm ratios, jackknifing over algorithms, or median-of-ratios aggregation, before drawing any language-level ranking.","section":"§III-A, Table I"},{"comment":"Despite 30 repetitions per configuration, no measure of dispersion or inferential statistic appears anywhere in the paper; only medians and cumulative sums are reported. This is inconsistent with the contribution list's 'statistical and discursive interpretation' and with the claim in Section V-A that the 30-run design makes the study statistically resilient to random confounding factors. The headline contrasts (e.g., C++ vs Java in training, the 54x inference gap) should be accompanied by at least interquartile ranges or confidence intervals, and preferably a paired test over the 30 runs or a bootstrap for the ratio statements. Note also that Section V-A is headed 'Conclusion Validity' but its text addresses internal validity (history, maturation, instrumentation), so the classification of threats needs correction as well.","section":"§II-E, §V-A"},{"comment":"The abstract and the conclusions state that compiled and semi-compiled languages (C++, Java) consistently consume less than interpreted languages (Python, MATLAB, R). This is contradicted by Table I for the training phase: for KNN, C++ (2.67 x 10^7 J) consumes more than both Python (1.61) and R (0.65); for decision tree, C++ (12.21) consumes more than Python (10.57); for AdaBoost, Java (32.76) consumes more than R (5.27). The 'consistently' claim holds only for the cumulative totals, where it is driven by the outlier cells discussed above. Please replace 'consistently' with a statement that is true of the data, e.g., that the smallest cumulative totals in both phases are attained by C++ and Java.","section":"Abstract, §VII"}],"minor_comments":[{"comment":"The passage 'one could assume that linear regression is per se a very computational-intensive algorithm' appears to refer to logistic regression, and the accompanying claim that logistic regression consistently ranks second or third in training efficiency is not supported by Table I, where its training rank ranges from 3rd (C++) to 7th (R); please correct both the terminology and the rank statement.","section":"§III-A"},{"comment":"Spelling: 'naive bayses' should be 'naive Bayes', and §II-B opens with 'we consider tree different datasets' instead of 'three different datasets'.","section":"Abstract, §II-B, Table I"},{"comment":"The sentence 'Java consumes approximately two times less than the the second most energy efficient programming language (C++)' contains a duplicated 'the'.","section":"§III-B"},{"comment":"The dataset feature counts appear inconsistent with the UCI descriptions cited: Breast Cancer Wisconsin (Original) has 9 features (30 features belong to the diagnostic variant), and Wine Quality has 11 features rather than four; please verify the exact files used in the experiment, correct the text, and state the file versions in the replication package.","section":"§II-B"},{"comment":"The subsection is headed 'Conclusion Validity' but opens with 'In terms of internal validity' and mixes conclusion-validity and internal-validity threats; separate the categories under their correct headings.","section":"§V-A"},{"comment":"Reference [8] is listed as 'B. Pereira and et al.', which is inconsistent with the author list of [6] for the same body of work; complete the citation or merge it with [6].","section":"References"}],"recommendation":"major_revision","confidential_remarks":"The stress-test concern lands: with exactly one library per language, the headline causal claim about 'programming languages' is not identified, and the paper's own Section V-C concession plus the aggregation sensitivity visible in Table I mean the abstract cannot be defended as written. I do not regard this as a reject-level flaw because the measured data, the per-algorithm table, and the replication package are all in hand, and the revision path is clear: reframe RQ1 and the abstract to the language/library-benchmark level (or add a second library per language for identification), add dispersion or inferential statistics for the headline ratios, correct the internally contradictory 'consistently' claim, and fix the dataset description errors. If the authors decline to reframe, the manuscript should be repositioned as a benchmark report rather than a causal study. Related-work coverage is adequate, and I have no concerns about novelty disclosure."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe paper is an honest, useful empirical study, and the authors deserve credit for the parts that are easy to get wrong. They produce the first dataset I know of on energy consumption of classic ML algorithms across five languages, run 30 repetitions per cell, report medians, and ship a replication package. The phase-dependent ranking (C++ best in training, Java best in inference) and the 4x to 54x spreads are the kind of numbers that actually move deployment choices if they hold.\n\nThe main result, however, is not as clean as the abstract makes it sound. Each language is represented by exactly one off-the-shelf library. So \"Python consumes X\" really means \"scikit-learn consumes X\", and the aggregate language totals are sums over a small basket of seven algorithm implementations. The paper's own per-algorithm Table I shows Java has the most efficient implementation in 4 of 7 training algorithms, yet C++ is reported as overall most efficient because the totals are dominated by a few heavy cells — R's logistic regression alone is 71% of R's training total. That means the language ranking is not stable under reasonable aggregation choices, and the causal language effect is not identified.\n\nThe saving grace is that the authors know this. Section V-C explicitly concedes the construct validity threat and says the more correct scoping is \"which algorithm implementation choices in different programming languages are more energy efficient.\" They also label their most interesting claim — implementation matters more than language — as a conjecture, which is the right epistemic status. So this is not a bad paper with a hidden flaw; it is a good measurement campaign whose headline overstates what the design can support.\n\nWhat is missing for a stronger version: variance information and significance tests (30 repetitions are cheap, so report the spread), and a per-algorithm analysis that does not lean on the arbitrary basket totals. I would also want to see the replication package actually run before trusting the numbers, since I could not verify execution from the text.\n\nWho gets value: anyone working on Green AI or empirical software engineering, as a data point and as a teaching example of why \"language X is faster/greener\" claims need a confound check. It deserves a serious referee. I would send it to review, with the expectation that the language-effect framing gets reworked and variance data gets added.\n\nRecommendation: engage with it, but require the authors to restate the central claim as a benchmark of seven specific implementations, and to add dispersion measures.","headline":"A useful and honest measurement campaign whose headline language ranking overstates what the design can identify, because each language is a single library and the aggregate totals are driven by a few algorithm implementations.","tokens_in":11141,"tokens_out":1759,"would_cite":true,"duration_ms":17437,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A controlled experiment measuring five languages across seven AI algorithms finds that programming language choice can change energy consumption by up to 54x, with compiled languages consistently ahead.","keywords":["energy consumption","programming languages","green AI","machine learning","empirical software engineering","training and inference","energy measurement","algorithm implementation"],"falsifier":"Choose one algorithm, implement it in two different mainstream libraries within the same language, and re-run the same measurement protocol; if the within-language spread is as large as the 54x between-language spread, the language-level ranking collapses to an implementation-level result.","tokens_in":10203,"feed_emoji":"⚡","tokens_out":5328,"duration_ms":48865,"temperature":0.7,"pith_summary":"This paper tries to establish that the choice of programming language is a first-order factor in how much energy an AI workload consumes. In a controlled experiment covering five languages, seven classic machine-learning algorithms, and three tabular datasets, the authors measure training and inference energy separately. They find that compiled and semi-compiled languages (C++ and Java) consistently consume less than interpreted languages (Python, R, MATLAB), with up to a 37x spread in training and a 54x spread in inference. A sympathetic reader should care because this suggests concrete levers for making AI greener without abandoning familiar tools.","feed_headline":"AI energy use swings 54x by programming language","feed_subtitle":"Controlled tests of five languages and seven algorithms show C++ and Java beating Python, R, and MATLAB by large margins.","key_machinery":"The carrying mechanism is a controlled factorial experiment rather than a single mathematical identity. For each combination of five programming languages, seven off-the-shelf machine-learning libraries/algorithms, and three datasets, the authors run 30 repetitions, measure total hardware energy with an OS-level power-monitoring library, take the median, and sum over datasets; training and inference are isolated at the line-of-code level, and startup and compilation overhead are excluded. The rankings and the 37x and 54x ratios emerge from these cumulative medians.","core_discovery":"The paper's central claim is that programming languages have a considerable, measurable impact on AI energy consumption. Measured in joules on a single machine, cumulative training energy ranks C++ < Java < MATLAB < Python < R, with R consuming 37 times C++; cumulative inference energy ranks Java < C++ < Python < R < MATLAB, with MATLAB consuming 54 times Java. The rankings are not stable across phases or across algorithms, and no algorithm is intrinsically the most expensive in every language. The paper also argues, as a conjecture based on the per-algorithm breakdown, that the specific implementation of an algorithm may matter more than the language or the algorithm family itself.","pith_inferences":["Because each language is represented by one library, the 54x figure should not be quoted as a property of the languages alone; a same-language comparison across multiple libraries would isolate how much of the gap is implementation.","A testable consequence not pursued in the paper is that energy-efficient libraries written in compiled languages could be wrapped in Python to retain developer ergonomics while recovering much of the efficiency gap.","The per-algorithm variance suggests that energy-aware tooling could profile the specific library version in use, rather than relying on a language-level lookup table.","If the implementation-dominates conjecture is right, energy rankings of languages will drift as libraries update, so the result is a snapshot tied to particular library versions."],"forward_implications":["If the claim holds, developers can reduce AI energy use by favoring compiled or semi-compiled languages (C++ or Java) for deployment, especially for inference-heavy services.","If the claim holds, energy-efficiency rankings for AI should be reported separately for training and inference, since the order flips between phases.","If the claim holds, optimizing a single hot algorithm implementation in an interpreted language may yield bigger energy savings than switching languages.","If the claim holds, existing practices do not need to be upended: the trade-off between energy efficiency and development ease can be managed by keeping current languages and choosing efficient implementations.","If the claim holds, the energy cost of an AI algorithm is not intrinsic to the algorithm family, so Green AI benchmarks should report language and library context."],"supporting_citations":[{"why":"Establishes the general method of ranking programming languages by measured energy consumption, which this study extends to AI workloads.","marker":"[8]"},{"why":"Provides the energy, time, and memory relation and language ranking background for non-AI programs.","marker":"[6]"},{"why":"Closest prior work, comparing energy costs of deep learning frameworks, motivating the AI-specific language comparison.","marker":"[12]"},{"why":"Defines Green AI as a research objective, framing why language efficiency matters.","marker":"[4]"},{"why":"Systematic review of Green AI used to position the gap this paper fills.","marker":"[5]"},{"why":"Gives the goal-question-metric framework used to structure the experiment.","marker":"[9]"},{"why":"Supplies the threats-to-validity taxonomy used to assess the experiment.","marker":"[10]"}],"fun_headline_variants":["AI energy gap: up to 54x by language choice","Interpreted AI languages consume up to 54x more energy","C++ and Java beat Python, R, MATLAB in AI energy test","Green AI: language choice skews energy use 54x"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that a single off-the-shelf library per language stands in for that language; if the choice of library, not the language, drives the energy difference, the headline ranking does not isolate language as a cause.","fun_headline_variants_meta":{"raw":{"variants":["AI energy gap: up to 54x by language choice","Interpreted AI languages consume up to 54x more energy","C++ and Java beat Python, R, MATLAB in AI energy test","Green AI: language choice skews energy use 54x"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000661,"raw_usage":{"total_tokens":3004,"prompt_tokens":912,"completion_tokens":2092,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":528,"completion_tokens_details":{"reasoning_tokens":2019}},"tokens_in":528,"tokens_out":2092,"duration_ms":16825,"temperature":1.0,"reasoning_tokens":2019,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T22:49:58.855535+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Choose one algorithm, implement it in two different mainstream libraries within the same language, and re-run the same measurement protocol; if the within-language spread is as large as the 54x between-language spread, the language-level ranking collapses to an implementation-level result.","supporting_citations":[{"cited_title":"A systematic review of Green AI,","cited_arxiv_id":null,"evidence_quote":"Systematic review of Green AI used to position the gap this paper fills."},{"cited_title":"The goal question metric approach,","cited_arxiv_id":null,"evidence_quote":"Gives the goal-question-metric framework used to structure the experiment."},{"cited_title":"Wohlin, P","cited_arxiv_id":null,"evidence_quote":"Supplies the threats-to-validity taxonomy used to assess the experiment."},{"cited_title":"A Gravitational Theory of the Quantum","cited_arxiv_id":"1709.00329","evidence_quote":"Establishes the general method of ranking programming languages by measured energy consumption, which this study extends to AI workloads."},{"cited_title":"Ranking programming languages by energy efficiency,","cited_arxiv_id":null,"evidence_quote":"Provides the energy, time, and memory relation and language ranking background for non-AI programs."},{"cited_title":"Green AI: Do Deep Learning Frameworks Have Different Costs?,","cited_arxiv_id":null,"evidence_quote":"Closest prior work, comparing energy costs of deep learning frameworks, motivating the AI-specific language comparison."},{"cited_title":"Green AI,","cited_arxiv_id":null,"evidence_quote":"Defines Green AI as a research objective, framing why language efficiency matters."}],"review_version":1}