---
title: "AlphaChip (controversy)"
chunk: 3/3
source: "https://en.wikipedia.org/wiki/AlphaChip_(controversy)"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:43:13.051556+00:00"
instance: "kb-cron"
---

== Nature editorial actions ==
In April 2022, the peer review file for the Nature article was included as a supplementary information file.
In September 2023, Nature added an editor's note to "A graph placement methodology for fast chip design" stating that the paper's performance claims had been called into question and that the editors were investigating the concerns.  On 21 September 2023, Andrew B. Kahng's accompanying News & Views article was retracted; the retraction notice said that new information about the methods used in the Google paper had become available after publication and had changed the author’s assessment, and it also said that Nature was conducting an independent investigation of the paper’s performance claims.  By late September 2024, the editor's note was removed without explanation, but Nature published an addendum to the original paper (dated 26 September 2024). The addendum introduced the name AlphaChip for the proposed RL technique and described methodological details that critics had previously identified as missing, including the use of initial 
  
    
      
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 locations. The addendum addressed some methodological details but still lacked the full training and evaluation inputs needed for independent replication.

== Author responses and ensuing debate ==
Lead authors Azalia Mirhoseini and Anna Goldie rejected internal allegations of fraud or serious methodological flaws, describing whistleblower Satrajit Chatterjee's complaints as a "campaign of misinformation." Google spokespeople stated that the method had been vetted, open-sourced, independently replicated, and deployed "around the world." Academics replied that independent replications had not shown the result claimed, and the use of AlphaChip in production does not prove its superiority over prior methods.  Google researchers also argued that critics omitted pre-training and used insufficient compute. In response, academics pointed out that Google code release included no support for pre-training, the examples used for pre-training were not publicly available, and the compute used in attempted replication equaled the levels reported in the paper.  Goldie, Mirhoseini, and Dean responded to the CACM paper with a letter to the editor, describing its meta-analysis as "regurgitating… unpublished, non-peer-reviewed arguments" and containing "thinly veiled fraud allegations already found to be without merit by Nature."

== Status as of 2026 ==
No positive independent replications of the Nature results have been reported in peer-reviewed literature three and four years since publication.
Starting in 2022, multiple researchers and commentators called for results on publicly available benchmarks to settle the dispute through independent verification and comparison but as of 2026 no such results have been published.  In December 2024, ACM's editor-in-chief, James Larus, publicly invited Jeff Dean and his co-authors to submit their technical response to critiques for peer review.
More indirectly, none of the commercial companies with competing products have adopted this approach.  A 2026 statement by Thomas Andersen, vice president for AI & Machine Learning at Synopsys, states: "In core EDA algorithms, there have been attempts with reinforcement learning to come up with better solutions, but that hasn’t really panned out."

== See also ==
Criticism of Google
Google Brain

== Notes ==

== References ==