Musk’s DOGE and the Data Rush: The Race to Secure the Ultimate Asset
By: Jason Reid

TL;DR

The article speculates that Musk is strategically positioning himself in the AI race by leveraging political influence and initiatives like DOGE to secure exclusive access to vast surveillance data. With traditional human-generated data nearing its limits, this move could provide his companies with a decisive competitive edge, though it also raises major ethical and national security concerns.

IMPORTANT NOTE: THIS ARTICLE IS SPECULATIVE

Training Large Language Models (LLMs): Compute, Data and Machine Learning (ML)

The training of Large Language Models (LLMs) is facing the finite nature of available human-generated data, often referred to as reaching "peak data," as noted by Ilya Sutskever[1]. This marks a pivotal constraint in AI development as the available high-quality datasets derived from sources such as the internet, books, and curated corpora are nearing full utilization[2]. While frontier AI companies can “scale up their compute power[3]” by purchasing high-end GPUs and hire machine learning (ML) researchers and engineers to enable algorithmic progress[4], the supply of human-generated data remains limited. Research indicates that by 2028-2032, the effective stock of public human-generated text will likely be exhausted if current LLM scaling trends persist[5]. The reliance on such data for pre-training, guided by neural scaling laws, underlines the critical challenge posed by this limit. Furthermore, over-reliance on synthetic data, especially when models are trained on data generated by other models, can lead to "model collapse[6]”, where outputs become progressively nonsensical[7]. As such, access to new high-quality human-generated data is the key element for training next generation frontier LLMs. This pressing need brings into focus the diverse sources of training material, which can be broadly divided into public and private datasets.     

Public datasets, widely accessible and openly licensed, include resources such as Wikipedia articles, the multilingual web corpus Common Crawl, BookCorpus and many others[8]. By contrast, private datasets—often proprietary collections maintained by frontier AI companies—encompass large-scale internal data pools derived from user interactions and organizational workflows. For example, Google’s anonymized logs of user search queries[9] remain off-limits to the public. Apple’s corpus of Siri voice transcripts, as discussed in Apple’s privacy statements[10], also remain under tight corporate control. Similarly, Amazon’s trove of Alexa requests, governed by the policies and documentation[11], is unavailable for external use. OpenAI likewise maintains extensive internal pre-training corpora, described in research publications and policies[12] which are not released publicly. Both public and private datasets supply raw data material for model training, yet their size, usefulness, and availability differ significantly, potentially limiting the future of LLM advancements.

Elon Musk’s Strategic Investments

Elon Musk strongly aligned himself to Donald Trump, first with the election campaign[13] and later to the upcoming administration, soliciting significant public engagement and speculation. The following is a summary list, with brief explanations and references, outlining the speculated motivations behind his engagement and investment. 

1. Personal Economic Gains: Elon Musk made significant monetary contributions ($277 million) backing republicans and Donald Trump in the last election campaign[14]. Since Election Day, his net worth has increased by $200 billion to $447 billion[15], making him the wealthiest person in the United States[16].
2. Strategic Business Interests: Deregulation. Both SpaceX and Tesla will presumably benefit from an administration that will ‘be defined by “the lowest regulatory burden”’[17] as per Donald Trump. With new contracts ($1.8 billion) for SpaceX[18] and high investor confidence for Tesla, with its stock increase of 73% since Election Day[19], there is a consensus that Elon Musk’s companies will benefit from his association with the Trump administration.
3. Political Influence: With his involvement in the Department of Government Efficiency (DOGE), Musk will potentially have broad access to influence government policies and departmental budgets. Furthermore, the Trump administration is expected to renew the 2017 Tax Cuts and Jobs Act (TCJA) which will continue to benefit both the wealthy[20]and predominantly corporations, with their income tax rate permanently reduced from 35% to 21%, its “lowest since 1939.”[21]
4. Sociological Influence: Thanks to his purchase of X (formerly Twitter), Elon Musk has acquired an important platform from which he can advocate for his ideals on such matters as censorship[22] and gender identity laws[23].
5. Recent developments:
   1. DOGE’s authority to audit and restructure federal agencies suggests Musk might not only benefit from deregulation but actively shape the future regulatory landscape in ways that favor his own companies.
   2. Musk’s ability to influence government budgets through DOGE raises the question of whether xAI (or SpaceX/Tesla) will receive preferential treatment in federal funding and contracts.

Lastly, Musk’s growing political influence — internationally and domestically — has attracted significant media attention and public debate. Amid this “noise,” various opinions have emerged to explain his actions and motivations. Here, we offer one insight[24] that sheds light on his true intent.

Forbidden Knowledge: The Archive

Nation-states collect massive amounts of surveillance data — none more extensively than the security agencies of the United States. Based on whistleblower disclosure[25], declassified documents[26], Inspector General’s reports[27] and investigative reporting[28], we can roughly estimate that modern mass surveillance programs have amassed data on the order of multiple petabytes (i.e. thousands of terabytes). Below is a non-exhaustive overview of potential value such a dataset could provide for training frontier LLMs in today’s environment.

1. The sheer volume of the datasets provides the raw material for LLM training that isn’t available from any other source. This abundance is critical when nearly all conventionally labeled and high-quality data has been largely exploited.
2. With human-generated data nearly saturated, a unique data source may offer fresh linguistic examples previously unrecorded and unobtainable. These new examples could help push model performance further where improvements from existing datasets are diminishing.
3. Surveillance data would capture informal, technical and dialectical language not commonly seen in existing corpora. This variety could enrich a model’s ability to generalize further across domains and better understand nuanced language and meaning.
4. The vast amounts of unlabeled, real-world data may enable frontier models to learn deeper, more robust representations using self-categorizing and self-supervised objectives. The increased scale may help uncover additional patterns that improve overall performance on downstream tasks.
5. Exposure to “noisy,” un-curated data may force the model to develop improved “filters” for irrelevant patterns. This may yield increased resilience to out-of-distribution inputs and adversarial disruptions.

If a frontier AI company, such as Elon Musk’s xAI, could obtain such a vast, unique and exclusive dataset, it could offer a significant competitive edge. While compute can be scaled and expertise hired, the acquisition of a new, massive untapped data reservoir may very well be the key differentiator in achieving dominance in the AI race.

Recent developments: 

• Connecting DOGE to potential AI training pipelines. This article already suggests Musk is strategically positioning himself to acquire exclusive datasets, but now there’s tangible evidence (e.g., DOGE’s Treasury access). Could xAI be leveraging its influence over DOGE to gain access to government-captured communications, financial transactions, or law enforcement records?
• Implications for AI Safety & Governance: If a private company like xAI gains privileged access to U.S. government surveillance archives, it raises enormous ethical and national security concerns.
  • National security implications of privatized AI models trained on classified or sensitive data.
  • The risk of AI-enhanced state surveillance if Musk aligns too closely with government agencies.
  • The broader AI governance issue—if xAI gains dominance through exclusive datasets, it could stifle open-source AI development, limiting innovation to a single corporate-controlled model.

Summary

In today's ultra-competitive AI landscape, where compute power and talent can be scaled relatively easily, data remains the ultimate differentiator — and a new, untapped reservoir of multi-petabyte surveillance – style data is nothing short of a game changer. With conventional sources of high-quality, human-generated data reaching their "peak" and now nearly exhausted, exclusive access to this immense dataset would grant xAI an unparalleled competitive edge. This dataset, with vast volumes of diverse, un-curated, real-world language— including informal conversations, technical jargon, and regional dialects — could propel xAI far ahead of its rivals. By harnessing these previously inaccessible petabytes of data, xAI could train models that learn deeper, more robust representations, paving the way for breakthroughs in representation learning, model robustness, and domain adaptation. Ultimately, while competitors scrape for diminishing returns from traditional datasets, xAI stands to dominate the AI race with a unique competitive edge derived from an exclusive, colossal reservoir of linguistic variation and nuance — a resource that could fundamentally redefine the capabilities of frontier large language models.

[1]Ilya Sutskever: "Sequence to sequence learning with neural networks: what a decade" https://www.youtube.com/watch?v=1yvBqasHLZs

[2] Will we run out of data? Limits of LLM scaling based on human-generated data https://arxiv.org/pdf/2211.04325v2

[3] Which Companies Own The Most Nvidia H100 GPUs? https://www.visualcapitalist.com/which-companies-own-the-most-nvidia-h100-gpus/

[4] Algorithmic Progress in Language Models https://epoch.ai/blog/algorithmic-progress-in-language-models

[5] Datasets for Large Language Models: A Comprehensive Survey https://arxiv.org/pdf/2402.18041

[6] The curse of recursion: Training on generated data makes models forget https://arxiv.org/pdf/2305.17493

[7] AI models collapse when trained on recursively generated data https://www.nature.com/articles/s41586-024-07566-y

[8] Datasets for Large Language Models: A Comprehensive Survey https://arxiv.org/pdf/2402.18041

[9] https://policies.google.com/privacy

[10] https://www.apple.com/legal/privacy

[11] https://www.amazon.com/alexaprivacyhub

[12] https://openai.com/policies/privacy-policy/

[13] Elon Musk’s Giant Leap https://www.nytimes.com/2024/10/07/style/elon-musk-donald-trump-campaign-rally.html

[14] Elon Musk put $277 million into the election. He’s $200 billion richer this year. https://www.washingtonpost.com/business/2024/12/15/elon-musk-trump-election-wealth/

[15] Elon Musk's record $447 billion fortune means he's nearly $200 billion ahead of Jeff Bezos — and worth more than Costco https://www.businessinsider.com/elon-musk-bezos-tesla-spacex-stock-wealth-net-worth-billionaires-2024-12#:~:text=Elon%20Musk's%20record%20%24447%20billion,and%20worth%20more%20than%20Costco&text=Elon%20Musk%20is%20almost%20%24200,valuation%20rose%20to%20%24350%20billion.

[16] Bloomberg Billionaires Index https://www.bloomberg.com/billionaires/

[17] What Elon Musk could gain from Trump's presidency https://www.bbc.com/news/articles/cjdl22yrrk2o

[18] Elon Musk will not receive highest-level government security clearance – reports https://www.theguardian.com/technology/2024/dec/16/elon-musk-government-security-clearance#:~:text=The%20company%20recently%20signed%20a,%2C%20pre%2Dexisting%20government%20contracts.

[19] What's behind Tesla's $570-billion boost despite wobbly demand? Musk’s political Trump card https://economictimes.indiatimes.com/news/international/business/whats-behind-teslas-570-billion-boost-despite-wobbly-demand-musks-political-trump-card/articleshow/116557454.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst

[20] The 2017 Trump Tax Law Was Skewed to the Rich, Expensive, and Failed to Deliver on Its Promises https://www.cbpp.org/research/federal-tax/the-2017-trump-tax-law-was-skewed-to-the-rich-expensive-and-failed-to-deliver

[21] President-Elect Donald Trump Wants to Lower the Corporate Tax Rate by 29% -- and There Couldn't Be a Clearer Winner, if Enacted https://www.fool.com/investing/2024/12/05/donald-trump-lower-corporate-tax-29-clear-winner/

[22] Elon Musk calls himself a free speech absolutist. What could Twitter look like under his leadership? https://www.npr.org/2022/10/08/1127689351/elon-musk-calls-himself-a-free-speech-absolutist-what-could-twitter-look-like-un

[23] Musk to move SpaceX and X HQ over gender identity law https://www.bbc.com/news/articles/c1vd097z7rvo

[24] This “insight” is purely speculative and should be taken as a work of fiction. It does not originate from insider information, spying, whistleblowing or any other 3^rd party data. 

[25] Snowden Archive https://snowden.glendon.yorku.ca/

[26] CIA’s Bulk Collection of American Records https://www.cato.org/blog/cias-bulk-collection-american-records

[27] Office of the Inspector General's (OIG) review of the Department's role in the Stellar Wind program https://oig.justice.gov/reports/2015/PSP-09-18-15-vol-III.pdf

[28] NSA Prism program taps in to user data of Apple, Google and others https://www.theguardian.com/world/2013/jun/06/us-tech-giants-nsa-data