In the latest developments from the US Treasury, Elon Musk and his Department of Government Efficiency (DOGE) team will overhaul federal payment surveillance with a series of “very obvious and necessary” reforms. His plan, clearly approved by the Ministry of Finance and set up for implementation, includes the classification codes for all government payments, mandated written grounds for all transactions, and the stricter “do-not-pay” A list is required. Musk argues that these measures will curb fraud and eliminate what he estimates to exceed $100 billion a year.
However, concerns about how masks will promote AI-driven automation across federal agencies and how these changes will be enforced, as well as automated rejection, delay or intent of financial payment algorithm monitoring Does not lead to financial outcomes?
Musk's growing influence on the US Treasury – the agency that governs federal spending, tax collection and financial oversight – is no coincidence. Former hedge fund manager and George Soros Associate Scott Bescent's Treasury Department has access to internal audit and payment systems for masks.
Classification code: Transparency or AI control?
The first element of reform requires that all outgoing government payments include a classification code. Musk argues that this is essential for financial audits. This sounds like a simple measure of transparency, but its actual meaning is much more complicated.
If your agency ignores classification, it's a reason to leave these fields blank as the labels are not missing in the issue. Are agents using outdated systems, avoiding scrutiny or intentionally bypassing deficits? Simply enforcement of compliance will not solve these issues. To avoid penalties, it simply puts pressure on the agency to fill in something, whether accurate or not.
Musk's “AI-First approach” is a concern in detail. This suggests that classification does not stop in the document. When AI monitors payments, it flags, delays, or rejects transactions based on stiff preset parameters. Classifying spending can justify budget cuts, limit funding to specific areas, and implement efficiency models that ignore actual complexity.
If the goal is true transparency, classification should be more accountable rather than automation. Without careful monitoring, this measure can do more to move controls to AI than to improve financial audits.
Obligate the theoretical obligation to pay: slippery slopes?
The second pillar of the plan requires written basis for each government payment. Musk claims that agencies often leave the field blank. He insists that they must at least “attempt” to justify the payment.
At first glance, this seems reasonable – after all, why shouldn't all payments have a stated purpose? But the bigger question is not whether the agency will submit an explanation.
The mask ensures that the judgment “still” does not apply. But someone decides that the agency must provide a basis for payment. When an AI-driven system takes over, it will likely flag, delay, or reject based on algorithm misunderstanding or built-in bias.
What happens if AI misunderstands an entry? Who decides what counts as an “acceptable” rationale? If AI-based fraud detection uses these explanations as data points, it could systematically strip or block certain government programs based on algorithmic trends.
Rather than simply documenting payments, this change positions AI and actively manages financial transactions. If accountability is the goal, the Treasury should focus on human-driven audits and standardized transparency measures rather than paving the way for automated spending management.
“Do-Not-Pay” list: fraud prevention or financial blacklist?
The third element of the new reform focuses on strengthening and accelerating the updates of the “do-not-pay” list. The Treasury designed the list to block payments to fraudulent entities, deceased, terrorist fronts and beneficiaries outside of Congress's budget. Musk claims it takes up to a year for officials to ignore it and add bad actors. His suggestion? Ensure that you have strictly enforce your list and update “at least weekly, if not daily.”
Fastest fraud detection makes sense, but the daily updated AI-surveillanced blacklist raises concerns about overreaching, false positives and financial blacklisting without legitimate procedures. If automation is used to flag and block payments in real time, legitimate recipients may be mistakenly denied the funds. And so far, there is no clear path to appeal.
Do you set the criteria for who will be on the list? If AI-driven fraud detection flags payments based on patterns rather than clear evidence, government contractors, charities, or political organizations are mistakenly blacklisted. Can you feel it?
While enhancing the “do-not-pay” list is logical, it requires prioritization of accuracy, accountability and legitimate processes, not just automation for “efficiency.” Without proper surveillance, this reform could easily change from a fraud prevention tool to an AI-driven system of financial exclusion.
Who writes the code?
Even if we assume that the intent of the mask is completely noble – despite all the evidence to the contrary, the public may want to consider the slightly inconvenient details. What happens when the next administration gets these tools?
AI can reprogram far faster than it can exchange bureaucrats. In other words, today's fraud detection systems can be simple to tomorrow's financial enforcement mechanism. Blacklists aimed at catching bad actors can flag political enemies, independent journalists, or those whose spending habits do not match the priorities of the administration.
Where is the council?
Musk and Doj will restructure the Treasury and other agencies, but Congress is missing in action.
Institutions aimed at managing federal spending are effectively handing over unelected technocrats. Billionaires with a corporate empire spanning AI, defense, space, communications, social media and self-driving cars have little scrutiny of government fund allocation, tracking and refusal.
Last week, Republicans thwarted attempts to summon Musk and reluctantly questioned his growing influence on the federal government. Some lawmakers reportedly fear that they will cross the musk, knowing that objections could cost them their seats.
Congresswoman who loves grandstands on surveillance should ask real questions. Who is actually writing the rules for AI-driven financial governance? What safeguards are available to stop the excessive reach of the algorithm? And who is responsible for AI-controlled payment systems when they fail or get abused?
So far, silent. The transformation of the Ministry of Finance has been recharged, unchecked, unchallenged, and barely noticed. There is no hearing or discussion – a quiet ham of automation that replaces human surveillance, one algorithm at a time.
Low Technological Correction
AI is not necessary to eradicate fraud. It enforces existing laws, eliminates waste, and distributes electricity to the nation. A real solution? It will shut down unconstitutional institutions, reduce Washington's bloated bureaucracy, make fraud easier to detect and difficult to hide.
Instead of passing financial governance to the algorithm, Congress should:
Performs independent audits that are independent of AI black box decisions. Enforce fraud laws with actual surveillance rather than automated denials. Increase your spending transparency without AI filtering. Return financial management to states with a strong accountability.
Musk's “AI-First” approach cannot prevent fraud. It integrates power. The real risk is not fraud itself, but who decides what qualifies as a scam in the first place?
Related:
Reprogramming of the Republic: Musk's quiet AI federal acquisition
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