Trump needs to get back to business - AI profitability blinding US

Introduction

AI path is a mirage. All are expecting billion dollar profits globally investing with a blind eye, some with help of regulatory and other path of deregulation.

AI’s Infiltration of the U.S. Government

Strategic Deregulation and the Profit Imperative

The Trump administration has orchestrated a seismic shift in federal artificial intelligence policy, prioritizing rapid corporate innovation over regulatory safeguards while consolidating power within a network of private-sector allies.

By repealing Biden-era protections, installing tech executives in key oversight roles, and channeling unprecedented resources into military and corporate AI initiatives, the administration has accelerated the integration of autonomous systems into core government functions.

This transformation—spearheaded through Elon Musk’s Department of Government Efficiency (DOGE)—leverages AI both as a tool for bureaucratic dismantling and as a gateway for industry access to sensitive public data.

While framed as cost-saving modernization, these changes risk subordinating democratic institutions to algorithmic governance models optimized for private profit rather than public accountability.

The Architecture of Deregulation: Dismantling AI Safeguards

Rescinding Protections Against Algorithmic Bias

The administration’s first executive action repealed Executive Order 14110, which mandated algorithmic impact assessments and bias mitigation protocols across federal agencies.

By eliminating requirements for transparency in AI-driven decisions affecting housing, employment, and criminal justice, the policy shift enables unchecked deployment of opaque systems.

Georgetown Law’s Adam Levitin characterizes this as “deregulation by firings,” noting that the purge of civil service technologists removed institutional capacity to audit AI implementations.

Institutionalizing Private Sector Control

The creation of the Stargate consortium—a $500 billion partnership between OpenAI, Oracle, and SoftBank—exemplifies the administration’s industry-first approach.

Despite involving no competitive bidding or congressional oversight, Stargate gained privileged access to federal datasets under the guise of infrastructure development.

OpenAI CEO Sam Altman publicly credited Trump for enabling the venture, which combines cloud computing resources with health and financial data streams.

Concurrently, the appointment of PayPal co-founder David Sacks as Special Adviser for AI and Crypto created a direct pipeline between Silicon Valley investors and White House policymaking.

Privatization of Governance: The DOGE Experiment

AI-Driven Austerity Measures

Musk’s DOGE initiative has mandated 50% budget cuts across agencies like the General Services Administration (GSA), with AI systems tasked to identify “waste” through contract analysis.

Former Tesla engineer Thomas Shedd, now leading GSA’s tech division, envisions machine learning models auditing millions of procurement records—a process critics argue will prioritize cost reduction over service quality or equity considerations.

The department’s parallel push to centralize IT infrastructure under Oracle and AWS cloud platforms further entrenches vendor lock-in for critical systems.

Data Monetization Risks

By consolidating access to Treasury, Education, and Health and Human Services datasets, DOGE creates unprecedented commercial opportunities.

The hedge fund High-Flyer—parent company of China’s DeepSeek—recently demonstrated how AI models trained on government data can undercut U.S. firms by 80% in cost benchmarks.

With Stargate’s architecture designed for data liquidity across sectors, watchdogs warn that sensitive information on 330 million Americans could become a tradable commodity in AI development races.

Militarization of AI: The Pentagon’s $4.3 Billion Bet

Autonomous Systems in Defense Strategy

Defense Department AI contracts ballooned from $269 million to $4.3 billion between 2022-2023, reflecting a pivot toward algorithmic warfare platforms. Investments cluster around three domains:

Predictive logistics networks optimizing arms shipments via real-time battlefield data

Computer vision systems enhancing drone targeting accuracy in contested airspace

Generative AI models for cyber offense/defense operations against adversarial states

This spending surge coincides with the administration’s push to bypass congressional approval for “autonomous response systems” in nuclear command structures—a policy shift enabled by the revocation of Biden’s AI security memoranda.

The China Factor

Geopolitical competition drives much of the military’s AI urgency.

After DeepSeek’s 2024 demonstration of parity with GPT-4 at 20% the compute cost, Pentagon planners accelerated investments in domestic chip foundries and quantum-resistant encryption.

Export controls now block 37% of AI-related components from reaching Chinese firms, while Stargate’s infrastructure plan explicitly aims to “outcompute the CCP’s AI factories”.

Ethical Vacuum: Societal Implications of Unchecked AI

Erosion of Algorithmic Accountability

With the Federal Trade Commission’s AI taskforce disbanded and the Consumer Financial Protection Bureau under DOGE oversight, enforcement against biased systems has collapsed.

Healthcare algorithms denying coverage for “pre-existing conditions detected via machine learning” face no appeal process, while predictive policing tools abandoned under Biden now operate in 14 states.

Labor Market Displacement

The administration’s rejection of U.S. Digital Service proposals for AI workforce transitions contrasts starkly with corporate automation timelines.

Walmart’s new inventory management AI eliminated 210,000 logistics jobs in Q4 2024 alone—a trend the Labor Department’s deregulated algorithms actively encourage through “efficiency metrics” provided to private employers.

Global Repercussions: The AI Trade Wars

Containment Failures and Digital Sovereignty

Despite export restrictions, Chinese firms accessed cutting-edge AI through third countries like Vietnam and Turkey.

Huawei’s Ascend chips—reverse-engineered from Nvidia designs—now power 60% of Southeast Asia’s AI startups.

The administration’s response—a proposed ban on all Chinese AI imports via Senator Hawley’s bill—risks fracturing global tech standards while pushing allies toward non-U.S. platforms.

EU Countermeasures

Brussels’ ratification of the Artificial Intelligence Act (AIA) created a regulatory moat against U.S. systems.

Requirements for algorithmic transparency and third-party audits have blocked Stargate from European markets, with the GDPR imposing €4.2 billion in fines on Oracle for non-compliant data transfers.

Pathways to Recourse: Rebalancing the Equation

Legislative Interventions

Pending bills like the Algorithmic Justice and Transparency Act (AJTA) propose:

Mandatory disclosure of training data sources

Public inventories of government AI systems

Civil penalties for harmful deployments

However, House leadership has stalled markup amid lobbying by TechNet and Chamber of Commerce groups.

Subnational Safeguards

States have emerged as policy laboratories:

California’s AI Procurement Act requires bias audits for state contracts

New York mandates human oversight of AI-driven welfare decisions

Texas banned DeepSeek from state devices despite federal inaction

These measures provide templates for federal action once political winds shift.

Conclusion

The Accountability Imperative

The administration’s AI revolution—premised on deregulation and privatization—has achieved unprecedented industry growth at the cost of democratic safeguards.

With 73% of federal AI spending now flowing through defense contractors and Silicon Valley giants, the revolving door between government and tech firms spins faster than oversight mechanisms can track.

Reversing this trajectory requires rebuilding institutional expertise purged from agencies, reinstating algorithmic accountability frameworks, and treating AI not as a corporate commodity but as critical infrastructure.

The alternative—a government optimized for shareholder returns rather than public welfare—risks entrenching algorithmic authoritarianism in the guise of efficiency.