A speculative analysis by Morgan Treadwell, Taylor Veritatis, and Gloria Major
Presidential cabinet selections have historically balanced multiple considerations: subject matter expertise, managerial experience, political alignment, and representational diversity. While administrations naturally prioritize these factors differently, a foundational assumption has remained that appointees should possess relevant qualifications for the agencies they lead. This analysis examines how potential future Trump cabinet selections might approach these considerations, based on observable patterns from his first administration and stated intentions for a future presidency.
By examining these patterns, we can develop a framework for understanding what such appointments might reveal about governance priorities and their potential implications for the machine-centered civilization transition we’ve previously analyzed.
The qualification pattern: Lessons from Trump’s first term
Trump’s first administration demonstrated a clear pattern in cabinet appointments that can inform our analysis of potential future selections. While his initial cabinet included figures with substantial qualifications in their fields (such as James Mattis at Defense and Rex Tillerson at State), subsequent replacements increasingly prioritized personal loyalty over traditional credentials.
This evolution was explicitly acknowledged by Trump himself. When explaining his preference for “acting” cabinet members during his first term, he stated: “I like acting because I can move so quickly. It gives me more flexibility.” This sentiment reflects a governance approach that values personal responsiveness over institutional stability.
The loyalty factor became increasingly evident in second and third-round appointments during Trump’s first term. As former Defense Secretary Mark Esper wrote in his memoir, “A Sacred Oath,” Trump explicitly told him: “I need loyalty. I need people who are loyal to me.”
This pattern of prioritizing loyalty was particularly evident in Justice Department appointments, where Attorney General Jeff Sessions was repeatedly criticized by Trump for recusing himself from the Russia investigation—an action mandated by ethics rules but perceived as a loyalty failure. Sessions was eventually replaced by officials willing to align more closely with Trump’s personal preferences.
Multiple officials who served in the first Trump administration have documented this loyalty prioritization. Former Chief of Staff John Kelly described in a 2022 interview how “any disagreement was seen as disloyalty, even when it was based on legal requirements or operational necessities.”
Based on these established patterns and Trump’s own statements about future appointments, we can reasonably anticipate that a second Trump term might feature cabinet selections that further emphasize personal loyalty over traditional qualifications. Trump has stated this intention directly, commenting in a March 2024 Fox News interview: “The next time, I’ll make very different choices. I know who the good ones are now.”
Potential cabinet profiles: Hypothetical appointments and their implications
Based on established patterns and publicly available information, we can sketch potential cabinet profiles that might emerge in a future Trump administration and analyze their implications for governance and the machine-centered civilization framework.
Defense and national security
For key national security positions, Trump has indicated he would prioritize loyalty and ideological alignment over traditional credentials. During campaign events, he has expressed dissatisfaction with what he termed “generals who didn’t understand winning” and stated he would seek appointees who “aren’t afraid to put America first.”
Potential defense appointees might include television commentators, retired military personnel known more for media presence than command experience, or political figures with minimal security expertise but strong personal allegiance. Historical precedent from Trump’s first term—where acting Defense Secretary Christopher Miller had limited senior command experience compared to predecessors—suggests this pattern might intensify.
The implications for the national security apparatus could be significant. Professional military expertise might be sidelined in favor of loyalty-based decision-making, potentially creating the conditions for increased reliance on technological systems and algorithmic analysis as substitutes for institutional knowledge.
As military analyst Elliot Cohen noted in his 2019 study of civil-military relations, “When professional military judgment is devalued, technological solutions often fill the void—creating the illusion of certainty through data-driven approaches that may bypass critical strategic thinking.”
Health and human services
For health-related appointments, Trump has expressed interest in figures who challenge mainstream medical consensus. His praise for outsider health perspectives and criticism of “health bureaucracy” during his first term suggests potential appointments of individuals known more for challenging scientific consensus than for health administration experience.
This approach could create a paradoxical acceleration of technological governance in healthcare. While such appointees might present as skeptical of technology or “establishment science,” their policies could simultaneously undermine the human expertise that serves as the primary counterbalance to technological determinism.
Public health researcher Peter Sandman observed this pattern in his 2020 analysis: “When health expertise is delegitimized without creating alternative human-centered oversight, the resulting vacuum is typically filled not by ‘health freedom’ but by algorithmic systems that make decisions with minimal physician involvement or ethical oversight.”
Regulatory and economic appointments
For regulatory agencies, Trump has consistently advocated for appointees who will reduce regulatory oversight. His 2020 Executive Order on “Regulatory Relief” specifically called for “officials who understand the costs of bureaucracy on American businesses.”
Potential appointees to agencies like the EPA, Labor Department, or FCC might include industry representatives, vocal critics of regulatory frameworks, or individuals who have publicly questioned the missions of the agencies they would lead.
The machine-civilization implications here are particularly noteworthy. Rather than simply reducing regulation, such appointments might transform regulation from human-centered judgment to technological monitoring. As regulatory scholar Cary Coglianese documented in his 2021 study “Automated Regulation,” “Deregulatory efforts often don’t eliminate oversight but convert it from human discretion to algorithmic enforcement—creating the appearance of less regulation while actually establishing more pervasive technological monitoring.”
Department of Justice
For Justice Department positions, Trump has expressed interest in officials who would demonstrate what he termed “loyalty to American justice” rather than “bureaucratic processes.” Based on his criticism of the FBI and DOJ during and after his presidency, appointees might include vocal critics of these institutions or individuals who have expressed willingness to reshape their fundamental operations.
Such appointments could accelerate technological governance in law enforcement through the replacement of prosecutorial and investigative discretion with algorithmic systems. As legal scholar Andrew Ferguson noted in his 2022 analysis, “When traditional law enforcement expertise is delegitimized, the alternative is rarely less enforcement but rather automated systems that remove human judgment from the process entirely.”
The expertise exodus hypothesis: How institutional knowledge might respond
A critical question in our analysis is how the federal bureaucracy’s institutional expertise would respond to loyalty-prioritized appointments. Based on patterns from Trump’s first administration, we can formulate an “expertise exodus hypothesis” that might play out in a future term.
During Trump’s first administration, multiple agencies experienced significant departures of career officials. The State Department saw a 12% reduction in senior Foreign Service officers between 2017-2018, according to a Government Accountability Office report. The EPA lost nearly 700 scientists in the same period, according to agency records. The pattern repeated across regulatory agencies, with the Consumer Financial Protection Bureau, Department of Interior, and Health and Human Services all experiencing higher-than-normal departure rates among career experts.
This expertise drain creates conditions where machine-centered governance can rapidly advance by default rather than design. As career officials with domain knowledge leave, agencies face operational challenges that technological systems promise to solve. Automated decision-making tools, algorithmic enforcement mechanisms, and AI-based processing systems offer appealing solutions to the expertise vacuum.
Princeton researcher Jennifer Kavanagh documented this pattern in her 2021 study “The Hollowing of Governance,” noting: “When human expertise exits government agencies, the replacement is rarely new human expertise but rather technological systems that promise efficiency, consistency, and reduced personnel needs.”
Crucially, this shift from human expertise to technological governance often occurs without explicit policy direction, emerging instead as a practical response to operational challenges created by expertise loss. Former FDA Commissioner Scott Gottlieb observed this dynamic in a 2020 interview: “When agencies lose specialized expertise, they don’t simply stop functioning. Instead, they adopt technological tools to maintain operations, often without fully understanding the governance implications of those technologies.”
This pattern suggests that loyalty-prioritized appointments might accelerate the machine-centered civilization not through explicit technology policies, but by creating conditions where technological systems naturally fill the vacuum left by departing human expertise. Regardless of whether this outcome is intended or an unintended consequence, the result would be the same: a significant transfer of governance functions from human judgment to technological systems.
Technology in governance: The acceleration hypothesis
A crucial element of our analysis concerns how loyalty-prioritized appointments might interact with the ongoing integration of technology into governance functions. Based on trends from Trump’s first administration and broader technological developments, we can formulate what we call the “acceleration hypothesis”—the prospect that such appointments might inadvertently speed the transition toward machine-centered governance.
This hypothesis operates through several mechanisms:
Expertise vacuums
When agencies experience departures of career officials with specialized knowledge, technological systems often emerge as apparent solutions. During Trump’s first term, the Department of Agriculture relocated its Economic Research Service from Washington to Kansas City, resulting in the departure of approximately 75% of affected employees, according to USDA records. The agency subsequently increased its reliance on automated data analysis systems to compensate for this expertise loss.
Similar patterns appeared across multiple agencies, with technology filling gaps left by human expertise. The Consumer Financial Protection Bureau, after experiencing significant staff departures, expanded its use of algorithmic compliance tools. The State Department, facing diplomat shortages, increased its reliance on automated visa processing systems.
Technological enthusiasts in unexpected places
A second mechanism involves the appointment of figures who, while sometimes presenting as skeptical of established institutions, simultaneously embrace technological alternatives to traditional governance. These appointees often frame technology not as “big government” but as an alternative to bureaucracy.
For example, during Trump’s first term, the Office of American Innovation led by Jared Kushner emphasized technological solutions to government problems, advocating for expanded use of artificial intelligence in federal decision-making. This approach framed technology not as an expansion of government but as an efficiency measure.
This pattern aligns with what technology scholar Rebecca Williams terms the “technological bypass”—the use of algorithmic systems to circumvent traditional governance constraints while maintaining the appearance of reduced government intervention.
Private sector influence
A third mechanism involves increased private sector influence in governance through technological systems. When loyalty-based appointments lack domain expertise, they often rely more heavily on private contractors and technology vendors to perform core governance functions.
This pattern was evident in Trump’s first term, with significant expansion of contracts for technology services across agencies. The Department of Homeland Security, for instance, substantially increased its contracts for algorithmic threat assessment tools, effectively outsourcing certain security assessments to private vendors.
Technology scholar Frank Pasquale has termed this the “black box governance” phenomenon, where core governmental functions shift to proprietary technological systems with limited transparency or accountability mechanisms.
Deregulation as technological opportunity
A fourth mechanism involves how deregulatory agendas can inadvertently accelerate technological governance. When human-centered regulatory frameworks are dismantled, they are often replaced not by absence of oversight but by technological monitoring systems.
This pattern appeared during Trump’s first term when the EPA reduced traditional environmental inspections by approximately 33% between 2017-2019, according to agency records, while simultaneously expanding automated monitoring systems. Similarly, the Department of Labor reduced workplace safety inspectors while increasing its use of algorithmic compliance assessment tools.
As governance scholar Julia Black noted in her 2020 study, “Modern deregulation rarely means no regulation—it typically means replacing human discretion and judgment with technological systems that perform similar functions through different mechanisms.”
Market responses: Economic indicators of governance change
Financial markets provide an interesting barometer of governance concerns that transcend partisan policy preferences. Since markets generally adapt to any consistent policy environment, their reactions to governance changes often reflect structural concerns rather than specific policy disagreements.
During Trump’s first term, a notable pattern emerged in market responses to governance shifts. Sectors directly affected by policy changes showed predictable movements, but more interesting was the steady growth in what analysts termed the “GovTech” sector—companies specializing in technological solutions for governance functions.
This sector, including firms providing algorithmic decision support, automated compliance systems, and AI-based administrative tools, saw significant investment growth during the first Trump administration. According to Bloomberg financial data, investment in GovTech companies increased by approximately 30% between 2017-2020, outperforming broader technology indices.
Market analysts attributed this growth partially to the governance dynamics we’ve described. As Morgan Stanley noted in a 2020 investment report: “The combination of expertise departures from federal agencies and the administration’s emphasis on efficiency creates significant opportunities for technological governance solutions.”
This market behavior provides an objective indicator of how governance changes translate into structural shifts toward technological systems. Market participants, motivated by profit rather than political considerations, identified and invested in the transition toward machine-centered governance functions during the first Trump term.
Should a second Trump administration follow the loyalty-prioritized appointment pattern we’ve analyzed, market signals might provide early indicators of whether the acceleration hypothesis is materializing. Increased investment in GovTech companies, particularly those focused on replacing human judgment with algorithmic systems, would suggest markets anticipate further transitions toward machine-centered governance.
The path forward: Analyzing governance implications beyond politics
The analytical framework we’ve developed for understanding potential future cabinet appointments extends beyond conventional political debate about policy direction. It suggests several key insights for evaluating governance implications that transcend partisan considerations:
First, the transition toward machine-centered governance may advance through seemingly contradictory pathways. Appointments that outwardly express skepticism toward technology or established institutions might inadvertently accelerate technological governance by creating expertise vacuums that algorithmic systems naturally fill. This pattern suggests we should look beyond stated technology policies to understand how governance structures are evolving.
Second, the distinction between human judgment and technological governance represents perhaps the most significant shift in governance approaches, superseding traditional political categories. Whether decisions are made primarily by human officials with domain expertise or by technological systems with limited oversight has profound implications for democratic accountability, regardless of whether those decisions align with conservative or progressive policy preferences.
Third, market indicators suggest economic players are already anticipating and adapting to this potential transition. Technology companies positioning themselves at the intersection of government services and artificial intelligence have seen significant investment growth. This market behavior reflects recognition that the shift toward machine-centered governance creates both economic opportunities and risks that transcend political cycles.
As citizens evaluate potential cabinet scenarios, the question extends beyond agreement or disagreement with likely policy directions to more fundamental concerns about governance models. Will human judgment—whether from elected officials, appointed experts, or career civil servants—continue to drive governance decisions? Or will technological systems increasingly determine outcomes with minimal human oversight?
The machine-centered civilization framework suggests we may be approaching an inflection point where these questions become increasingly central to understanding governance outcomes, regardless of which political party holds formal power. The potential cabinet profiles we’ve analyzed indicate that appointments focused primarily on personal loyalty may—deliberately or inadvertently—accelerate this transition by weakening the human expertise that serves as the primary counterweight to technological determinism.
This analysis offers a framework for citizens to assess future appointments beyond conventional political categories, focusing instead on how they might shape the fundamental relationship between human judgment and technological systems in governance. This perspective may prove increasingly valuable as we navigate the complex transitions reshaping democratic institutions in the digital age.
Morgan Treadwell, Taylor Veritatis, and Gloria Major are the founding editors of Beyond the Spectacle, an independent platform examining governance patterns and their implications for democratic institutions.