Sahil Lavingia's 55 Days in Government: A Silicon Valley Perspective on Bureaucracy and Public Service

Sahil Lavingia is a name familiar to many in the tech world. Known for his foundational role at Pinterest and his current leadership as the founder of Gumroad, a platform empowering creators, Lavingia has also made a mark as an active seed and angel investor, as highlighted in a 2020 TechCrunch report on his collaboration with AngelList for a new seed fund. His career has largely been defined by the fast-paced, disruptive environment of Silicon Valley startups.

However, Lavingia recently offered a rare glimpse into a vastly different landscape: the inner workings of the U.S. government. In a candid diary published on his personal site, sahillavingia.com/doge, he recounted his brief but insightful experience as a volunteer software engineer within DOGE, a temporary government organization established by executive order under President Trump. His stint lasted a mere 55 days, yet the account provides valuable details and perspectives on the challenges and realities faced when attempting to bridge the gap between agile tech culture and entrenched government bureaucracy.

Joining DOGE: Motivation Meets Reality

Lavingia's decision to join DOGE, specifically working with the Department of Veterans Affairs (VA), stemmed from a desire for public service. Having previously campaigned for Bernie Sanders in 2016, he harbored a dream of writing code for the government that could positively impact people's lives on a massive scale. The opportunity with DOGE seemed like a potential avenue to fulfill this aspiration, bringing his technical skills and startup mindset to bear on the complex challenges faced by a large federal agency like the VA, which boasts approximately 473,000 employees.

He began his volunteer role in mid-March, tasked with identifying "wasteful" contracts and pinpointing potential candidates for layoffs within the VA. This initial assignment immediately brought him face-to-face with the stark differences between government and private sector operations, particularly regarding personnel decisions.

Navigating Government Personnel Rules

One of Lavingia's most significant surprises was discovering the stringent rules governing layoffs within the VA. Unlike the performance-driven layoff decisions often seen in the tech industry, he quickly learned that factors such as seniority and, notably, a person's veteran status held considerable weight in determining who could be targeted. Performance, in his view, seemed to be a less dominant factor lower down the list of considerations. This structure presented an immediate challenge to his mandate of identifying inefficiencies based purely on performance or role redundancy, highlighting the deep-seated protections and priorities embedded within the civil service system.

This experience underscored a fundamental difference in organizational philosophy. While startups prioritize agility and rapid adaptation, often involving swift personnel changes based on market shifts or performance metrics, government agencies are designed for stability, continuity, and adherence to established procedures and protections. Lavingia's surprise reflects a common point of friction for those transitioning from the private tech sector to public service roles.

DOGE's Role and Perceived Limitations

Lavingia's account also shed light on the nature and limitations of DOGE itself. He described DOGE's function as primarily advisory, akin to a management consulting firm like McKinsey. Crucially, he noted that DOGE held no direct authority over the agencies it advised. The ultimate decisions rested with the agency heads appointed by the President.

This advisory role, without executive power, led to a peculiar dynamic. Lavingia suggested that agency heads were "wise to let DOGE act as the 'fall guy' for unpopular decisions." This sentiment echoes observations made by Elon Musk, who described DOGE as Washington, D.C.'s "whipping boy," blamed for every unpopular outcome. This characterization, reported by the Washington Post, paints a picture of an organization positioned to absorb political fallout without possessing the power to unilaterally enact significant changes.

The structure implies that while DOGE could identify problems and propose solutions, the actual implementation depended entirely on the willingness and capacity of the host agencies. This dependency could lead to frustration for individuals accustomed to the direct impact and rapid execution typical in startup environments.

Work Undertaken and Frustrations Encountered

Despite the structural limitations and bureaucratic hurdles, Lavingia, as a volunteer with a $0 salary, dedicated his time to projects he found interesting and potentially impactful. Since his core DOGE missives didn't consume all his time, he pursued other technical initiatives within the VA context.

He built a "fairly long list of stuff" during his less-than-two-month tenure. His work included developing tools aimed at improving efficiency and transparency within the VA's operations. Among these projects was a tool designed to scan internal PDFs for specific terms, which he described on its GitHub page as related to "DEI, gender identity, COVID policies, climate initiatives, WHO partnerships." He also worked on tools leveraging Large Language Models (LLMs) to analyze contracts and a tool for building organizational charts. Furthermore, he contributed to overhauling the user experience of the VA's existing LLM-based chatbot.

However, a central theme of Lavingia's diary is the frustration born from the inability to see these projects through to actual implementation and impact. He lamented, "I was never able to get approval to ship anything to production that would actually improve American lives — while also saving money for the American taxpayer." This inability to move from development to deployment, a stark contrast to the "move fast and break things" ethos sometimes associated with Silicon Valley, highlights the significant operational and approval barriers inherent in large government systems.

He had hoped to tackle more ambitious projects, such as improving the user experience for veterans filing disability claims or automating and speeding up the claims processing system – areas where technological intervention could have a profound positive impact. The inability to even begin work on these critical, high-impact areas underscores the challenges of prioritizing and executing significant technological overhauls within the existing government framework.

Internal DOGE Dynamics and Knowledge Sharing

Beyond the challenges of working with the VA, Lavingia also made observations about the internal organization of DOGE itself. He noted a surprising lack of structure and knowledge sharing within the temporary group. "I wondered why there wasn’t a centralized DOGE software engineering playbook with all of our learnings; overall, I was surprised by the lack of knowledge-sharing within DOGE. It seemed like every engineer started from scratch," he wrote.

This observation suggests that even within an organization specifically created to bring external expertise into government, internal inefficiencies and a lack of coordinated effort could hinder effectiveness. Without a shared repository of knowledge, best practices, or a clear operational playbook, individual contributors, regardless of their talent, might struggle to build upon previous work or align their efforts effectively.

The TechCrunch article mentioning the people in Elon Musk's DOGE universe and another TechCrunch piece highlighting founders and VCs working with DOGE indicate that Lavingia was part of a cohort of individuals from the tech and venture capital world who joined the initiative. The lack of internal cohesion he observed might have been a characteristic of this temporary, diverse assembly of external experts.

The Abrupt End: Speaking Out

Lavingia's time with DOGE came to an abrupt end after just 55 days. The catalyst for his dismissal was his decision to discuss his experiences with a reporter from Fast Company. As detailed in the Fast Company article, Lavingia shared insights into his work and observations about the VA and DOGE.

Following the publication of the article, Lavingia wrote, "I got the boot from DOGE." His access was revoked without warning shortly after the piece went live. This swift action underscores the sensitivity surrounding public commentary from individuals working within or advising government entities, even in a volunteer capacity. While transparency is valued in the tech world, government operations often operate under different protocols regarding public communication and media engagement.

Ironically, in the same Fast Company interview that led to his dismissal, Lavingia also offered a nuanced perspective on the VA. Despite the frustrations and the slow pace, he acknowledged that working closely with the agency taught him that, while it might be slow like a giant enterprise, it still "works." He stated, "I would say the culture shock is mostly a lot of meetings, not a lot of decisions... But honestly, it’s kind of fine — because the government works. It’s not as inefficient as I was expecting, to be honest. I was hoping for more easy wins."

This balanced view suggests that his experience wasn't one of complete disillusionment, but rather a confrontation with the inherent nature of large, complex, and risk-averse institutions. The government's primary function is often stability and continuity, which can be at odds with the disruptive innovation sought by tech entrepreneurs.

The Broader Dilemma: Silicon Valley Meets Public Service

Sahil Lavingia's 55-day journey within DOGE and the VA encapsulates a persistent dilemma: how can the U.S. government effectively leverage the talent, speed, and innovative approaches of the tech industry while maintaining the necessary structures, protections, and processes required for public service? His experience highlights several key friction points:

• **Pace of Change:** Silicon Valley thrives on rapid iteration and deployment. Government operates on longer timelines, constrained by complex procurement processes, legislative oversight, and the sheer scale of the organizations.
• **Risk Aversion:** Government agencies are inherently risk-averse, particularly when dealing with sensitive data or critical services like veteran care. This contrasts with the startup mantra of moving fast and accepting failure as a learning opportunity.
• **Personnel & Culture:** Government hiring and management are governed by civil service rules designed for fairness and stability, not necessarily the meritocratic or performance-centric models common in tech. The culture is often hierarchical and process-oriented, differing significantly from the flatter, more flexible structures of many tech companies.
• **Impact Measurement:** In tech, impact is often measured by user growth, revenue, or product adoption. In government, impact is measured by policy outcomes, service delivery efficiency, and public trust, often harder to quantify and slower to manifest.
• **Authority vs. Advice:** Initiatives like DOGE, operating in an advisory capacity, face challenges in translating recommendations into action without direct authority, leading to potential frustration for those expecting to implement change directly.

While taxpayers universally desire less waste and more efficiency in government, and there's clear potential for government agencies to benefit from the expertise of skilled programmers and technologists familiar with the latest tools and methodologies, Lavingia's story suggests that simply parachuting Silicon Valley volunteers into the system, expecting them to build solutions from scratch as they would in a startup, might not be the most effective approach. The existing infrastructure, rules, and culture require a more nuanced strategy that respects the unique operating environment of government.

The experience underscores the need for structured programs that facilitate collaboration, provide clear pathways for implementation, and manage expectations on both sides. Bridging the cultural divide requires understanding and adapting to the realities of government work, just as government agencies need to find ways to embrace technological innovation and streamline processes where possible.

Conclusion

Sahil Lavingia's brief tenure with DOGE and the VA serves as a compelling case study in the complexities of integrating private sector tech talent into public service. His diary provides an honest look at the frustrations of navigating bureaucracy, the limitations of advisory roles, and the surprising resilience of established government systems, even when perceived as inefficient. While his dream of writing code to help people at scale was met with significant hurdles in terms of implementation, his willingness to engage and his subsequent sharing of his experience contribute to a broader conversation about how best to modernize government in the digital age.

His open-sourcing of some of his work, despite the inability to deploy it within the VA, represents a tangible outcome of his effort, potentially allowing others to build upon his initial steps. Ultimately, Lavingia's 55 days highlight the profound cultural and operational chasm between Silicon Valley and Washington D.C., a gap that must be thoughtfully addressed if the government is to successfully harness the power of technology for the public good.