Replacing Government Consulting:

Automating Bureaucracy and Cutting Waste

Governments globally spend billions annually on consulting firms to navigate complex regulations, procure approvals, and manage compliance. This creates an entrenched industry with high costs and slow processes, limiting agility and innovation.

Challenge
Government workflows rely heavily on human experts for knowledge-intensive tasks that are repetitive and rules-based. Consulting firms act as intermediaries, but their involvement drives up costs and creates dependency. Existing software solutions often remain bespoke, fragmented, and lack scalability.

Cutting in & Opportunity
Develop AI-powered platforms that leverage large language models to automate regulatory interpretation, policy compliance checks, and government procurement processes. These platforms should embed up-to-date legal frameworks and provide transparent audit trails to increase trust. The focus is on transforming opaque bureaucratic workflows into efficient, accessible, and scalable systems.

Opportunities exist in building domain-specific LLMs tuned for various government functions, integrating with public data sources, and creating user-friendly interfaces for officials and contractors.

Startups can also create feedback loops to continually improve AI accuracy and relevance based on regulatory changes.

Case in Point
Companies like Govini apply data analytics and AI to enhance government contracting insights. Civis Analytics combines data science with policy expertise to improve decision-making. These precedents highlight the value in bridging government complexity with scalable AI automation.

Why This Matters
Replacing costly, manual consulting with AI-driven platforms reduces waste and accelerates government responsiveness.

Startups that navigate regulatory nuances and embed trust mechanisms will play a critical role in modernizing public sector operations while unlocking a multi-billion dollar opportunity.

AI-Native Enterprise Software:

Reshaping Workflows and Value Capture

Enterprise software giants emerged by harnessing cloud computing to offer vastly improved, scalable products that incumbents struggled to replicate. AI now presents a similarly transformative wave, embedding intelligence deeply into core workflows rather than treating it as an add-on.

Challenge
Existing enterprise software often functions as a system of record, tracking human activity but lacking intelligent assistance. Legacy vendors face inertia and complexity in redesigning products to integrate AI meaningfully. Customers expect faster, more accurate, and context-aware tools, but widespread adoption demands trust and seamless integration.

Cutting in & Opportunity
Focus on building AI-native applications that augment employee productivity across sales, HR, finance, and operations by embedding AI assistants that proactively surface insights, automate routine tasks, and continuously learn from interactions.

Position products not as mere incremental upgrades, but as entirely new workflows that fundamentally redefine how work is done, distinct from legacy systems.

Developing vertical-specific AI models trained on domain data can differentiate offerings. Startups can also capture value by making AI explainable and compliant with enterprise security standards. The goal is to become indispensable in day-to-day operations, shifting cost centers into profit drivers.

Case in Point
Companies like Gong apply AI to analyze sales conversations for coaching and forecasting. UiPath automates workflows with AI-powered robotic process automation. These illustrate how AI can move beyond simple automation to strategic business enablers.

Why This Matters
The next generation of enterprise software will be defined by AI’s ability to transform work itself.

Startups that embrace this from day one avoid legacy constraints and capture disproportionate value by rewiring how businesses operate at scale.

Lean Team Productivity Metrics:

Redefining Scale Through Revenue per Employee

The era of sprawling corporate behemoths is giving way to lean, nimble teams powered by AI and cloud infrastructure. These small groups can achieve outsized impact by focusing relentlessly on efficiency and execution.

Challenge
Traditional companies grow headcount to scale, which often dilutes focus and slows decision-making. High costs and internal politics sap energy. For startups aiming to build category-defining businesses, balancing rapid growth with maintaining a high-agency culture remains difficult.

Cutting in & Opportunity
Design companies that optimize for revenue per employee, leveraging AI tools to automate routine tasks and amplify human judgment. Building “10-person, $100 billion” companies means rethinking org structure, workflows, and talent acquisition to maximize individual leverage.

The opportunity lies in creating SaaS and AI-powered platforms that enable small teams to handle complex workflows without layers of middle management. Tools that provide real-time analytics, support asynchronous collaboration, and integrate seamlessly into everyday work become critical.

Case in Point
Notion enables small teams to centralize knowledge and workflows, boosting productivity without bloating headcount. GitHub Copilot amplifies developer output with AI assistance, reducing the need for large engineering teams.

Why This Matters
Companies that master scaling through high-agency teams will outpace larger, slower competitors.

Investors recognize this shift and prize startups that demonstrate how to generate more revenue with fewer people, marking a fundamental change in the nature of scale.

YC’s latest Requests for Startups reveal where capital flows to structural bottlenecks and entrepreneurial advantages instead of merely listing trending topics.

1. Skills retraining targets more than AI researchers; the shortage lies in blue-collar trades essential for building physical infrastructure.

   • The opportunity involves developing rapid vocational training powered by AI, combining multimodal teaching and real-world simulation to overcome traditional scaling limits.

   • Standardizing instructional workflows with AI and creating platforms that connect trainees directly to employers unlocks growth potential.

2. Video generation shifts from content creation toward programmable assets that become core software components.

   • Founders should focus on building developer platforms and APIs that serve media, e-commerce, gaming, and other industries.

   • This fosters a long-tail ecosystem instead of isolated video products.

3. Government consulting suffers from high costs and inefficiency.

   • Automating complex government workflows with large language models integrated with compliance and transparency offers a path forward.

   • This institutional replacement requires deep domain expertise but promises steady returns.

4. AI-native enterprise software reconstructs workflows at their core rather than adding features.

   • Deeply specialized intelligent SaaS focusing on explainability and security targets vertical markets where legacy incumbents lag.

   • Startups gain advantage through tailored solutions that incumbents find difficult to replicate quickly.

5. Capital shifts from valuing team size to emphasizing revenue per employee.

   • Entrepreneurs must build tools and processes that maximize individual productivity and automate organizational operations.

   • Flattened hierarchies and automation represent essential factors for the next generation of scaling.

   
   

   These themes highlight systemic challenges where technology acts as an entry point, but value depends on institutional design and ecosystem building.

   The real defensibility lies in standardized, trusted, and operable AI infrastructure that links people, machines, and organizations into a robust, hard-to-copy moat.