a16z Games

Nicolas Kopp, co-founder and CEO of the agentic enterprise resource planning (ERP) platform Rillet, outlines the strategic foundations behind his company’s rapid growth, including a $100 million Series C funding round achieved within fourteen months of its third round. The discussion emphasizes the critical importance of execution-focused leadership, the necessity of early customer engagement, and the strategic value of product wedges in competitive markets.

A central thesis of the discussion is that while building a comprehensive platform is often necessary to compete with established incumbents like NetSuite or SAP, utilizing a specific product wedge is a more effective way to gain market traction. Kopp notes that wide-surface-area products create significant operational complexity and support burdens for early-stage startups. Consequently, he advocates for using a credible wedge—such as Rillet’s focus on revenue recognition and ARR tracking for SaaS and AI companies—to simplify the initial sales and support process while establishing a beachhead for broader platform expansion.

Regarding methodology and operational philosophy, the company prioritized live production testing during its stealth phase, conducting multiple micro-launches to gather critical, often negative, user feedback. This approach allowed the team to refine the product based on real-world market dynamics rather than internal assumptions. Furthermore, Kopp highlights the importance of maintaining a high talent bar, describing the hiring process as a flywheel where early, high-quality hires attract subsequent talent, ultimately serving as the primary driver of the company’s success. The insights provided reflect a focus on the B2B software sector, specifically targeting finance teams within the broader enterprise technology landscape.

Founders of early-stage startups are cautioned against relying solely on algorithmic distribution for growth, a strategy often described as buckshot growth. The core thesis posits that abdicating growth responsibilities to automated platforms is a strategic error. Instead, founders are encouraged to act as students of the game, actively studying the mechanics of emerging distribution channels to identify creative, non-obvious opportunities before standard industry playbooks are established.

The analysis highlights that while mature channels like TikTok, Instagram, and Twitter remain necessary for visibility, they are increasingly saturated. Consequently, startups gain a significant competitive advantage by being early adopters of new paradigms, such as the current rise of AI agents. By understanding the underlying logic of these platforms—such as how agents make decisions or how invitation heuristics function—founders can engineer growth hacks that bypass traditional barriers. Historical examples, such as manipulating early Facebook invitation acceptance rates, illustrate the effectiveness of deep platform analysis over passive content broadcasting.

The recommended approach emphasizes a dual-track strategy: maintaining discipline in established channels while reserving resources for moonshot experiments. Founders are urged to move beyond generic growth tactics and instead focus on identifying and engaging with niche communities where their specific target audience resides. This requires a proactive, hands-on effort to understand user behavior and platform mechanics. Ultimately, the responsibility for growth rests with the founder, who must be opinionated about their audience and willing to experiment boldly to achieve traction in an increasingly crowded entrepreneurial landscape.

The core thesis presented by Jesse Zhang, CEO of Decagon, is that market competition is a positive indicator of a viable, large-scale opportunity rather than a deterrent for new founders. Instead of attempting to invent entirely novel concepts, entrepreneurs should focus on discovering existing, high-value problems that customers are already willing to pay to solve. By treating product discovery as a rigorous sales process, founders can validate market demand through direct engagement, identifying specific pain points that justify a scalable business model.

Effective discovery requires moving beyond theoretical interest to confirm financial commitment. Founders should prioritize conversations that uncover budget ownership, return on investment, and the specific problems for which enterprises are currently spending significant capital. Once a problem is identified, the strategy shifts to building a competitive advantage—whether through faster product iteration, superior sales execution, or better integration with existing enterprise workflows. Competition serves as a useful tool for team motivation and strategic refinement, as it highlights which features are essential and which approaches are ineffective.

Regarding operational strategy, the analysis emphasizes the importance of productization over reliance on forward-deployed engineering. While early-stage startups may use custom deployments to bridge gaps and learn from customer pain, the ultimate goal must be to build a scalable, standardized product. This approach is contrasted with the "deploy company" model, which may face scaling challenges against large, well-funded incumbents. Ultimately, the most successful AI agents are those designed to interface with enterprise systems and teams in the same manner as human employees, effectively automating high-value tasks across revenue-generating and back-office functions.

Instacart co-founder Max Mullen provides a framework for early-stage founders to evaluate investor feedback, emphasizing that not all guidance carries equal weight. The core thesis posits that advice should be categorized into three distinct buckets: science, religion, and art. Science-based advice, which concerns objective operational or product experiments, is where investors provide the most value. Conversely, religion-based decisions regarding company culture and values should be informed by a broad range of peer examples, while art-based decisions—the mission and core strategy—are the exclusive domain of the founder and should be protected from external interference.

Beyond the categorization of advice, the discussion highlights that product-market fit is a gradual spectrum rather than a singular event. Using Instacart’s evolution as a case study, the transition from initial product-customer fit among urban professionals to broad market adoption took several years of iterative development. Founders are cautioned against skipping the foundational work of identifying a truly worthy problem, as premature scaling or branding cannot compensate for a lack of genuine market need.

Operational focus is identified as a critical success factor, with the recommendation that early-stage companies align their entire team around a single, clear metric. While this approach can create a high-pressure, "thrashy" environment, it ensures organizational alignment. Finally, for co-founders who are not the CEO, the guidance emphasizes the necessity of subordinating ego to maintain a unified front. Disagreements must be resolved privately to prevent internal instability, acknowledging that the ultimate decision-making authority must reside with one individual to ensure company cohesion.

The emergence of highly capable AI models has fundamentally altered the software development landscape, shifting the focus from manual product creation to the optimization of automated "software factories." In this new paradigm, intelligence is treated as an abundant, accessible utility, enabling founders to iterate and improve products at unprecedented speeds. The core thesis posits that the ability to build systems that constantly acquire and process signals—effectively creating a self-improving loop—is now the primary driver of competitive advantage.

Key findings emphasize that the traditional model of product-led growth is evolving into "agent-led growth." Data indicates that autonomous agents are increasingly discovering, evaluating, and purchasing software products on behalf of their human users, often bypassing traditional marketing funnels. Consequently, companies are moving away from deploying hundreds of disparate internal agents toward centralized, unified intelligence systems that function as a foundational "Jarvis-like" peer for every employee, handling everything from onboarding to strategic communication.

Methodologically, these insights are derived from real-world operational experiments at Vercel, where the company transitioned from decentralized agent experimentation to a singular, company-wide intelligence infrastructure. The analysis concludes that in a market saturated with AI-generated options, long-term defensibility no longer stems from the models themselves, but from the quality of the surrounding "software factory." Success now depends on a company’s ability to implement robust verification loops, maintain high standards of trust, and execute rapid, high-signal improvements that differentiate their output in an increasingly automated ecosystem.

The discussion centers on the evolving role of software engineering in the era of autonomous AI agents, featuring insights from Peter Steinberger, creator of the open-source project OpenClaw. The primary thesis posits that modern development has shifted from manual coding to the architectural design of agentic workflows. Success in this new paradigm requires developers to act as executives or system architects, creating robust pipelines that allow agents to execute tasks, review their own output, and manage routine maintenance without constant human intervention.

Key findings emphasize that effective agent management requires a fundamental shift in mindset, moving away from micromanagement toward optimizing for high-level outcomes. Steinberger notes that while agents can navigate large codebases, they struggle with holistic system understanding and complex feature integration. Consequently, human oversight remains critical for strategic direction and maintaining product vision. Furthermore, the analysis highlights the importance of authenticity in communication; as AI-generated content becomes ubiquitous, maintaining a distinct human voice and providing proof of effort are essential for cutting through digital noise.

The scope of these insights covers the intersection of software development, open-source project management, and personal productivity within the global tech industry as of August 2026. The methodology relies on qualitative expert testimony and practical experience from a seasoned entrepreneur. A central conclusion is that building for external critics rather than personal utility can degrade product quality and diminish the developer's creative joy. Ultimately, the most successful implementations of agentic workflows are those where the builder remains deeply engaged with the product, using agents to handle repetitive noise while reserving human focus for high-impact, strategic decision-making.

Public relations for early-stage startups is often misunderstood as a primary engine for customer acquisition, when it should instead be viewed as a strategic test of a company’s narrative. The core thesis is that founders should avoid premature media engagement—referred to as hitting the nitrous oxide too soon—until they have achieved sufficient product-market fit and a clear, validated story. Engaging with the press before a company is ready often results in wasted resources and ineffective messaging.

Rather than a distribution channel, media relations serves as a crucible for refining a startup's value proposition. By pitching to journalists, founders can determine if their story resonates with an audience that has no inherent incentive to be supportive. This process acts as a forcing function to align the company’s internal vision with an external, intentional audience. While coverage can provide valuable signaling for fundraising and recruitment, it is rarely the most efficient path to immediate customer growth.

The modern landscape has shifted toward a direct-to-audience model, where founders are increasingly expected to build in public and manage their own narratives through personal platforms. This approach reduces reliance on traditional media gatekeepers, though it requires founders to be highly disciplined in identifying where their specific customers reside. Whether targeting niche industry publications or leveraging personal social channels, the strategy must be tailored to the audience rather than broad tech-sector visibility. Ultimately, founders are advised to seek professional guidance for messaging and execution rather than attempting to manage complex media processes independently, ensuring that their public-facing story is both credible and strategically sound.

The decision to raise venture capital is a strategic choice that carries significant structural implications for a business, rather than a universal requirement for growth. The primary thesis is that venture capital functions as "jet fuel"—a powerful, high-stakes resource designed to accelerate companies with massive scale potential. When applied to businesses that are already stable, profitable, or operating on a smaller scale, this capital can be counterproductive or even destructive. Founders are encouraged to evaluate whether their business model aligns with the venture capital expectation of rapid, high-growth trajectories and an eventual exit through acquisition or public offering.

Key considerations for founders include the "ego tax" associated with fundraising, where entrepreneurs may seek capital for perceived credibility rather than actual operational necessity. The analysis highlights that the modern economic landscape has shifted; advancements in AI and lean operational tools now allow small teams to achieve significant revenue milestones without the need for external funding. Consequently, the traditional argument that capital is required for headcount or infrastructure is increasingly scrutinized.

The guidance emphasizes that venture capital is not a renewable resource but a high-pressure commitment that introduces new stakeholders—investors—to the company’s governance. Founders are advised to distinguish between building a sustainable, profitable business and building a venture-backed startup. Ultimately, the decision to raise should be predicated on a clear, long-term vision for a high-growth exit, as the structural requirements of venture funding are not suitable for every company or founder’s personal goals.

The essay argues that the long‑standing startup norm of pairing founders with co‑founders is shifting, driven largely by advances in AI that enable highly capable solo founders to build high‑growth companies. Recent Stripe Atlas data shows 63 % of new C‑level founders in Q2 2026 are solo, a record high. Stripe economics reports that the share of businesses reaching $1 million in revenue within a year rose 30 % for the 2025 cohort versus 2023, and that solo founders now clear top income thresholds at roughly double the rate of two‑founder teams over the past two years. Academic research from Harvard and INSEAD demonstrates that AI‑native startups complete 12 % more tasks, acquire paying customers 18 % faster, and generate 1.9× higher revenue while cutting capital needs by nearly 40 %. However, the data also reveal a widening performance gap: top‑decile solo founders earned 34× more than median solo founders in 2022, a figure that grew to 61× by 2025. By month 24, top‑decile multi‑founder firms outpace solo counterparts by 53 % in revenue, underscoring that while AI can compensate for missing skills, a complementary human partner still adds significant value. The piece concludes that solo founding is defensible for exceptionally talented individuals, but securing an elite co‑founder remains a high‑bar signal to investors and a source of critical friction that AI cannot replicate.

The article outlines seven common pitfalls early‑stage founders encounter while pursuing their first sales deals, offering practical remedies to accelerate traction. It argues that founders should prioritize rapid customer engagement over product perfection, emphasizing hypothesis testing and iterative feedback as essential to achieving product‑market fit. The piece cautions against underpricing, urging founders to set initial prices that reflect value and test market willingness rather than defaulting to low rates for fear of rejection. It stresses the importance of identifying the economic buyer early, ensuring that enthusiasm translates into budget authority and contract closure. Active listening is highlighted as a key skill; founders are advised to allocate 80 % of conversation time to questions, capturing customer language to refine messaging. Diversifying prospects is recommended over chasing a single high‑profile lead, as reliance on one deal can stall progress. The author encourages continuous evolution of the pitch, noting that each interaction should inform adjustments to messaging and feature emphasis. Finally, the article advises founders to maintain sales responsibilities until a repeatable process is established before hiring dedicated sales talent, arguing that founder involvement shapes product direction and credibility. The guidance draws on observations from a16z speedrun’s early‑stage cohort, aiming to help founders close deals faster and generate the traction metrics that attract investors.

The article explains how founders should decide when and how to pivot in the rapidly evolving AI landscape, drawing on advice from a16z speedrun investors Emily Bennett and Troy Kirwin. It defines a pivot as maintaining core assets—such as the team, capital, or customer insights—while shifting product direction. The piece cites three recent pivots: Clay’s shift to a growth‑team tool that grew from $0 to over $100 M ARR, Lovable’s move from a command‑line prototype to a polished product for non‑technical users that achieved $100 M ARR in eight months, and Cursor’s transition from CAD AI to coding assistance after founders realized they were better suited as customers in that space. These examples illustrate the importance of staying true to learned insights while exploring new markets.

Key guidance includes testing market pull early, conducting rapid customer conversations (e.g., 50 calls in two weeks), and using qualitative depth before scaling with A/B tests. Bennett stresses that founders should not ignore signals of lack of demand and should view pivoting as a sign of agility rather than failure. Kirwin encourages founders to embrace truth‑seeking, even when it means abandoning a venture that is not scaling.

The article targets early‑stage founders globally, with no specific geographic or industry limits, and relies on anecdotal case studies rather than quantitative surveys. It frames pivoting as a core skill for founders in the AI era, emphasizing timely decision‑making and leveraging existing team strengths to navigate new opportunities.

The article reports on the 2026 New York Tech Week, a large industry gathering that attracted approximately 50,000 attendees across more than 1,500 events. The coverage focuses on observations from the a16z speedrun team, who hosted sessions and interviewed founders and investors during the week. Key insights highlight a cultural lag in AI adoption between New York and San Francisco, with New York firms adopting Claude‑based tools 4–6 months behind their Bay Area counterparts. The narrative also underscores New York’s preference for localized, referral‑driven networking over broad platform lists, and a desire for more intimate, anonymized events.

The piece spotlights several event formats that resonated with participants. HubSpot for Startups ran a blind pitch session where investor Kenan Saleh provided feedback without seeing the founders, while student hackathons drew significant participation—Cindy Morand’s “vibe code & tea” event received 1,100 requests and produced 143 finished apps. Founder‑centric gatherings such as a16z’s “Culture Brunch” and ElevenLabs’ founder‑only pop‑up fostered candid discussions. The article also notes the growing prominence of Substack among creators, as it balances professional depth with a less commercial feel than Instagram or TikTok.

Overall, the report suggests that New York Tech Week successfully blended large‑scale networking with niche, founder‑focused programming, and that the city’s startup ecosystem is rapidly catching up to San Francisco in AI enthusiasm while maintaining a distinct, community‑driven culture.