The post "O(n) vs. O(n^2) Startups" argues that startups can be categorized by how their complexity scales with the number of users (n). O(n) startups, like Instagram or TikTok, benefit from network effects where each additional user adds value linearly, often through content creation or consumption. Their operational costs scale proportionally with user growth. In contrast, O(n^2) startups, exemplified by marketplaces like Uber or Airbnb, involve facilitating interactions between users. This creates quadratic complexity, as each new user adds potential connections with every other user, leading to scaling challenges in matching, trust, and logistics. Consequently, O(n^2) startups often face higher operational burdens and slower growth compared to O(n) businesses. The post concludes that identifying a startup's complexity scaling characteristic early on helps in understanding its inherent growth potential and the likely challenges it will face.
Bell Labs' success stemmed from a unique combination of factors. Monopoly profits from AT&T provided ample, patient funding, allowing researchers to pursue long-term, fundamental research without immediate commercial pressure. This financial stability fostered a culture of intellectual freedom and collaboration, attracting top talent across diverse disciplines. Management prioritized basic research and tolerated failure, understanding that groundbreaking innovations often arise from unexpected avenues. The resulting environment, coupled with a clear mission tied to improving communication technology, led to a remarkable string of inventions that shaped the modern world.
Hacker News users discuss factors contributing to Bell Labs' success, highlighting management's commitment to long-term fundamental research, a culture of intellectual freedom and collaboration, and the unique historical context of AT&T's regulated monopoly status, which provided stable funding. Some commenters draw parallels to Xerox PARC, noting similar successes hampered by parent companies' inability to capitalize on innovations. Others emphasize the importance of consistent funding, the freedom to pursue curiosity-driven research, and the density of talented individuals, while acknowledging the difficulty of replicating such an environment today. A few comments express skepticism about the "golden age" narrative, pointing to potential downsides of Bell Labs' structure, and suggest that modern research ecosystems, despite their flaws, offer more diverse avenues for innovation. Several users mention the book "The Idea Factory" as a good resource for further understanding Bell Labs' history and success.
Getting things done in large tech companies requires understanding their unique dynamics. These organizations prioritize alignment and buy-in, necessitating clear communication and stakeholder management. Instead of focusing solely on individual task completion, success lies in building consensus and navigating complex approval processes. This often involves influencing without authority, making the case for your ideas through data and compelling narratives, and patiently shepherding initiatives through multiple layers of review. While seemingly bureaucratic, these processes aim to minimize risk and ensure company-wide coherence. Therefore, effectively "getting things done" means prioritizing influence, collaboration, and navigating organizational complexities over simply checking off individual to-dos.
Hacker News users discussed the challenges of applying Getting Things Done (GTD) in large organizations. Several commenters pointed out that GTD assumes individual agency, which is often limited in corporate settings where dependencies, meetings, and shifting priorities controlled by others make personal productivity systems less effective. Some suggested adapting GTD principles to focus on managing energy and attention rather than tasks, and emphasizing communication and negotiation with stakeholders. Others highlighted the importance of aligning personal goals with company objectives and focusing on high-impact tasks. A few commenters felt GTD was simply not applicable in large corporate environments, advocating for alternative strategies focused on influence and navigating organizational complexity. There was also discussion about the role of management in creating an environment conducive to productivity, with some suggesting that GTD could be beneficial if leadership adopted and supported its principles.
OpenAI is restructuring to better pursue its mission of ensuring safe and beneficial artificial general intelligence (AGI). They're creating two new entities: "OpenAI Nonprofit" will continue to guide their mission, fund open-source AI research, and advocate for responsible AI development. "OpenAI LP," a capped-profit company, will conduct product development and other commercial activities. This structure allows them to raise capital for computationally intensive AGI research while ensuring that any excess returns beyond the cap will flow back to the nonprofit for the benefit of humanity. This change reflects their evolving needs and commitment to prioritizing their long-term mission over immediate profits.
HN commenters express skepticism and concern about OpenAI's restructuring announcement. Many see it as a power grab by Sam Altman and Ilya Sutskever, consolidating control under the guise of AGI development urgency. Some speculate about internal conflicts and the possibility of Altman positioning OpenAI for acquisition by Microsoft. Others question the sincerity of their stated mission, given the perceived shift towards commercial interests. Several commenters also criticize the lack of transparency and specific details in the announcement, calling it vague and performative. A few express cautious optimism, hoping the changes will lead to faster AGI progress, but the overall sentiment is one of distrust and apprehension about the future direction of OpenAI.
"Accountability Sinks" describes how certain individuals or organizational structures absorb blame without consequence, hindering true accountability. These "sinks" can be individuals, like a perpetually apologetic middle manager, or systems, like bureaucratic processes or complex software. They create an illusion of accountability by seemingly accepting responsibility, but prevent real change because the root causes of problems remain unaddressed. This ultimately protects those truly responsible and perpetuates dysfunctional behaviors, leading to decreased efficiency, lower morale, and a culture of learned helplessness. Instead of relying on accountability sinks, organizations should prioritize identifying and addressing systemic issues and cultivating a culture of genuine responsibility.
Hacker News users discussed the concept of "accountability sinks," where individuals or teams are burdened with responsibility but lack the authority to effect change. Several commenters shared personal experiences with this phenomenon, particularly in corporate settings. Some highlighted the frustration and burnout that can result from being held accountable for outcomes they cannot control. Others discussed the difficulty of identifying these sinks, suggesting they often arise from unclear organizational structures or power imbalances. The idea of "responsibility without authority" resonated with many, with some proposing strategies for navigating these situations, including clearly defining roles and responsibilities, escalating issues to higher levels of authority, and documenting the disconnect between accountability and control. A few commenters questioned the overall premise of the article, arguing that true accountability necessitates some level of authority.
The author argues that abstract architectural discussions about microservices are often unproductive. Instead of focusing on theoretical benefits and drawbacks, conversations should center on concrete business problems and how microservices might address them. Architects tend to get bogged down in ideal scenarios and complex diagrams, losing sight of the practicalities of implementation and the potential negative impact on team productivity. The author advocates for a more pragmatic, iterative approach, starting with a monolith and gradually decomposing it into microservices only when justified by specific business needs, like scaling particular functionalities or enabling independent deployments. This shift in focus from theoretical architecture to measurable business value ensures that microservices serve the organization, not the other way around.
Hacker News commenters generally agreed with the author's premise that architects often over-engineer microservice architectures. Several pointed out that the drive towards microservices often comes from vendors pushing their products and technologies, rather than actual business needs. Some argued that "architect" has become a diluted title, often held by those lacking practical experience. A compelling argument raised was that good architecture should be invisible, enabling developers, rather than dictating complex structures. Others shared anecdotes of overly complex microservice implementations that created more problems than they solved, emphasizing the importance of starting simple and evolving as needed. A few commenters, however, defended the role of architects, suggesting that the article painted with too broad a brush and that experienced architects can add significant value.
This 2015 blog post outlines the key differences between Managers, Directors, and VPs, focusing on how their responsibilities and impact evolve with seniority. Managers are responsible for doing – directly contributing to the work and managing individual contributors. Directors shift to getting things done through others, managing managers and owning larger projects or initiatives. VPs are responsible for setting direction and influencing the organization strategically, managing multiple directors and owning entire functional areas. The post emphasizes that upward movement isn't simply about more responsibility, but a fundamental shift in focus from tactical execution to strategic leadership.
HN users generally found the linked article's definitions of manager, director, and VP roles accurate and helpful, especially for those transitioning into management. Several commenters emphasized the importance of influence and leverage as key differentiators between the levels. One commenter highlighted the "multiplier effect" of higher-level roles, where impact isn't solely from individual contribution but from enabling others. Some discussion revolved around the varying definitions of these titles across companies, with some noting that "director" can be a particularly nebulous term. Others pointed out the emotional labor involved in management and the necessity of advocating for your team. A few commenters also shared their own experiences and anecdotes that supported the article's claims.
Jo Freeman's "The Tyranny of Structurelessness" argues that informal power structures inevitably arise in groups claiming to be structureless. While intending to promote equality and avoid hierarchy, the absence of formal procedures and explicit roles actually empowers a hidden "elite" who influence decisions through informal networks and pre-existing social capital. This informal power is difficult to challenge because it's unacknowledged and therefore lacks accountability. The essay advocates for consciously creating explicit structures and processes within groups to ensure genuine participation and distribute power more equitably, making decision-making transparent and enabling members to hold leaders accountable.
HN commenters discuss Jo Freeman's "The Tyranny of Structurelessness," largely agreeing with its core premise. Several highlight the inherent power dynamics that emerge in supposedly structureless groups, often favoring those with pre-existing social capital or manipulative tendencies. Some offer examples of this phenomenon in open-source projects and online communities. The "tyranny of the urgent" is mentioned as a related concept, where immediate tasks overshadow long-term planning and strategic decision-making. A few commenters question the binary presented in the essay, suggesting more nuanced approaches to structure and leadership, like rotating roles or distributed authority. The essay's age and continued relevance are also noted, with some arguing that its insights are even more applicable in the decentralized digital age.
The original poster is exploring alternative company structures, specifically cooperatives (co-ops), for a SaaS business and seeking others' experiences with this model. They're interested in understanding the practicalities, benefits, and drawbacks of running a SaaS as a co-op, particularly concerning attracting investment, distributing profits, and maintaining developer motivation. They wonder if the inherent democratic nature of co-ops might hinder rapid decision-making, a crucial aspect of the competitive SaaS landscape. Essentially, they're questioning whether the co-op model is compatible with the demands of building and scaling a successful SaaS company.
Several commenters on the Hacker News thread discuss their experiences with or thoughts on alternative company models for SaaS, particularly co-ops. Some express skepticism about the scalability of co-ops for SaaS due to the capital-intensive nature of the business and the potential difficulty in attracting and retaining top talent without competitive salaries and equity. Others share examples of successful co-ops, highlighting the benefits of shared ownership, democratic decision-making, and profit-sharing. A few commenters suggest hybrid models, combining aspects of co-ops with traditional structures to balance the need for both stability and shared benefits. Some also point out the importance of clearly defining roles and responsibilities within a co-op to avoid common pitfalls. Finally, several comments emphasize the crucial role of shared values and a strong commitment to the co-op model for long-term success.
James Shore envisions the ideal product engineering organization as a collaborative, learning-focused environment prioritizing customer value. Small, cross-functional teams with full ownership over their products would operate with minimal process, empowered to make independent decisions. A culture of continuous learning and improvement, fueled by frequent experimentation and reflection, would drive innovation. Technical excellence wouldn't be a goal in itself, but a necessary means to rapidly and reliably deliver value. This organization would excel at adaptable planning, embracing change and prioritizing outcomes over rigid roadmaps. Ultimately, it would be a fulfilling and joyful place to work, attracting and retaining top talent.
HN commenters largely agree with James Shore's vision of a strong product engineering organization, emphasizing small, empowered teams, a focus on learning and improvement, and minimal process overhead. Several express skepticism about achieving this ideal in larger organizations due to ingrained hierarchies and the perceived need for control. Some suggest that Shore's model might be better suited for smaller companies or specific teams within larger ones. The most compelling comments highlight the tension between autonomy and standardization, particularly regarding tools and technologies, and the importance of trust and psychological safety for truly effective teamwork. A few commenters also point out the critical role of product vision and leadership in guiding these empowered teams, lest they become fragmented and inefficient.
Summary of Comments ( 6 )
https://news.ycombinator.com/item?id=43991962
HN commenters largely agree with the author's premise of O(n) (impact scales linearly with users) vs. O(n^2) (impact scales with user interactions) startups. Several highlight the difficulty of building O(n^2) businesses due to the network effect hurdle. Some offer examples, categorizing companies like Uber/Doordash as O(n), marketplaces/social networks as O(n^2), and open source software/content creation as O(n) with potential O(n^2) community aspects. A few commenters point out that the framework oversimplifies reality, as growth isn't always so neatly defined, and successful businesses often blend elements of both. Some also argue that "impact" is a subjective metric and might be better replaced with something quantifiable like revenue. The difficulty of scaling trust in O(n^2) models is also mentioned.
The Hacker News post titled "O(n) vs. O(n^2) Startups" (https://news.ycombinator.com/item?id=43991962) sparked a discussion with several interesting comments focusing on the article's core concept of scaling challenges related to different startup types.
Several commenters agreed with the author's premise that certain startup models, like marketplaces or platforms, inherently involve more complex interactions, symbolized by O(n^2) complexity, making scaling more difficult. One commenter highlighted the importance of recognizing these scaling challenges early on, as they can significantly impact resource allocation and overall strategy. Another commenter appreciated the clear articulation of this distinction, suggesting that the O(n) vs. O(n^2) analogy provides a useful framework for understanding the inherent complexities of various business models.
A few commenters offered nuanced perspectives on the original article's simplification. One noted that while the broad categorization is helpful, real-world scenarios often involve a mix of O(n) and O(n^2) characteristics. For example, a seemingly linear business might have hidden quadratic complexities in customer support or other internal processes. Another commenter pointed out that even within the O(n^2) category, there are varying degrees of complexity. A marketplace with a limited number of distinct product categories might scale differently than one with a vast and ever-changing inventory.
The discussion also touched upon strategies for mitigating the challenges of O(n^2) businesses. One commenter suggested that focusing on specific niches or segments can help reduce the complexity of interactions, effectively making the scaling problem more manageable. Another discussed how technological advancements, particularly in areas like AI and automation, could play a crucial role in streamlining these interactions and enabling more efficient scaling.
One commenter drew a parallel to the concept of "n-body problems" in physics, emphasizing the exponential increase in computational complexity as the number of interacting entities grows. This analogy further reinforced the difficulty of scaling businesses with intricate network effects.
Overall, the comments section provides valuable insights into the complexities of startup scaling, expanding upon the article's core idea and offering practical considerations for entrepreneurs navigating these challenges. The discussion highlights the importance of understanding the inherent scaling characteristics of different business models and proactively addressing the potential complexities they present.