The Networking Game in Silicon Valley's Elite Circles: Those with Connections Get $50 Million, While the Truly Talented Can't Raise Money?

marsbitPublished on 2026-07-10Last updated on 2026-07-10

Abstract

"Silicon Valley's Meritocracy to Relationship Game: How Networks Now Trump Talent." The article argues that Silicon Valley has shifted from a meritocracy to a "kingmaker" system where connections and background outweigh true ability. Key factors driving this change include: 1. **AI-Distorted Expectations:** Unprecedented growth curves (e.g., Anthropic) have led VCs to seek only "sure things" or pattern-match against past successes. 2. **Capital Concentration:** LP funds are concentrated in a few large, multi-stage funds, pushing VCs to overpay for hot deals to secure capital. 3. **VC Professionalization:** The industry has become a standardized career path, attracting conformist "NPCs" rather than independent thinkers. The long IPO timeline incentivizes safe, consensus bets for career advancement over risky, fund-returning outliers. This consensus capital fuels consensus founders. Startups are now a standard career option, with accelerators pressuring uniform ideas (e.g., 81% AI). Founders from elite schools (Stanford, OpenAI) easily raise millions based on pedigree, not proof. Large funds preemptively back "centrally cast" teams with $10-50M war chests to dominate categories, sidelining outsiders. The "kingmaker" strategy has downstream effects: it encourages aggressive, sometimes fraudulent, revenue reporting and allows founders to sell significant secondary shares early, attracting grifters. The author predicts a mean reversion. History shows the hottest trends rare...

Author: Shower Thoughts

Compilation: Deep Chao TechFlow

Deep Chao Guide: Silicon Valley is shifting from meritocracy to connections-based favoritism. Founders with Stanford backgrounds raise funds effortlessly, VCs preemptively invest $50 million in 'centrally cast' teams to create hype, while truly capable outsiders struggle to raise capital. This approach may work short-term, but ultimately, it will lose to the underestimated outliers—those who follow the herd are waiting to be slaughtered.

Peter Thiel often asks variations of this question: "In a given environment, what is it that you cannot say?" In the evangelical-dominated South, it's dangerous to be gay or liberal. On university campuses, it's dangerous to be conservative.

In Silicon Valley, the unassailable dogma is meritocracy.

Silicon Valley has long prided itself on meritocracy. Outsiders with no background or connections could emerge, build generational companies, and be rewarded for it. The industry has always been proud of being 2,851 miles away from Washington D.C.—a place notorious for getting things done through lobbying and insider connections.

Today, success in Silicon Valley depends on who you know and how willing they are to prop you up.

This is no different from how any other old-money industry operates. In East Coast high finance, you need the right elite school. In British politics, you need the right family surname.

How did Silicon Valley go from meritocracy to king-making?

Consensus Groupthink

It's no secret that Silicon Valley thinking has become extremely consensus-driven in recent years. This is mainly due to 1) AI distorting growth expectations, 2) LP capital concentration, and 3) the professionalization of the venture capital industry.

First, AI has completely distorted expectations for revenue growth. For the first time in history, we're seeing startups go from $0 to $100 million ARR in one to two years. Compared to the SaaS era, sustained three-year growth would get your company to an IPO. Even more extreme is growth on the scale of Anthropic—from $9 billion ARR in December 2025 to $47 billion ARR by May 2026 (adding up the annual revenues of Palantir, Snowflake, and CoreWeave along the way), which is unprecedented.

Well-known VCs now say never invest in diamonds in the rough. Either wait for an inflection point and try to get into the hottest companies, or try to pattern-match past successes and back a new company early. The former is the right strategy for growth-stage investing; the latter is a mistake. I'll explain why later and how this affects founders.

Second, LP capital has concentrated into the hands of a few established multi-stage franchise funds. In the first half of last year, 12 VCs took 50% of all LP capital. This is largely a reaction to over-allocation to the venture asset class in 2021-2022 and a flight to "quality" brand names that institutional allocators don't have to risk their careers defending in IC meetings. Family office LPs, in particular, are especially keen on getting into hot Silicon Valley companies, regardless of valuation. If a VC fund must pay a high price for a tiny stake in a hot company to secure LP capital, then so be it.

Third, the culture of the VC industry has shifted from a boutique craft to a mature career path. Over a decade ago, venture capital was a craft. Like a medieval guild, VCs followed an apprenticeship model, where seasoned GPs trained young junior VCs to develop the taste for judging founder quality and the feel for market timing.

Over time, the VC industry has professionalized into another standard career path. Previously, it was 2 years of investment banking → 2 years of business school → private equity. Now it's 2 years at a big tech company → 2 years at a high-growth startup → venture capital. Once there's a standard career path, it attracts excellent sheep NPCs who follow the herd, rather than the intensely independent thinkers the industry relies on to make contrarian bets.

Given that IPO timelines are longer than ever, thus extending feedback cycles, getting into hot companies (not necessarily the best companies!) is a better strategy for promotion within a VC firm. Mid-level VCs would rather quickly and easily get markups from safe consensus bets than risk betting on a potential fund returner. Large VC firms also have higher turnover than ever, so they might not even be at the firm in a few years to get carry on that fund-returning deal.

Consensus Money Attracts Consensus Founders

One would think the typical startup founder is an intensely non-conformist rebel, carving their own path in the world, who doesn't care at all what the establishment thinks. These founders are often polarizing to their peers, ignore their boss's instructions, and would be fired from structured corporate jobs. But that's less the case now.

Starting a company is becoming a more standard career option, no different from big tech or consulting. A contributing factor is the high unemployment rate for new college graduates looking for entry-level white-collar jobs, which are shrinking due to AI. Instead of struggling in the job search, apply to a startup accelerator, treat it like an internship program, burn through $500k, have fun, and figure out adult life.

The Stanford Review once wrote that YC is for cowards. With YC increasing from 2 batches a year to 4 (about 800 startups per year!), plus the explosion in the number of other accelerator programs, it's no surprise that the typical startup founder has become more uniform and less an unorthodox outlier.

Accelerators pressure startups to be legible to VCs by demo day, so startups naturally wandering through the idea maze trying to find product-market fit tend to build in the most obvious, crowded categories that already work. 81% of the current YC batch are doing AI for XYZ. Crypto startups are building new banks for stablecoins in XYZ region or prediction markets for XYZ niche. Consensus VCs fund these consensus ideas because they feel safe and familiar, easily pattern-matching to what already works. But the truth is the best companies define new categories and start years before that category is obvious or even has a name.

For founders not going through accelerators, a good background is more important than ever. Anyone who went to Stanford can raise money. Anyone spinning out of OpenAI can raise money. Check size and valuation are a function of how good the pedigree is and how well-connected the founder is within VC circles.

Beyond that, large multi-stage funds are giving a set of centrally cast characters (i.e., those with the best pedigrees) war chests of $10 million to $50 million to crown a category before their company has traction, making it hard for others who aren't centrally cast to win those markets.

So now it's not "Can you build a great business?" but "Can you fit the mold that large VC firms want to fund?"

An insular inner circle of soul-less individuals—those with background and connections getting preferential treatment—goes against the meritocratic idea that any skilled, hardworking entrepreneur can win. Meritocracy historically gave Silicon Valley its halo, the only place in America where the American Dream still exists and works. Today, Silicon Valley is becoming more like Wall Street or K Street.

Founders outside the network now feel they have to play "the game" to become one of the centrally cast. This means schmoozing with VC associates at happy hours and dinners, acting slightly autistic to create FOMO and fundraising momentum. Normally, founders networking with VCs is a waste of time; they should be focused on building the company and talking to customers. Now it's all part of the game, an extra skill founders must cultivate.

Downstream Effects of King-Making

To be fair, king-making does work to some extent. Raising massive capital gives you a huge war chest to acquire customers at a loss (i.e., unprofitably until your competitors go bankrupt or pivot). It scares other teams away from entering your market.

However, king-making also creates moral hazard for bad behavior. Companies get ~creative~ in reporting revenue, and founders sell secondary shares early.

King-making pressures companies to show revenue growth at all costs to be legible to VCs. This leads some companies to outright fabricate revenue (securities fraud) or get creative with methodologies. One example is taking a one-off contract and annualizing it into ARR. These contracts are often just pilot pricing with exit clauses, so they are ironically neither "annual," "recurring," nor even "revenue." Another example is rebranding ARR from "annual recurring revenue" to "annual run rate" and calculating ARR as last week's revenue × 52 or even last day's revenue × 365. This isn't exactly securities fraud, but it doesn't look good to anyone doing diligence.

VCs trying to king-make a competitive round often allow founders to sell secondary shares to win the deal. Apparently, 10% of a hot company's round going to founder secondary shares is now common practice. The downstream effect of founder secondary shares is that it attracts scammers. Those who can play the "game" described earlier well to create VC FOMO for a Series A and use it to sell millions in founder secondary shares (often more than the company's lifetime revenue) and then slowly rug.

Mean Reversion

The pendulum has swung too far toward consensus today, and I bet there will be a mean reversion toward contrarian thinking.

History repeatedly shows that the hottest theme in any given year is not the category of the most valuable company founded that year. I have no reason to believe this time will be different.

I'd rather back outsiders with chips on their shoulders 24/7 than insiders prematurely crowned by VCs. I believe there's a huge blind spot outside the Silicon Valley groupthink bubble—great founders without backgrounds, outside the distribution, illegible to most VCs.

I'm optimistic that meritocracy will ultimately win, and those chasing momentum playing the king-making game will be left licking their wounds.

Those who follow the herd are waiting to be slaughtered.

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Related Questions

QAccording to the article, how has Silicon Valley's principle of meritocracy transformed in recent years?

AThe article argues that Silicon Valley has transformed from a meritocracy, where success was based on talent and hard work, into a 'kingmaking game' where results depend heavily on who you know and your background, such as attending elite schools like Stanford or having connections to firms like OpenAI. It compares this new dynamic to the relationship-driven cultures of Wall Street and Washington D.C.

QWhat are the three main factors the author cites for the shift towards consensus thinking in Silicon Valley venture capital?

AThe author cites three main factors: 1) AI has distorted growth expectations, making VCs chase only proven 'inflection points' or pattern-match to past successes. 2) LP capital has concentrated into a few large, established multi-stage franchise funds, which drives investments into high-profile 'safe' deals. 3) The VC industry has professionalized into a standard career path, attracting conformist 'NPCs' rather than independent thinkers, and internal promotion incentives favor safe consensus bets over risky, potentially high-return outliers.

QWhat negative downstream effects does the 'kingmaking' approach to funding have on startup behavior, as described in the article?

AThe 'kingmaking' approach creates moral hazard and encourages bad behavior. It pressures companies to show explosive growth at all costs, leading to creative or fraudulent revenue reporting (e.g., annualizing one-time pilot contracts, redefining ARR as 'annual run rate' based on a single day's revenue). It also attracts founders who are good at the 'game' of creating VC FOMO, allowing them to sell large secondary shares early and potentially 'rug' or abandon the project slowly after cashing out.

QHow has the profile of a typical startup founder changed, according to the author's analysis?

AThe author argues the typical founder is no longer a rebellious, non-conformist outlier. Startups have become a standard career option, akin to big tech or consulting, especially for graduates facing high unemployment. The proliferation of accelerators (like YC expanding batches) pressures founders to work on easily understood, consensus ideas (e.g., 'AI for XYZ'), creating more uniform founders. Success now often depends on fitting the mold of what large VC firms want to fund—having a prestigious background and networking skills—rather than purely on building a great business.

QWhat is the author's final prediction regarding the current trend of consensus and kingmaking in Silicon Valley investment?

AThe author predicts a mean reversion towards contrarian thinking. They argue that history shows the hottest trends in a given year do not produce its most valuable companies. They express a belief that meritocracy will ultimately win, and that great founders exist as 'outliers' outside the Silicon Valley groupthink bubble. The final line is a warning: 'Those who chase momentum, get ready to be slaughtered.'

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Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

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