a16z: Two Suggestions for Crypto Industry Builders in 2026

marsbitPublished on 2026-01-15Last updated on 2026-01-15

Abstract

In their 2026 outlook, a16z crypto partners offer two key recommendations for builders in the crypto space. First, Arianna Simpson argues that while many successful crypto companies are pivoting to trading platforms, this short-term strategy may dilute market focus and limit long-term competitiveness. Trading, though important, should be a transit point rather than the end goal. Founders are encouraged to focus on building substantive products rather than seeking immediate product-market fit through speculative token dynamics. Second, Miles Jennings highlights that regulatory clarity in the U.S. could resolve long-standing legal uncertainties that have hindered blockchain development. Historically, ambiguous securities laws forced projects to prioritize legal risk over product strategy, leading to non-transparent practices and distorted incentives. The anticipated passage of crypto market structure legislation may establish clear standards for token issuance, governance, and decentralization, enabling networks to operate as truly open, autonomous, and decentralized systems. Together, these insights urge builders to prioritize sustainable product development over short-term gains and welcome regulatory clarity as a catalyst for genuine innovation.

Author:a16z crypto

Compiled by: Deep Tide TechFlow

1. Transactions Can Be a Transit Point, Not the End Goal of Crypto Business

Today, aside from stablecoins and some core infrastructure, almost all well-performing crypto companies are transitioning or have already transitioned into trading platforms. But if "every crypto company becomes a trading platform," where does that leave everyone else? When so many players are doing the same thing, market attention becomes fragmented, ultimately leaving only a few big winners. This also means that companies that pivot to trading too early may miss the opportunity to build more competitive and enduring businesses.

I fully understand the efforts of all entrepreneurs to get their finances in order, but pursuing short-term product-market fit also comes at a cost. This is particularly true in the crypto industry, where the unique dynamics of tokens and speculation can lead founders down the path of instant gratification while searching for product-market fit... one might call it a "marshmallow test" (an experiment testing patience and self-control).

There's nothing wrong with trading itself—it's an important function in market operations—but it doesn't have to be the end goal. Founders who focus on the "product" aspect rather than merely chasing product-market fit may ultimately become bigger winners.

—Arianna Simpson (@AriannaSimpson), Partner at a16z Crypto Fund

2. This Year, Regulation Will Help Eliminate Industry Distortions

Over the past decade, one of the biggest obstacles to building blockchain networks in the United States has been legal uncertainty. Securities laws were stretched and selectively enforced, forcing founders into a regulatory framework designed for companies, not networks. For years, reducing legal risk replaced product strategy; the role of engineers was taken over by lawyers.

This dynamic led to many anomalies: founders were advised to avoid transparency; token distributions became legally arbitrary; governance became superficial; organizational structures were optimized for legal cover; and tokens were designed to avoid economic value or lacked a business model. Worse, crypto projects that circumvented the rules often developed faster than conscientious builders who followed them.

However, this year, the government is closer than ever to passing crypto market structure regulation, which could eliminate all these distortions. If legislation passes, it will incentivize transparency, establish clear standards, and replace "enforcement roulette" (random enforcement) with a clearer, structured path for fundraising, token issuance, and decentralization. After GENIUS (note: likely referring to a policy or event), the explosive growth of stablecoins has already become evident; and legislation on crypto market structure will bring even greater change, but this time for networks.

In other words, such regulation will enable blockchain networks to truly operate as networks—open, autonomous, composable, credibly neutral, and decentralized.

—Miles Jennings (@milesjennings), Head of a16z Crypto Policy Team and General Counsel

Related Questions

QAccording to Arianna Simpson, why might crypto companies that pivot to trading too early miss out on building more competitive and lasting businesses?

ABecause when many players are doing the same thing, market attention becomes fragmented, leaving only a few big winners, and the pursuit of short-term product-market fit can lead to immediate gratification at the expense of long-term competitiveness.

QWhat does Arianna Simpson suggest as an alternative focus for crypto founders instead of just pursuing product-market fit through trading?

AShe suggests that founders should focus on the 'product' part itself, rather than just pursuing product-market fit, which could lead to becoming bigger winners in the long run.

QWhat has been the biggest obstacle for blockchain network builders in the U.S. over the past decade, according to Miles Jennings?

AThe biggest obstacle has been legal uncertainty, where securities laws were stretched and selectively enforced, forcing founders into a regulatory framework designed for companies, not networks.

QHow did the legal uncertainty in the U.S. crypto space lead to 'weird distortions,' as described by Miles Jennings?

AIt led to founders being advised to avoid transparency, token distributions becoming legally arbitrary, governance becoming superficial, organizational structures being optimized for legal cover, and tokens being designed to avoid economic value or business models.

QWhat potential positive impact does Miles Jennings believe crypto market structure legislation could have if passed?

AIt could eliminate distortions by incentivizing transparency, establishing clear standards, and providing a clearer, structured path for fundraising, token issuance, and decentralization, allowing blockchain networks to operate as truly open, autonomous, composable, credibly neutral, and decentralized networks.

Related Reads

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit1h ago

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit1h ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit1h ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit1h ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit1h ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit1h ago

Trading

Spot
活动图片