2026 Crypto Funding Reshuffle: Game and DePIN Are Dead, Prediction Market Duo Takes 18% of All Year's Funding with Two Deals

marsbitPublished on 2026-05-08Last updated on 2026-05-08

Abstract

Cryptocurrency Funding in 2026: Gaming & DePIN Falter as Prediction Markets Dominate Data from the first four months of 2026 reveals a stark shift in crypto venture funding. The gaming and DePIN (Decentralized Physical Infrastructure Networks) sectors have seen capital nearly dry up. In contrast, the "Consumer" category, led by two massive deals for prediction market platforms Kalshi ($1B) and Polymarket ($600M), captured a significant share. These two deals alone accounted for 18% of the year's total $8.65 billion raised and exceeded the combined funding of all 47 DeFi projects. Overall, the $8.65B across 305 deals is misleading. A March surge to $4.57B was largely due to two major acquisitions (BVNK at $1.8B and Kalshi). Excluding these, the underlying monthly funding rate is approximately $1B, indicating continued softness. The "Payments" and "Consumer" sectors together consumed 72% of all capital. Another notable trend is the rise of mergers and acquisitions (M&A), with 48 deals nearly matching the 57 seed-round investments. This signals a market pivot from funding new ideas to consolidating around established leaders. The most active investors so far in 2026 are Coinbase Ventures (18 deals), Tether (13 deals), Animoca Brands (11 deals), and GSR (11 deals). Notably, a16z's pace has slowed significantly compared to previous years.

Author:Memento Research

Compiled by: Deep Tide TechFlow

Deep Tide TechFlow Introduction: Crypto funding data for the first four months of 2026 reveals a harsh reality: the Game and DePIN sectors are nearly starved of capital, while Kalshi and Polymarket, two prediction market companies, have taken more money than all DeFi projects combined for the entire year. More alarmingly, the number of M&A deals has already matched seed rounds, indicating a shift in capital from betting on new ideas to acquiring existing leaders.

Funding Overview: March's Surge Was an Illusion

From January 1st to May 6th, 2026, the crypto industry completed 305 funding rounds, totaling $8.65 billion. However, the "surge" to $4.57 billion in March was actually just two massive M&A deals: BVNK's $1.8 billion and Kalshi's $1.0 billion.

Excluding these two deals, the real funding pace is about $1 billion per month, even weaker than at the end of 2025.

Capital Flow: Payments and Consumer Absorb 72%

By sector breakdown:

Payments: $3.74 billion (56 deals)

Consumer: $2.48 billion (35 deals)

DeFi: $1.06 billion (47 deals, the highest number of transactions)

The Payments and Consumer sectors combined account for 72% of the year's total funding. Funding for Game and DePIN has nearly vanished.

Prediction Markets Dominate the Consumer Sector

Two prediction market companies accounted for 18% of the year's total funding:

Kalshi: $1.0 billion

Polymarket: $600 million

These two deals totaling $1.6 billion exceed the sum of all 47 DeFi funding rounds.

M&A Becomes Mainstream

M&A deals reached 48 (accounting for 23% of known-stage transactions), nearly matching the 57 seed rounds (27%). This cycle has shifted from investing in new ideas in early stages to acquiring industry leaders.

Investor Rankings Reshuffled

Most active funds in 2026:

Coinbase Ventures: 18 deals (ranked second during 2021-26 period)

Tether: 13 deals (new top lead investor)

Animoca Brands: 11 deals (ranked first during 2021-26 period)

GSR: 11 deals

a16z: 7 deals (a significant drop compared to ~200 deals during 2021-26 period)

Related Questions

QAccording to the article, which two sectors took the majority of crypto funding in early 2026?

AThe Payments and Consumer sectors took the majority of crypto funding, together accounting for 72% of the total capital raised.

QWhat significant trend does the data reveal about mergers and acquisitions (M&A) compared to seed funding?

AThe data shows that M&A deals reached 48 (23% of known-stage deals), almost catching up to seed rounds at 57 deals (27%). This indicates a shift in the investment cycle from funding new ideas to acquiring established industry leaders.

QWhy was the spike in funding for March 2026 described as an 'illusion'?

AThe spike to $4.57 billion in March was largely an illusion because it was driven by just two mega-deals: an $1.8 billion acquisition of BVNK and a $1 billion deal with Kalshi. Removing these two transactions reveals a slower, more sluggish monthly funding pace of around $1 billion.

QHow did the funding for prediction market companies compare to the total funding for all DeFi projects in the period covered?

AThe two prediction market companies, Kalshi ($1 billion) and Polymarket ($600 million), together raised a combined $1.6 billion. This amount exceeded the total raised by all 47 DeFi projects, which was $1.06 billion.

QWhich investment firms were the most active in terms of deal count during early 2026, and how did this compare to their historical activity?

AIn early 2026, the most active investors by deal count were Coinbase Ventures (18 deals, historically ranked 2nd from 2021-26), Tether (13 deals, new top investor), Animoca Brands (11 deals, historically ranked 1st), GSR (11 deals), and a16z (7 deals, showing a significant decline from its historical ~200 deals between 2021-26).

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