2026 Crypto Financing Reshuffle: Gaming and DePIN Are Dead, Two Prediction Market Deals Gobble Up 18% of Yearly Funding

marsbitPublicado a 2026-05-09Actualizado a 2026-05-09

Resumen

Crypto Funding Shakeup in 2026: Gaming & DePIN Fade, Prediction Markets Dominate Analysis of crypto funding from January 1 to May 6, 2026, reveals a major sectoral shift. The industry raised $8.65 billion across 305 deals. However, a March surge to $4.57 billion was largely due to two mega-deals: BVNK's $1.8 billion acquisition and a $1 billion raise by Kalshi. Excluding these, monthly funding is a sluggish ~$1 billion. Capital concentration is extreme. The Payments ($3.74B) and Consumer ($2.48B) sectors absorbed 72% of all funds. Within Consumer, prediction markets were dominant: Kalshi's $1B and Polymarket's $600M raises together accounted for 18% of the year's total, surpassing all 47 DeFi deals combined ($1.06B). In contrast, Gaming and DePIN sectors saw funding nearly vanish. A strategic pivot is underway. Merger & Acquisition (M&A) activity reached 48 deals, nearly matching the 57 seed-stage rounds. This indicates capital is increasingly flowing toward acquiring established leaders rather than betting on new ideas. 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 its 2021-2026 average.

Author: Memento Research

Compilation: TechFlow Deep Tides

Deep Tides Intro: Crypto financing data from the first four months of 2026 reveals a brutal reality: funding for the gaming and DePIN sectors has nearly dried up, 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 caught up with seed rounds, indicating that capital is shifting from betting on new ideas to acquiring existing industry leaders.

Financing Overview: The March Surge Was Just an Illusion

From January 1 to May 6, 2026, the crypto industry completed 305 financing rounds, totaling $8.65 billion. However, the "surge" of $4.57 billion in March was essentially just two mega M&A deals: BVNK's $1.8 billion and Kalshi's $1 billion.

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

Fund Flow: Payments and Consumer Gobble Up 72%

By sector:

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 together account for 72% of the year's funding. Financing for gaming and DePIN has almost vanished.

Prediction Markets Dominate the Consumer Sector

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

Kalshi: $1 billion

Polymarket: $600 million

These two deals total $1.6 billion, exceeding the combined total of all 47 DeFi financing rounds.

M&A Becomes Mainstream

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

Investor Rankings Reshuffled

Most active funds in 2026:

Coinbase Ventures: 18 deals (Ranked 2nd during 2021-26)

Tether: 13 deals (New top lead investor)

Animoca Brands: 11 deals (Ranked 1st during 2021-26)

GSR: 11 deals

a16z: 7 deals (Significantly down from ~200 deals during 2021-26)

Preguntas relacionadas

QWhat were the two major M&A deals that dominated the funding figures for March 2026, and what were their values?

AThe two major M&A deals were BVNK, which raised $1.8 billion, and Kalshi, which raised $1 billion.

QWhich two sectors accounted for 72% of total funding in early 2026, and what were the respective funding amounts?

AThe Payments and Consumer sectors accounted for 72% of total funding. Payments raised $3.74 billion across 56 deals, while Consumer raised $2.48 billion across 35 deals.

QHow much funding did the prediction market companies Kalshi and Polymarket receive collectively, and what percentage of the year's total funds does this represent?

AKalshi and Polymarket collectively received $1.6 billion in funding, which represents 18% of the total funds raised in early 2026.

QWhat significant trend is highlighted by the number of M&A transactions (48) nearly matching the number of seed rounds (57) in early 2026?

AThe trend highlights a market shift where capital is moving away from funding new ideas (seed-stage investments) and towards acquiring existing market leaders (M&A).

QAccording to the article, which were the most active investment funds in early 2026 by number of deals?

AThe most active funds were Coinbase Ventures with 18 deals, Tether with 13 deals, and Animoca Brands and GSR with 11 deals each.

Lecturas Relacionadas

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

Trading

Spot
活动图片