Startup Funding Predictions 2026: What's Getting Funded

The 2026 VC Landscape

Global venture capital investment is projected at approximately $350-400 billion in 2026, a meaningful recovery from the $285 billion low of 2023 but still below the $643 billion peak of 2021. The recovery is real but selective -- capital is flowing disproportionately to AI-related startups while many sectors remain starved of funding.

The composition of VC investment has shifted dramatically. In 2021, consumer-facing startups commanded roughly 40% of funding. In 2026, that share has dropped below 25%, replaced by enterprise software, AI infrastructure, and deep tech. Prediction markets on predict.codes reflect this shift, with enterprise and AI-focused markets generating the highest trading volume.

The VC ecosystem itself is transforming. Mega-funds ($1B+) from firms like Andreessen Horowitz, Sequoia, and Tiger Global continue to dominate late-stage funding, but a new generation of specialized micro-funds ($50-200M) is emerging to fill gaps in early-stage funding for specific verticals. Corporate venture capital (CVC) from technology giants is also increasingly important, particularly for AI startups seeking strategic partnerships alongside capital.

AI Startup Funding: The $100B+ Category

AI startups absorbed an estimated $100+ billion in venture and growth-stage funding in 2025, and 2026 is on track to exceed that figure. The AI funding boom is the most concentrated capital deployment into a single technology category in venture capital history, surpassing the dot-com era in both absolute dollars and relative share.

The AI Funding Stack

AI funding is distributed across several distinct layers, each with different risk profiles and prediction market dynamics:

• Foundation model companies. The most capital-intensive layer. Companies like OpenAI, Anthropic, xAI, and Mistral are raising multi-billion-dollar rounds to fund compute infrastructure and model training. Prediction markets track which foundation model companies will raise their next round, at what valuation, and from which investors. "Will a foundation model company raise a round at $100B+ valuation by end of 2026?" trades at 55% YES.
• AI infrastructure and tooling. Companies building the picks-and-shovels of the AI revolution: vector databases (Pinecone, Weaviate), MLOps platforms (Weights & Biases, MLflow), GPU cloud providers (CoreWeave, Lambda), and developer tools (Vercel AI SDK, LangChain). This layer attracts $15-25 billion annually and is generally seen as lower-risk than the model layer.
• AI application layer. Companies applying AI to specific verticals: legal (Harvey AI), healthcare (Abridge), coding (Cursor, Replit), customer support (Intercom, Zendesk AI), and hundreds more. This is the broadest and most competitive layer, with thousands of startups competing for market position. Prediction markets here focus on which vertical applications will achieve product-market fit.
• AI agents and autonomous systems. The emerging frontier of AI startups building systems that can take actions, not just generate text. This category is the most speculative but also the most actively traded on prediction markets, given the enormous uncertainty about timeline and capabilities.

AI Funding Risks

The unprecedented concentration of capital in AI creates significant risks that prediction markets are pricing:

• Revenue gap. AI startups have raised far more capital than they generate in revenue. The gap between AI funding and AI revenue is estimated at 5-10x, compared to 2-3x for typical VC-backed startups. Markets price the probability of a significant AI startup failure (valued at $1B+) in 2026 at 45% YES.
• Concentration risk. If the AI bubble deflates, the entire VC ecosystem is affected because AI represents such a large share of total funding. Prediction markets on sector-level outcomes (total AI funding volume, median AI valuation multiples) are heavily traded.
• Regulatory risk. AI regulation in the EU (AI Act enforcement), US, and other jurisdictions could constrain certain AI business models. Markets on regulatory outcomes directly affect AI startup valuations.

Climate Tech: From Niche to Necessity

Climate tech funding has grown from $20 billion in 2020 to approximately $70 billion in 2026, making it the second-largest funding category after AI. Unlike AI, climate tech funding is less concentrated in a few mega-deals and more distributed across diverse sub-sectors.

Where Climate Capital Is Flowing

• Energy storage. Battery technology startups (solid-state, sodium-ion, iron-air) are attracting $15+ billion in 2026. Grid-scale storage and EV battery innovation are the primary use cases. Prediction markets on predict.autos track EV battery technology adoption, creating cross-domain trading opportunities.
• Carbon capture and removal. Direct air capture (DAC) and enhanced weathering startups are scaling from pilot to commercial operations. Prediction markets price the probability of a DAC facility capturing 100,000+ tons annually by end of 2027 at 35% YES.
• Sustainable agriculture. Precision agriculture, alternative proteins, and regenerative farming startups are addressing agriculture's 25% share of global emissions. Markets on predict.garden track agricultural technology adoption.
• Green hydrogen. Electrolyzer startups and green hydrogen infrastructure companies are raising billions to support the transition from fossil fuels in industrial processes. This is one of the most capital-intensive and uncertain climate tech categories.

Climate Tech Prediction Markets

"Will climate tech funding exceed $80 billion globally in 2026?" trades at 42% YES on predict.codes. "Will a climate tech startup achieve a $10B+ valuation in 2026?" trades at 50% YES. The uncertainty in climate tech markets reflects the category's dependence on policy (carbon pricing, subsidies, mandates) as much as technology.

Fintech Evolution: Infrastructure Over Consumer

Fintech, once the darling of venture capital, is undergoing a fundamental transformation in 2026. Consumer fintech (neobanks, buy-now-pay-later, trading apps) has largely fallen out of favor after the implosion of several high-profile consumer fintech companies. Infrastructure fintech, however, is thriving.

The Infrastructure Fintech Thesis

The thesis driving fintech infrastructure funding is straightforward: regardless of which consumer-facing financial products succeed, they all need infrastructure. Payments processing, compliance automation, fraud detection, banking-as-a-service, and cross-border payment rails are essential regardless of which consumer brands win.

Companies like Stripe, Plaid, Adyen, and their competitors continue to grow because their infrastructure is embedded in thousands of other companies' products. Prediction markets track specific infrastructure metrics: "Will Stripe process more than $1.5 trillion in payments in 2026?" trades at 60% YES. "Will real-time payments exceed 30% of US electronic payments by end of 2026?" sits at 28% YES.

Crypto-Fintech Convergence

The convergence of traditional fintech and crypto infrastructure is one of the most actively traded themes on predict.codes. Stablecoin payment rails, tokenized real-world assets, and institutional crypto custody are attracting significant funding from VCs who previously avoided crypto. Prediction markets on crypto-fintech outcomes directly intersect with markets across the Predict Network, particularly on crypto adoption and regulation.

Biotech: AI-Powered Drug Discovery

Biotech funding in 2026 is increasingly intertwined with AI. The convergence of artificial intelligence and drug discovery has created a new category -- AI-biotech -- that is attracting capital from both traditional biotech investors and AI-focused funds.

AI Drug Discovery Landscape

Companies like Recursion Pharmaceuticals, Insilico Medicine, and Isomorphic Labs (a Google DeepMind spinout) are using AI to dramatically accelerate the drug discovery process. Traditional drug discovery takes 10-15 years and costs $2-3 billion per approved drug. AI-powered approaches aim to cut both timelines and costs by 50% or more through computational screening of drug candidates, protein structure prediction, and clinical trial optimization.

Prediction markets on predict.codes are highly active on AI-biotech outcomes. "Will an AI-discovered drug receive FDA approval by end of 2027?" trades at 32% YES. "Will AI-biotech companies attract more than $25 billion in funding in 2026?" sits at 45% YES. These markets reflect genuine uncertainty about whether AI's theoretical advantages in drug discovery will translate to approved therapies.

Beyond Drug Discovery

Biotech funding extends well beyond AI drug discovery. Gene therapy, CRISPR-based treatments, mRNA platforms (building on COVID vaccine technology), and synthetic biology are all attracting significant capital. The mRNA platform market alone is projected to generate $50+ billion in revenue by 2030, driving continued investment in the technology's expansion beyond vaccines into cancer treatment, rare diseases, and autoimmune conditions.

The IPO Window: Opening or Closing?

The IPO market is one of the most consequential prediction market topics for the entire VC ecosystem. IPOs provide the liquidity that allows VCs to return capital to their limited partners, who then recycle that capital into new funds. A closed IPO window constrains the entire venture capital cycle.

2026 IPO Outlook

After the near-total closure of the tech IPO window in 2022-2023 and a tentative reopening in 2024-2025, 2026 is shaping up as a pivotal year. Several factors support an IPO recovery: interest rates have stabilized, public market valuations for tech companies have recovered, and a large backlog of VC-backed companies with $100M+ revenue are IPO-eligible.

Prediction markets are actively trading specific IPO outcomes. "Will more than 50 VC-backed tech companies IPO in the US in 2026?" trades at 48% YES. Markets on specific company IPOs (Stripe, Databricks, Canva, Discord) are among the highest-volume markets on predict.codes. These markets move on financial results, market conditions, and insider signals.

SPAC and Direct Listing Alternatives

SPACs have largely fallen out of favor after the 2021-2022 debacle, but direct listings and other alternative paths to public markets remain relevant. Prediction markets price the probability of a major tech company using a direct listing in 2026 at 35% YES. The choice of listing mechanism provides signal about company confidence and market conditions.

Dry Powder and Down Rounds

An estimated $300+ billion in VC dry powder (raised but uninvested capital) remains available in 2026. This overhang creates a complex dynamic: there is plenty of capital available, but VCs are being more selective about deployment, leading to a bifurcated market where hot companies (AI, climate) receive intense competition from investors while companies in less fashionable categories struggle to raise at any valuation.

The Down Round Reality

Approximately 25-30% of funding rounds in 2026 are down rounds (at lower valuations than the previous round), compared to a historical average of 10-15%. The down round prevalence reflects the valuation correction from 2021's excesses and creates specific prediction market opportunities. Markets tracking whether specific unicorns will raise down rounds generate significant trading volume, as these events signal broader market health.

Trading Startup Markets on predict.codes

Startup and VC prediction markets reward traders who synthesize information across multiple sources:

1. Crunchbase and PitchBook data. Funding round announcements, valuation data, and investor activity provide the raw material for startup prediction markets. Track deal flow velocity, median round sizes, and sector allocation for leading indicators.
2. SEC filings. For late-stage companies approaching IPO, SEC filings (S-1, 10-K equivalents for pre-IPO companies, Reg D filings) contain financial data that moves prediction markets.
3. Job postings. A company's hiring velocity is a leading indicator of funding (companies hire aggressively after raising) and growth (declining job postings signal trouble). Track postings on LinkedIn and Glassdoor.
4. Product launches and customer announcements. Enterprise startup traction (new customers, partnerships, product launches) drives both funding and IPO timing. ProductHunt launches, press releases, and industry conference appearances provide signals.
5. Macro indicators. Interest rates, public market valuations, and risk appetite directly affect VC funding pace and IPO feasibility. Federal Reserve decisions and market volatility move startup prediction markets as much as company-specific events.

2026 Funding Forecast

Based on prediction market data from the Predict Network:

• Global VC funding will reach $350-400 billion in 2026 (58% probability for this range), a meaningful recovery from 2023-2024 lows.
• AI will account for more than 30% of total VC funding (65% probability), the highest concentration in any single technology category since the dot-com era.
• More than 30 tech unicorns will IPO in the US in 2026 (45% probability), with Stripe and Databricks as the most-watched candidates.
• At least one AI startup will raise at $100B+ valuation (55% probability), setting a new record for private company valuations.
• Climate tech funding will exceed $70 billion globally (52% probability), driven by policy support and corporate sustainability commitments.
• Down rounds will decline as a share of total rounds from 25-30% to 18-22% by Q4 2026 (48% probability), signaling market normalization.

The startup ecosystem in 2026 is emerging from its correction phase into a new growth cycle -- one more concentrated, more AI-centric, and more demanding of unit economics than the 2020-2021 boom. Prediction markets on predict.codes are the venue where this transition is priced in real time.

For related analysis, see our Programming Language Predictions 2026 and AI Industry Predictions 2026. For cross-domain opportunities, explore beauty industry markets on predict.beauty.