AI Startup Funding Opportunities

We reviewed 785 pitches in the last three months and invested in six. Three things each of the six teams got right: 1. Deep domain knowledge that is impossible to fake The best founders, even those who are average presenters, can make VCs immediately understand what they're building and why it matters, even if the concept is highly technical. Deep domain knowledge translates into a "let me show you" narrative when other founders are stuck at "I think" analogies. That depth shows up most clearly when the questions get hard. One team was grilled on free, open-source models in their space. How do they not kill you? They didn't just explain how and why their product was better, but walked us through how several of their large early clients had already used those open-source models before coming to them. Tried them, weren't impressed, and switched to their solution. Calm credibility wins term sheets. 2. Early signals of outlier potential Poor differentiation from existing solutions, no meaningful traction, no team advantage specific to that problem, or no product edge that couldn't be replicated in six months with sufficient funding. All common reasons why VCs reject investment opportunities. Pre-seed is our sweet spot, and many of these teams were pre-revenue. But not pre-potential. Each of the six teams didn't just claim they were different, they proved it. One had locked in a proprietary data relationship that took a competitor ten years and $50m to build. Another had independently benchmarked its core technology against the market leader, and won. A third gained paying enterprise customers within weeks of launching, in a space where most startups spend a year chasing pilots. 3. Ripe market opportunities We see many me-too pitches, or ones where the why now argument boils down to "now we have AI". AI is of course a driving factor for disruption and the genesis for many new startups, but everyone has the same paint brush. Each of the six teams we invested in had specific examples showing why their windows of opportunity were absolutely ripe, right now. One team pointed to the fact that their customers were already doing manually what their product systematizes, i.e. the behavior existed, the tool didn't. Another pointed to inference costs. Running their core process eighteen months ago cost $4 per query. Today it costs fractions of a cent. A third identified a structural gap: every enterprise is now deploying AI agents internally, but none of those agents can communicate across company boundaries. A specific infrastructure layer became urgently necessary precisely because adoption moved faster than anyone built the connective tissue. Three different industries, three completely different timing arguments, and one clear message: the window just opened, and it won't stay open long. 779 teams could not convincingly address these three areas. If you're pitching to a VC, make sure you can.

“If it’s not AI, I don’t want it” – a VC headed to Monaco for summer Q2'25 data* shows AI companies are securing significantly larger rounds across sectors, with median deal sizes hitting $4.6M – over $1M above the broader market. In Q2’25, the AI premium was strongest in Auto Tech which saw AI companies securing deals $20.6M larger than traditional peers (lead by Applied Intuition's $600M Series F at $15B valuation), followed by Robotics and Cybersecurity with median deal premiums of $10.7M and $6.4M respectively. The AI premium extends beyond funding to company performance and trajectory metrics. AI companies consistently score higher on our Mosaic Score (success probability) and Commercial Maturity (ability to compete and partner) metrics, proving their fundamentals justify investor confidence. Why are AI companies commanding these premiums? 1) Capital-intensive development cycles AI companies often require dramatically more upfront investment for compute infrastructure, data acquisition, and model training before achieving product-market fit, necessitating larger initial rounds to reach meaningful milestones. 2) Longer runway to defensibility Unlike traditional SaaS where competitive advantages emerge quickly, AI companies need 12-18 months of continuous model refinement and data collection to build meaningful moats, requiring sustained funding through extended R&D phases. 3) Premium for hybrid expertise The most successful AI companies combine rare AI/ML talent with deep domain expertise (like automotive engineers for autonomous driving), creating interdisciplinary teams that command higher compensation. 4) Infrastructure-first business models AI companies often build foundational platforms (like simulation environments or data processing pipelines) that require significant upfront investment but can later support multiple product lines and customer segments. The AI premium continues to reflect investors' "go big or go home" approach; making concentrated bets on AI teams they believe can capture outsized market share. The AI premium signals more than just funding enthusiasm – it's recognition that AI-first companies are simultaneously disrupting the last two decades of companies and building the infrastructure for tomorrow's economy. *Data from CB Insights’ State of Venture Q2’25 report. Explore the latest data on what happened last quarter across the startup ecosystem at the link in the comments.

Companies waste millions on AI products that turn out to be vaporware. I have been simmering and seasoning this AI product evaluation framework for 12 years. My clients need innovative AI tools that deliver competitive advantages, so it’s not feasible to reject startups altogether. Here are my assessment points. ✅ The startup knows something about the market or your needs that no one else does. They discuss your problems and desired outcomes like they’ve worked at your company. ✅ They explain how early design partners and limited releases led to improvements and new features. They share early outcomes from both, and the result metrics align with your strategic goals. ✅ The solution makes sense, and demos are focused on functionality, not just technology. They are transparent about the product or platform’s weaknesses and gaps and have plans to address them. ✅ They ask questions during the demo to better understand your needs and showcase the most relevant functionality based on your answers. ✅ They have built competitive advantages with data, and the platform or product delivers functionality that competitors can’t. ✅ They have a platform or product roadmap and admit it isn’t set in stone. However, they can provide a clear vision for the product or platform. ✅ The company has a low burn rate, path to profitability, or strong financials that indicate it will be around for several years. ✅ Their service level agreements, data management practices, contract/pricing structures, etc., are mature and built for enterprises vs. consumers. ✅ They have an implementation/integration roadmap and provide initial support or onboarding. The company doesn’t just drop and run or rely 100% on chatbot support. My book and articles provide more frameworks to help businesses navigate the emerging AI tools landscape. Follow me here or use the link under my name to access my library. #GenerativeAI #AIStrategy

🚨The youngest-ever partner at Bain Capital Ventures, Rak Garg oversees AI and cybersecurity investments, and has also launched BCV Labs, an AI incubator for the firm. He joins us for VC Wednesdays.🚨 ✒️How has being a product manager made you a better investor? Early-stage founders are typically the first and only product managers at the company. Being an investor means getting signal from very noisy inputs — discerning, especially in AI where there are 50 companies that all do the same thing, who has made better product choices and has product intuition. That's what a product manager does. I built that muscle at Atlassian and have carried it over to investing. ✒️How do you evaluate differentiation when so many AI startups look similar? We look for domain depth. Not every founder has direct industry experience, but we want someone who can demonstrate the ability to absorb depth — maybe they’ve done 100 interviews in the last three months. Second, point products have a hard time scaling. If your product only helps with drafting in legaltech, you might get into one aspect of the workflow but have a hard time growing. The strategy and tactics will change, but we want founders ambitious enough to want to dominate their market. ✒️ What are some recent AI investments that you’ve made, and why? The AI ecosystem has become more application-focused, largely because the infrastructure has gotten so good. When we first invested in unstructured.io, they were building core infrastructure, training models and hiring data labelers. Today, they've moved up the stack and are a workflow platform that integrates with all the other models. The trend has played out in different industries, for example in cybersecurity, where companies like Prophet Security are leveraging AI in ways that cuts down a security operator's work. Another recent investment is Lightswitch, which is using AI to automate property management. ✒️ Are enterprises actually ready to adopt AI? There are two sides of the coin — functional and vertical. Functionally, we’re seeing companies like Decagon and Sierra do tremendously well, as there’s no barrier to automating customer support. In design, there’s companies like krea.ai that have been able to displace one-off contracts by letting people design assets themselves. Where adoption has been slower than expected are where you have heavily data-dependent workflows. ✒️ What’s one investment that you regret passing on? We had multiple shots at Anthropic, and didn't take them. Foundation models on their own are a horrific business, where you spend hundreds of millions of dollars training, only to be beaten by another lab two weeks later. But Anthropic has done a good job moving up the stack, and are spiking in the enterprise and among coders. The lesson is that many markets aren't winner-take-all, and developer tools are one example of a market where you can have multiple winners. #VCWednesdays #vc #venturecapital #startups #TechonLinkedIn

Investor Lens: What Investors Look for in a Slow-Growth Market In a slow-growth market, capital becomes more selective. The question investors ask is no longer just, “How fast can this company grow?” It is also, “How much capital does it take to create that growth?” That shift matters. In H2 2026, smart money is likely to move toward startups that can prove disciplined execution, stronger margins, and clearer paths to sustainable scale. Growth still matters. But growth without efficiency is becoming harder to defend. The companies that stand out will be the ones that can show: 🌟 Clear unit economics Founders need to understand CAC, payback periods, gross margins, retention, and contribution margin early. 🌟 Revenue quality Not all revenue is equal. Recurring revenue, strong retention, enterprise expansion, and repeatable demand matter more than one-off spikes. 🌟 Lean operating discipline Teams that can do more with less will have an advantage. 🌟 Faster path to profitability Profitability does not mean slowing ambition. It means building a business that can survive changing market cycles. 🌟 Stronger customer urgency In slower markets, buyers delay nice-to-have purchases. Startups solving urgent problems around cost reduction, automation, productivity, compliance, and operational efficiency will remain more resilient. 🌟 AI as leverage AI will matter most where it improves speed, reduces manual work, sharpens decision-making, or increases margins. Moving Forward In a slower market, capital efficiency is not a defensive strategy. It is a signal of founder quality. The strongest startups will not be the ones spending the most to look like they are scaling. They will be the ones building durable growth with discipline, focus, and measurable outcomes. For investors, H2 2026 will not just be about finding the fastest companies. It will be about finding the companies that know how to compound intelligently. Where do you think smart money will move in the second half of 2026? Arise Ventures Avinash Vashistha Tholons Inc. Frank Pendle Shari Wenker StrongHer - by Arise Ventures #InvestorLens #CapitalEfficiency #StartupGrowth #VentureCapital #FounderJourney #StartupFunding #AIStartups #EnterpriseAI #Startups #AriseVentures #StrongHer

There’s the right way & the wrong way to do it…. Most founders think raising funding for an AI startup starts with pitching to investors It doesn’t…. The best AI ventures raise capital by first proving one thing: customers are willing to pay In today’s market, investors are flooded with AI pitches 🔹 A great demo is no longer enough 🔹 A clever use of LLMs is no longer enough 🔹 Even a talented team is often not enough So how do they pay attention to yours? ✅ A clear business problem ✅ Evidence that customers need the solution ✅ Early revenue or strong customer commitments ✅ A repeatable path to growth So the first steps before chasing venture capital is to focus on: 1. Building the smallest version of the product that solves a real problem 2. Getting it into the hands of customers quickly 3. Generating measurable outcomes & testimonials 4. Refining the business model until customers consistently see value The strongest fundraising story isn’t “Look at our technology.” It’s “Look at the market traction we’ve already created.” 📈 Investors fund momentum Customers create momentum Build for customers first, & fundraising becomes a conversation about scaling success rather than funding an idea What’s the biggest fundraising lesson you’ve learned as a founder?

A simple trick I use to evaluate AI startups: I put my finger over the word "AI" in their pitch deck and ask: "Does this still make sense as a business?" After seeing hundreds of AI pitches, here's what I've learned: 1. Strong companies pass this test easily: - Clear revenue model remains intact - Customer pain point is still obvious - Value proposition stands on its own - Unit economics make sense 2. Red flags emerge when: - The entire business case vanishes without "AI" - The go-to-market strategy becomes unclear - The competitive advantage disappears - The pricing model falls apart 3. Why this matters: AI should enhance a solid business, not be its only selling point. Just like cloud computing or mobile, it's a powerful tool—not a business model. AI is an incredible accelerant. It's like rocket fuel. But the rocket needs to be pointed in the right direction to start! 🎯 For founders pitching AI companies: Before your next investor meeting, try this test yourself. If covering "AI" breaks your story, you might need to rethink your fundamentals. #VentureCapital #AI #Startups #Investing

Just got out of a partner meeting with 3 top-tier VCs. Here’s what they’re really thinking about AI startups right now. Short version? The gold rush is over. Now they want picks, shovels, and cash flow. Here’s what came out of the room—unfiltered: 1. “We’re drowning in wrappers.” Everyone’s pitching an LLM layer with a sexy UI. But most have zero retention, no moats, and fragile margins. If OpenAI ships your roadmap in their next release, you're done. 2. “We’re looking for real workflow ownership.” Founders who deeply understand a specific, painful, expensive use case— and wedge into it with AI as a 10x unlock, not just a buzzword. Think: — AI that shortens sales cycles — AI that cuts payroll costs — AI that eliminates compliance risk Not “AI that writes tweets.” 3. “Infra fatigue is setting in.” Infra plays are sexy—until they’re commoditized. If you’re building infra, you need deep tech defensibility and a built-in GTM wedge (dev comms, plugin ecosystems, etc.). 4. “We want revenue, not just razzle.” No one cares about your demo anymore. They want paying customers, usage growth, and retention curves that don’t nosedive after day 3. 5. “Boring is the new hot.” The loudest decks are getting ignored. The quiet ones—solving dusty problems in healthcare, logistics, procurement, etc.—are getting partner-level attention. VCs aren’t anti-AI. They’re anti-hype. They’ve seen too many founders chase the model, not the market. So if you’re building in AI in 2025—don’t lead with the tech. Lead with the pain, the wedge, and the money flow. That’s what gets funded now. ♻ Repost to update the ecosystem. 🔔 Follow Anshuman Sinha for more Startup insights. #Startups #AI #VentureCapital #Entrepreneurship #LeanStartups

Deep tech investors are often not domain experts. So, how do they make sure they are investing in the right startups?   Technical due diligence (TDD).   It’s how VCs de-risk investments and assess a startup’s technological foundations, without needing to be AI researchers or quantum physicists themselves.   At APEX Ventures, we’ve been investing in deep tech for over a decade, with 50+ active portfolio companies and over 8000 startups assessed.   We know exactly what investors should be looking for during a due diligence round.   TDD is not to find flaws in startups, but rather to build clarity for both investors and founders. Done right, it strengthens the foundation for future growth. Here’s what we focus on at the core of TDD:   𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 Think of it like the engine of a car. If it runs smoothly, it tells you a lot about the startup’s potential. We assess: – Does the tech solve a real-world problem? – Is it built to scale? – What’s the USP and how easily can it be replicated? – Is it robust enough for future growth?   𝗧𝗲𝗮𝗺 The best tech still needs the right team behind it. We evaluate: – Engineering structure & leadership – Hiring plans and scaling strategy – Culture, onboarding, and technical learning velocity   𝗣𝗿𝗼𝗱𝘂𝗰𝘁 Understanding the product itself from all angles helps in gauging its potential and future direction. – Roadmap clarity ↳ Are upcoming iterations well thought out or reactive? – Decision-making process ↳ Who decides what to build—and why? – Value attribution ↳ Are priorities driven by impact or inertia? – Execution timeline ↳ Can the team reliably ship what they commit to?   𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 Even the best product vision fails without strong delivery operations. We assess: – Development process from idea to deployment – Estimation accuracy and iteration rhythm – Transparency in reporting progress and blockers – Automation maturity (CI/CD, test coverage, infra-as-code, etc.)   At APEX Ventures, our TDD process is adaptable across sectors, from AI to healthcare to climate tech. But our principles remain the same: clarity, rigor, and long-term readiness. If you're a VC evaluating a deep tech investment or a founder preparing for diligence, this is the kind of structure that turns unknowns into informed confidence.   #Venturecapital #AI #Deeptech #Startups   Follow us for strategies and resources for Deep Tech founders and VCs!   And get access to exclusive content on deep tech startups like ATMOS Space Cargo, planqc, smedo GmbH, and SENISCA in our newsletter:   https://t2m.io/EV2qHQuo

Hard truth for founders in 2026: The fact that you can build a product is no longer impressive. Crunchbase put it bluntly this week: technical expertise no longer differentiates founders because anyone can ship a product in a weekend with AI tools. Obviously, quality varies. The question investors are now asking isn't "can they build it?" It's "do they know something nobody else knows?" I've seen this shift happen in real time at Recursive Ventures. Two years ago, a founder who could demo a working AI product got a meeting. Today, that's table stakes. The demo is expected. The question is what's underneath it. What I'm actually evaluating now: → Founder-market insight: do they have a non-obvious view on why this problem matters NOW and AI can reshape it? → Distribution edge: do they know how to reach the buyer in a way that's not obvious? → Moat: Do they have access to something proprietary that makes their AI better? → Domain credibility: does the customer base trust them? The most dangerous thing a founder can do in 2026 is mistake product-building ability for competitive advantage. AI tools lowered the cost of building by 10x. That means the supply of "fundable-looking" companies went up 10x. But the bar for actual investment conviction went up even more. What do you think is the most underrated moat for an early-stage AI startup in 2026? #FounderAdvice #AIStartups #VentureCapital #PreSeed