Biggest US Funding Rounds: AI & Drones Surge

A massive week for big checks, led by AI and drones

The U.S. venture market just delivered another reminder that mega-rounds aren’t a relic of the past—they’re simply being concentrated into themes investors believe can reshape entire industries. This week’s funding lineup, tracked for the Jan. 17–23 window, shows that the biggest checks are still flowing toward AI-first companies, plus a smaller set of “physical world” platforms where automation can change cost structures in a visible way. In other words, the money is going to teams that either build foundational AI capabilities or apply AI and robotics to real-world distribution, logistics, and services that customers feel immediately.

From the standpoint of the ai world organisation, this is exactly the pattern we’ve been watching in boardrooms and founder communities: AI isn’t being treated like a single product category anymore. It’s becoming the default lens for building modern businesses—whether that business ships code, diagnoses disease, secures factories, or moves parcels through the sky. That’s why the ai world summit and ai world organisation events focus heavily on “applied AI,” not just model demos, and why ai conferences by ai world increasingly spotlight the operational side of AI—deployment, inference, reliability, and the economics of scaling.

This week’s largest raise underlines the point. Drone delivery provider Zipline closed more than $600 million at a multi-billion-dollar valuation, a sign that investors are hungry not only for AI software but also for platforms that can become part of everyday infrastructure. Meanwhile, the second-biggest round—an eye-catching $480 million seed—went to a new AI lab, Humans&, signaling that investors still have appetite for ambitious, early-stage bets when the team and vision feel big enough.

If you’re reading this as a founder, investor, marketing leader, or enterprise decision-maker, there are two immediate takeaways. First, the “AI premium” in fundraising remains very real, especially in categories that support scaling (infrastructure, inference, networking) or promise defensible data moats (healthcare, security, specialized exploration). Second, the market is rewarding stories that connect AI to measurable outcomes: faster delivery times, improved clinical decisions, stronger cybersecurity posture, or better unit economics for energy discovery.

At the ai world summit 2025 / 2026, these are the exact conversations that matter: where the funding is going, what business models it’s validating, and what it suggests about the next wave of winners. The point isn’t to chase hype; it’s to spot repeatable patterns—because the same categories that attract capital today often shape product roadmaps, partnership priorities, and go-to-market narratives tomorrow.

Zipline’s $600M: drone delivery moves from “cool” to “infrastructure”

The biggest round of the week went to Zipline, a drone delivery company that has long been associated with ambitious logistics, healthcare distribution, and last-mile innovation. This financing—more than $600 million—came with a valuation of $7.6 billion and backing from major names that included Fidelity, Baillie Gifford, Valor Equity Partners, and Tiger Global. More importantly, Zipline signaled expansion expectations, including plans to begin service in Houston and Phoenix and to enter at least four new U.S. states over the year.

For anyone tracking the commercialization arc of drones, this matters because it suggests investors see drone delivery as moving beyond pilot programs into something that can be operationalized at scale. In market terms, that means the conversation shifts away from “Can it fly?” and toward “Can it reliably deliver at a cost that makes sense, while meeting safety, regulatory, and customer experience expectations?” The fact that the week’s largest check went to a company in this category is a strong signal that VCs and growth investors are still excited by automation that touches the physical economy.

It’s also a reminder that the definition of “AI company” is widening. Many of the most valuable automation businesses won’t position themselves purely as AI startups, even if AI is deeply embedded in routing, autonomy, predictive maintenance, logistics forecasting, fleet operations, or customer experience. The market increasingly rewards teams that make AI invisible—where users don’t buy “AI,” they buy speed, reliability, and convenience.

From a brand and growth perspective, Zipline’s expansion into major metros like Houston and Phoenix also changes the communications playbook. When a product moves into everyday consumer contexts, marketing becomes less about futuristic storytelling and more about trust, safety, and consistency. This is where AI-enabled operations quietly support the front end: accurate ETAs, reliable service windows, clean handoffs, and minimal friction in ordering and receiving.

For the ai world organisation community, Zipline’s raise is also a useful case study for how deep tech narratives are sold in 2026. Investors are not just buying a drone; they’re buying a distribution network, an operational advantage, and a platform that can open new delivery categories over time. That “platform logic”—the idea that a core system can support many use cases—shows up repeatedly across the week’s funding rounds as well.

Humans& and the return of the ultra-ambitious AI lab

If Zipline represents investment in physical-world automation, Humans& represents the other pole of the current market: investors still love bold AI-native visions, especially when they come from builders with elite research credentials. Humans& raised $480 million in seed funding, positioning itself as an AI lab looking to apply the technology in ways centered around people and their relationships with each other. The company was founded in September by researchers with experience from heavyweight AI and tech organizations including Google, Anthropic, xAI, OpenAI, and Meta.

A seed round of this size is not just a funding event—it’s a narrative event. It tells the market that investors believe the next frontier of AI may depend on deeper understanding of human context, collaboration, and interaction, not only on bigger models. In simpler terms: after years of focusing on capability, speed, and scale, some capital is now chasing “meaningful use”—systems that fit into how people actually work together, communicate, make decisions, and build relationships inside organizations and communities.

This round also signals how much investor confidence is tied to teams and origin stories right now. In a world where AI capabilities can be acquired through APIs and open-source models, investors often look for differentiation through talent density, research depth, and the ability to build end-to-end systems that can evolve quickly. The market’s willingness to fund a newcomer at this level suggests that the “founding team effect” remains strong—especially when the team has been close to major breakthroughs and understands how frontier labs operate.

For founders and operators, Humans& also highlights a go-to-market challenge that will define many AI-first products in 2026: distribution and habit formation. Building a system that is “centered around people and relationships” is compelling, but it also requires earning daily usage and trust. That means careful product design, thoughtful privacy framing, and a clear explanation of how the system improves collaboration without creating new risks.

At the ai world summit, this is exactly the kind of discussion we expect to dominate panels and workshops: not “What can AI do?” but “How do you make AI genuinely useful in the real rhythms of work and community?” For the ai world organisation, the aim is to translate these headline funding moments into practical learnings for marketers, founders, agencies, and growth leaders who need frameworks—not buzzwords.

The infrastructure wave: Baseten and Upscale AI

The next set of large rounds points to a structural truth: as AI usage explodes, the supporting layers—deployment, inference, performance optimization, and networking—become just as investable as flashy applications. Two companies in this week’s top deals sit squarely in this “picks and shovels” layer of the AI boom.

Baseten, an AI infrastructure company, reportedly raised $300 million, with investors including IVP, CapitalG, and Nvidia, and the financing reportedly set a $5 billion valuation for the San Francisco-based company. In practical terms, the excitement around infrastructure comes from one central demand: getting models into production reliably. Training may grab headlines, but inference, deployment, observability, and cost optimization determine whether AI is profitable at scale.

This is why infrastructure startups are often evaluated through a different lens than consumer apps. Investors and customers look for signs of durable integration: how hard is it to remove the system once it’s embedded? Does it become part of the operational backbone? Does it improve developer velocity and reduce compute waste? A platform that makes inference smoother, faster, and cheaper can earn a meaningful position in the AI stack, especially as enterprises push from pilot projects to organization-wide adoption.

Upscale AI offers another angle on the same theme. The AI networking infrastructure startup raised $200 million in a Series A led by Tiger Global, Premji Invest, and Xora Innovation, reportedly setting a valuation north of $1 billion for a Santa Clara-based company that’s less than two years old. For the market, the message is clear: bottlenecks are shifting. As more teams deploy models, networking, throughput, and reliability become business-critical, not just technical nice-to-haves.

The presence of these infrastructure mega-rounds in a single week also reinforces a strategic point for go-to-market teams. When infrastructure is the product, marketing cannot rely on vague claims about “transformation.” The buyer wants specificity: how much faster, how much cheaper, how much more stable, and how quickly it integrates into existing workflows. Positioning must be measurable, proof-based, and anchored in real operational pain.

For the ai world organisation, this infrastructure emphasis is also a call to design better learning pathways at ai world organisation events. Many teams in agencies, brands, and startups now need a working understanding of inference economics, vendor lock-in risks, and how to evaluate AI platforms—because these decisions will shape content velocity, customer experience, and operational efficiency across the next few years. That’s also why ai conferences by ai world increasingly blend marketing strategy with technical practicality: you can’t market AI outcomes if the stack can’t deliver them reliably.

AI in medicine, security, education, and energy: where applications get serious

Beyond drones and infrastructure, the remaining deals in the roundup reflect how widely AI is being applied—and how investors are still willing to write big checks when the use case is clear, valuable, and defensible.

OpenEvidence, described as an AI platform for doctors, announced a $250 million Series D that doubled its valuation to $12 billion. The round was co-led by Thrive Capital and DST, and it marked the company’s fourth fundraise in less than a year, with the company based in Miami. In healthcare, the logic for rapid follow-on rounds often comes down to urgency and adoption: if a product can credibly help clinicians make faster, better-informed decisions, the market opportunity can scale quickly—especially if the platform becomes embedded into clinical workflow.

However, healthcare AI also comes with a high bar: trust, accuracy, and safety aren’t optional. The commercial upside is massive, but so is the reputational and regulatory risk if systems are over-promised. That tension is precisely why well-capitalized players can have an advantage; they can invest in validation, partnerships, and careful deployment, not just rapid growth.

Claroty, a cybersecurity provider, raised $150 million in Series F led by Golub Growth. The company was founded in Israel and is headquartered in New York, and it has raised close to $900 million in equity funding to date, according to Crunchbase data. The security category matters because cybersecurity is increasingly intertwined with operational technology—factories, critical infrastructure, and industrial systems—and AI can both strengthen defenses and expand attack surfaces.

This duality is becoming a board-level issue. Leaders are asking how to secure AI systems, how to protect data pipelines, and how to manage new threats that come with automation. Security companies that can address real operational environments, not just cloud endpoints, are positioned to be seen as essential infrastructure.

Preply, an online tutoring and language learning marketplace, also landed a $150 million Series D led by WestCap, with reported valuation around $1.2 billion. Education platforms are in an interesting position in the AI era: they can use AI to personalize learning and improve retention, but they also face questions about differentiation if content becomes commoditized. The winners will likely be those who combine pedagogy, community, and product experience—not simply those who bolt on AI features.

Inferact, founded by creators and maintainers of the open-source LLM inference engine vLLM, announced its launch along with $150 million in initial funding led by Andreessen Horowitz and Lightspeed Venture Partners, and the deal set an $800 million valuation. This is another sign that inference—getting models to run fast and efficiently—remains a high-value battleground. Open source, developer credibility, and performance optimization are becoming growth levers, not just engineering talking points.

Zanskar, a startup applying AI to geothermal exploration, raised $115 million in Series C led by Spring Lane Capital along with a long list of investors, and it is based in Salt Lake City. Energy exploration is a category where AI has a clean story: better targeting can reduce wasted spend, shorten timelines, and improve success rates. In a world that increasingly values sustainable energy production, applying AI to geothermal discovery also fits a broader narrative about climate-aligned innovation.

Finally, Noveon Magnetics raised $215 million in Series C, including $200 million from One Investment Management, with the company based in San Marcos, Texas. While this deal is not framed as “AI” directly, it connects to the AI era in an important way: manufacturing capacity for critical components and materials matters when demand rises for advanced hardware, electrification, and the broader infrastructure behind modern tech. Industrial capability often becomes the hidden constraint behind “digital” revolutions.

What these mega-rounds mean for the AI World community

If you zoom out, this week’s funding rounds don’t just show that investors still love AI—they show the market’s evolving definition of what AI leadership looks like. It’s no longer only about building a model. It’s about building the systems around that model: deployment infrastructure, networking layers, secure operational environments, and distribution mechanisms that put automation into everyday life.

For marketers, agencies, and growth teams, the lesson is equally concrete: the strongest stories in 2026 connect AI to outcomes that customers can feel and measure. Drone delivery becomes “minutes, not days.” Healthcare AI becomes “better clinical decisions with less cognitive overload.” AI infrastructure becomes “production reliability and cost control.” Cybersecurity becomes “resilience in industrial environments.” Energy AI becomes “smarter exploration and faster learning cycles.”

This is why the ai world organisation treats these funding signals as more than news—they are strategic clues. They hint at the next categories where partnerships, sponsorships, hiring, and product roadmaps will concentrate. They also shape the types of case studies and playbooks that business leaders will want at gatherings, because capital flows often predict where the ecosystem will build next.

The AI World Summit Singapore 2026 is positioned as a practical, tactical event organised by The AI World Organisation and planned to be held in Singapore in 2026. The event is described as application-only, with an emphasis on actionable workflows, real-world use cases, and strategies for creators, founders, agencies, and brand builders who want results—not just theory. For anyone watching rounds like Zipline, Humans&, Baseten, and the rest, the summit context matters because the gap between funding headlines and operational execution is where winners are made.

If your team is trying to understand what to build, how to position it, or how to scale it, the best move is to treat these mega-rounds as a map of momentum. The categories receiving capital are also the categories likely to shape enterprise budgets, media narratives, and customer expectations over the next 12–24 months. That’s why ai world organisation events and ai conferences by ai world focus on cross-functional execution—strategy, marketing, product, data, and operations—because AI is no longer a siloed initiative.

And as we head deeper into 2026, one more theme stands out: investors still reward conviction. Whether it’s a drone delivery platform betting on infrastructure-scale logistics, a brand-new AI lab betting on human-centered interaction, or infrastructure companies betting on inference and networking, the market is leaning toward teams that can articulate a big, defensible vision and back it with real execution.