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Every edition of this series has asked the same question: what do investors want founders to build next? This one asks a different question instead: of everything YC, a16z and others asked for this year, what actually got funded?
There’s a practical reason for that shift. As of mid-July, no new “Fall 2026” request-for-startups list has been published. YC’s site still shows Summer 2026 as its most recent set of asks, and a16z hasn’t updated its “Big Ideas” memo since December. But there is still a signal: a16z’s Speedrun SR007 cohort is now underway, built around themes like agent-native infrastructure, AI-native services, voice agents, and AI selling to AI, all extensions of ideas YC has been pushing since early 2026.
When multiple accelerators converge on the same themes without publishing new lists, it usually means the market has already moved.
So instead of summarizing a list that doesn’t exist yet, this edition does something different: it checks the last two sets of requests, Winter 2026 and Summer 2026, against real funding activity from the first half of the year. You can track previous editions published in 2024 and 2025 part 1 and part 2 as well as the most recent one from February 2026. All the requests below are new.
Why Israel is a useful stress test
To ground this analysis, I looked at Israeli venture funding in H1 2026. Israel is a small, dense and well-documented ecosystem, which makes it possible to map actual deals against specific theses rather than relying on anecdotes.
The headline numbers look strong. According to IVC–LeumiTech Q2 2026 and my weekly tracking of funding deals in Israel via the #FIRGUN Newsletter data, startups raised roughly $7.6 billion in H1 2026. Q2 alone reached over $4.2 billion, making 2026 one of the strongest years on record by quarterly average.
But the structure of that funding tells a more nuanced story:
- Capital is concentrated. The top nine rounds in Q2 captured over 60% of funding.
- Late-stage dominates. D+ rounds took nearly half of all capital, while Seed–A accounted for just ~17% and pre-seed barely registered.
- Fewer new bets. Only 35.5% of VC/CVC activity went into first-time investments — a record low.
- Deal count is down. Q2 saw just 87 deals, far below historical averages.
There is plenty of capital, but much less permission to be merely interesting.
The sector mix has also shifted dramatically. Cybersecurity now accounts for roughly a third of all funding, while defence, space, quantum and semiconductors have grown rapidly. Life sciences, automotive and other previously large sectors have shrunk as a share of capital.
That shift mirrors, almost line for line, what YC has been asking for: more defence, more hardware, more deep tech, more infrastructure, and less generic SaaS. Mapping H1 Israeli funding rounds against the Summer 2026 requests reveals a clear pattern: many of the most prominent categories were not just validated, they were flooded with capital.
Looking across written requests and actual funding activity, a few patterns emerge:
- Explicitly named categories get funded quickly, and become crowded. Agent security and enterprise AI are now highly competitive.
- Adjacent categories often offer better opportunities. Quantum and AI-native discovery systems sit next to major themes but face less competition.
- Gaps matter. Categories with strong theses but little funding, like agriculture, may represent open ground.
The key is to treat requests for startups as forecasts to be tested, not instructions to be followed.
Ten requests for startups for Fall 2026
1. The reliability layer for enterprise agents
Security is only part of the problem. Enterprises need to know whether agents completed tasks correctly, used the right data and behaved within policy.
There is room for evaluation, simulation and observability platforms built around real business outcomes. One of the challenges lies in the indeterministic nature of LLMs. Run the same prompt 100 times and you might get very different responses. It might be ok for creative writing, but it’s not ok for business decisions like approving a loan or refunding a product. Startups are trying to solve this with harnesses or extensive stress testing of agents, or by analysing the logs (after the fact) and being notified of anomalies. Agent reliability is another one of those categories that may already feel a bit crowded, but the problem is big and still remains.
2. Maintenance infrastructure for AI-generated software
AI makes it easy to create software, but not to maintain it.
The next bottleneck is managing complexity: dependencies, architecture drift and security. Founders can build systems that continuously map, test and explain codebases. I’ve written about ‘Cheap prototype, expensive maintenance’. All those vibe coded projects built with Lovable, Base44 and Claude Code artefacts were incredibly exciting to launch with prompts. But as they are used with real users and stress tested at scale, the bugs start to creep up, making maintenance precarious. Code review or automatic bug patching won’t do the trick here.
3. The economics control plane for AI
Companies still struggle to connect compute usage to business outcomes.
There is an opportunity to build financial infrastructure for inference: routing, cost measurement, budgeting and decision-making about when intelligence is worth buying. The rise of open-source LLMs, many of them Chinese, is currently being scrutinised by the US government, so there’s a need to better manage tokens, usage and the economic efficiency of AI. I wrote about this in my latest post on Tokenmaxxing being the wrong metric to track. I’ve seen ‘model-routing’ startups take a gatekeeping approach where every prompt will be analysed and routed to the best/cheapest model for the task. I don’t think it will work from a privacy perspective, so the door is open for better solutions.
4. Identity, payments and reputation for an agent economy
Agents are beginning to transact, but commerce infrastructure assumes humans.
We need identity, delegated authority, payments, escrow and reputation systems designed for machines. According to Cloudflare, between June 2025 and April 2026, human traffic to sites of businesses in many industries fell ~40%. Users are still buying products online, but they’re relying more and more on AI search and LLMs to do their research. The next step will be to let the agents transact, but there’s a whole tooling layer required to do that safely.
5. AI-native service companies in regulated markets
Instead of selling tools, become the service provider.
Tax, insurance, healthcare administration and compliance are all ripe for AI-native companies that deliver outcomes directly. I’ve written about AI native services in VC Cafe based on data from Sequoia’s Julian Bek, and others, and I foresee this trend will continue to grow.
6. Systems of action for the physical economy
Despite massive inefficiencies, sectors like construction and manufacturing remain underfunded.
The opportunity is to build systems that not only analyse data but take action, scheduling, sourcing, pricing and execution. Agentic AI for non-sophisticated ICPs is attractive because their margins are low (so they are desperately looking for more efficiency). In addition, the manufacturing, construction or even retail ICP would most likely choose to ‘buy’ (vs. build) due to lack of tech talent in their organisation. The caveat is that it’s not ‘one size fits all’ and go to market may feel more like a services company than a software company selling ‘saas’ licences/ seats.
7. Cheap defence against cheap autonomy
The economics of defence are shifting. Cheap drones require cheap countermeasures. Israel’s battle again Hezbo**a and the Ukraine-Russia war have demonstrated the scale of the drone threat.
Opportunities exist in sensing, electronic warfare and autonomous defence systems optimised for cost per threat neutralised. This is a category that is getting a lot of funding already and may very well become crowded, but it seems like the demand is there not just from homeland security, but also for protecting critical infrastructure.
8. Evidence infrastructure for personalised medicine
Personalised medicine requires trust, validation and longitudinal data.
Startups can build the infrastructure that makes recommendations explainable, auditable and clinically useful. We’ve seen a lot of funding going into drug discovery, but advancements in models and the availability of genomic data open interesting possibilities in the space of personalised medicine.
9. The task economy for training and supervising AI
As AI systems become more capable, they require better data and human oversight.
The next generation of data companies will organise expert knowledge into structured, high-quality tasks. This is a category that got popularised by the likes of Mercor and Micro1, but it continues to be relevant, especially as organisations aim to refine their models based on proprietary data.
10. Consumer AI that earns repeat behaviour
Consumer AI is not dead, but the bar is higher. Since the introduction of vibe coding, the number of apps on the app stores has exploded, but usage hasn’t increased massively.
The opportunity is to build products where AI enables something new, but long-term value comes from habit, identity and relationships. Founders that manage to find a niche, create a habit and get the engagement and retention needed to build a big business, may strike gold here. Gaming is part of this story.
What can you take away from this as a founder
The most important insight from H1 2026 is not which categories are hot. It is how quickly capital moves once a thesis is validated.
By the time a category appears on multiple investor wish lists, dozens of teams are already building in it. That also puts the VCs in a tricky situation, where even though the category seems ‘hot’, competition will make it tough to scale.
The better strategy is to identify the underlying shift, from copilots to agents, from tools to outcomes, from software to systems, and then find the implication that has not yet been fully funded.
The gap between the request and the deal flow is where the next opportunities will come from. No matter what you choose, keep in mind that the timeline for startups to exit didn’t shorten significantly, and while many startups eventually pivot, you should be willing to commit the next 7-10 years of your life working on the problem you’re focusing on, so choose wisely!
Shameless plug, if you’re an Israeli founder building in these areas, I’d love to hear from you. At Remagine Ventures, we’re often the first believers in the startup, providing pre-seed funding and helping the teams traverse the most risky part of their startup journey.
He is a former General Partner at Google Ventures (GV) in Europe, former head of Google for Entrepreneurs in Europe, and founding head of Campus London, Google’s first startup hub. Eze writes on Israeli tech, venture capital, artificial intelligence, and founder strategy.
He is also the founder of Techbikers, a nonprofit that brings together the startup ecosystem on cycling challenges in support of Room to Read.