Most startups equate the process of fundraising to dating – founders have to typically kiss a lot of frogs until the find the right fit. Investors are typically focused on stage, geo and in some cases sector, but there are always exceptions so it’s hard for startup founders to assess who might be interested to embark on their journey. In addition, founders thinking about starting a company can be overwhelmed by choice, as there are so many problems to tackle with technology, but it could be comforting to know that investors are interested in those areas in the first place.
That’s why lists like Ycombinator’s Requests for Startups (RFS) help entrepreneurs thinking about starting a company (or picking a problem) and can be a good barometer for founders on what’s interesting to investors right now. It’s been a while since they’ve updated their RFS list and it captures all the buzzword’s of today’s tech world: AI, defence tech, climate, spatial computing, etc. I’ve recently come across several of such lists and I thought it could be useful to aggregate them and share them here.
Ycombinator’s RFS 2024
- Applying machine learning to robotics – While consumer use-cases feature heavily in science fiction, some of the overlooked and most immediately addressable applications for robots are B2B
- Using machine learning to simulate the physical world – companies replacing existing simulations with ML-based ones, along with companies using ML-based simulations to open new markets currently unaddressable
- New defence technology – New companies that sell to the DoD like Palantir and Anduril
- Bring manufacturing back to America – New ML-based robotics systems will make it possible to automate far more, which will reduce the cost-of-labor arbitrage that pushed manufacturing to other countries in the first place.
- New space companies – If we are entering a future with access to space being as routine and inexpensive as commercial air travel, shipping or trucking… what new businesses does that unlock?
- Climate tech – We have a fair chance of avoiding catastrophic climate change if startups offer commercial solutions to decarbonize society or remove carbon from the atmosphere.
- Commercial open source companies – being open source is a powerful way to gain developer adoption and sell to enterprises a lot sooner.
- Spatial computing – with the launches of the Apple Vision Pro and the Meta Quest 3 a new personal computing platform may evolve over the next decade. Looking for startups solving practical use cases that go beyond gaming.
- New enterprise resource planning software (ERPs) – new startups that build software that helps businesses run.
- Developer tools inspired by existing internal tools – tools or frameworks that were built by programmers at their previous company to help solve their own particularly painful or repetitive problems.
- Explainable AI – For society to reap the full benefits of AI, more work needs to be done on explainable AI. Looking for startups building new interpretable models or tools to explain the output of existing models
- LLMs for manual back office processes in legacy enterprises – LLMs allow whole categories of manual processes to be automated in ways that weren’t possible until recently. Where there’s linguistic ambiguity or some amount of subjective evaluation needed, LLMs come into their own.
- AI to build enterprise software – In the future, every enterprise could have their own custom ERP, CRM or HRIS that is continually updating itself as the company itself is changing.
- Stablecoin finance – looking for great teams building B2B and consumer products on top of stablecoins, tools and platforms that enable stablecoin finance and more stablecoin protocols themselves
- A way to end cancer – innovations in the MRI hardware, the AI algorithms to interpret scans and reduce false positives, and the business models and consumer marketing to make it a viable business
- Foundation models for biological systems – highly technical founders building foundational models from scratch in any part of biology or medicine
- The managed service organization model for healthcare – Looking for startups building on the MSO model that enables doctors to run their own clinics by (1) providing them software that can handle back office tasks such as billing and scheduling and (2) channeling patients to them.
- Eliminating middlemen in healthcare – from using AI to automate repetitive human jobs to exploring new and better business models for providing care.
- Better enterprise glue – By generating custom code for uncommon, company-specific use cases, large language models have the potential to eliminate the need for glue code altogether including ETL pipelines, integrations, and custom workflows.
- Small fine-tuned models as an alternative to giant generic ones – when finely tuned with appropriate data, smaller, specialised LLMs can yield comparable results at a fraction of the cost. Looking for companies creating tools to facilitate construction of or fine tuning models.
Andreessen Horowitz Consumer Team Opportunities for startups
I’m a big fan of the content A16Z has been publishing on generative AI over time. One of my favourite pieces has been The Abundance Agenda, a deck on how AI will transform consumer technology, packing inside areas of opportunity, or in other words, requests for startups.
Content creation:
- Generation tools that kill the “blank page” problem – ideating,
- Making open source models accessible – create the interface to utilise open source tech in the browser (or an app).
- Creating remixable outputs – allowing creators to expose their prompts and make their work instantly remixable
- Enabling consumers to build content creation apps – products that help users “chain” together models and prompts behind the scenes – and then save these workflows and / or publish them for others to use.
Content editing:
- Owning multi-media workflows – combine an image with text, music with video, or animation with a voiceover. Alow users to generate, refine, and stitch different content types in one workspace
- Enabling in-platform refinement – AI products can help users identify what can be improved, and then automatically make these changes.
- Iterating with intelligent editors – products that enable users to take an existing output and refine it (ex. regenerate one frame or feature) without completely starting from scratch.
- Automatically repurposing content – leveraging AI to automate manual editing to repurpose a piece of content for different platforms.
Productivity gains
- Agents that act as systems of action – agents that can complete common consumer tasks like booking a restaurant or finding and sending a gift to a friend.
- Voice-first apps – voice dictation and summarisation AI apps allowing consumers to share more complex and even unfinished thoughts.
- Apps that provide in-flow assistance – an AI assistant like Microsoft’s ‘Clippy’ that magically appears where and when needed to avoid context switching.
- “Build your own” workflows – no-code automations and workflows – LLMs acting as intermediaries to allow users to stitch together much more complex flows than pre-AI.
Companionship
- Differentiated value prop vs. generalist chat products – AI companion products hat specialise in content that mainstream models aren’t good at (or don’t allow), like fictional role plays or erotica.
- New methods of interaction – beyond text box interaction, a companion you can summon anywhere, with a voice, avatar, and animation that feels like a real friend hanging out with you
- Apps that enable memory and progression – A companion should get to know you better over time, remember your previous conversations, and change the nature of the relationship.
Social
- Hybrid AI x human communities – messaging & social apps where bots are treated as equal citizens. Bots should be able to join chats with you and friends, and weigh in or spark discussions.
- Live, interactive entertainment at massive scale – AI allows anyone to be an entertainer with the help of generative avatars. AI characters are already hosting interactive streams and shows in which they respond to audience questions, comments, or votes in real-time.
- Next-gen avatars for next-level communication – AI gives consumers a hyperrealistic digital likeness they can use to instantly generate and share these assets.
- AI to make IRL matches – What if you could chat with a bot that learns about you on a deeper level and uses this information to make a curated set of matches?
Education
- Multimodal ability – apps that allow users to “input” questions, topics, or ideas in all forms (audio, image, text, and even video) and get a response in the media type that helps them learn best.
- New interfaces that break the “edtech” mold – the rise of more casual, experiential learning at scale.
Personal Finance
- Cross-account visibility and management – Today’s AI products can analyze and move money between accounts – as agents improve, they will make trades across accounts.
- Auto-optimization across products – Many consumers are “overpaying” for their debt, insurance, and bills, AI agents can take over this process.
- Programmatic investing using natural language – Using natural language or decision trees, consumers with no knowledge of code can build algorithms that execute trades for them.
- Complex transactions move from services ? AI – tax planning or wealth management augmented and in some cases replaced with AI.
Ark Invest’s Big Ideas 2024
Cathy Wood, the founder of Ark Invest published the ARK Big Ideas 2024, an annual report titled ‘Disrupting the Norm, Defining the Future’. It identifies five innovation platforms converging and defining the technological era: AI, public blockchains, energy storage, robotics, and multiomic sequencing.
- Technological Convergence – The global equity market value associated with disruptive innovation could increase to 60% by 2030.
- Artificial Intelligence – Scaling global intelligence and redefining work: AI training costs should continue to fall 75% per year.
- Smart Contracts – Powering the internet-native financial system, smart contract networks could generate fees of $450bn in 2030.
- Digital Wallets – Digital wallets could grow select vertical software platforms’ revenues to $27-$50bn in 2030.
- Robotics – Generalizing automation, thanks to the convergence of AI software and hardware. Generalizable robotics represent a $24 trillion-plus global revenue opportunity.
- Digital Consumers – Transitioning toward digital leisure, where spending could teach $23 trillion in 2030.
- Electric Vehicles – Lower battery costs powering adoption mean EV sales could reach 74 million in 2030.
- Robotaxis – Robotaxi platforms could create $28 trillion in enterprise value in 2030.
- Multiomic Tools & Technology – Translating biological insights into economic value: R&D spending could decline by more than 25%, thanks to multiomic tools and technology.
- Reusable Rockets – Satellite connectivity revenues could exceed $130bn per year in 2030.
- Autonomous Logistics – Global autonomous delivery revenue could reach $900bn by 2030.
- Bitcoin Allocation – Growing the role of bitcoin in investment portfolios. During the last seven years, bitcoin’s annualized return has averaged around 44%.
- Bitcoin in 2023 – After challenges in 2022, bitcoin’s price surged 155% last year, reaching $827 billion in market cap.
- Precision Therapies – Curing disease more efficiently and less expensively. The enterprise value of companies focused on precision therapies could reach $4.5 trillion by 2030.
- 3D Printing – Revenues could grow 40% at an annual rate to $180bn by 2030.
Sarah Guo – Conviction Capital and Embed Accelerator
Sarah Guo believes that with current advancements in generative AI, and LLMs in particular, smart founders can be up and running with a product that users want in a matter of weeks. For that purpose, she launched Embed, an accelerator program.
- Verticalized Video Understanding – machine interpreted video. there is a wealth of opportunity in home security, public safety, workplace safety, health/fitness, contextual advertising, automated video editing, customer education/support, retail (checkout and loss) and parking/traffic management.
- Deepfake Detection – demand for a mobile application that provides deepfake detection, Caller ID, spam blocking and perhaps even live translation or agentic experiences for call handling.
- Your Workshop Assistant – build assistive tooling that either helps engineers narrow down their design space more efficiently or performs some set of menial tasks for them as a starting point; second, combining generative models with post-processing work to “clean up”, like simulation for validation and other post-processing, to reduce the burden on zero-shot model output.
- KYC/KYB Automation – emulate some of what a human compliance agent might do in their investigation, and, at minimum, can parallelize workstreams and surface potential red flags.
- One Giant Leap for Healthcare Admin -Breakthroughs in transcription, speaker diarization, translation, summarization, LLM web navigation/form filling, and general retrieval and reasoning will revolutionize the patient encounter and claims / prior auth workflows
- Your Personal Seller – Foundation models enable the at-scale generation of alternative product listings and also therefore A/B testing of variants to improve conversion and potentially optimize for different audiences
- AI Therapist & Coach – One of the emergent use cases with the initial wave of LLMs has been a mix of therapy and personal coaching, but the current models are poor on memory, safety and security.
- Automated Root Cause Analysis – there’s an opportunity for automated root cause analysis to substantially improve incident resolution time, and the experience for engineers. A lightweight “agent” with access to logs and metrics can, to start, retrieve relevant information (e.g. service statuses, past error logs, similar prior incidents) and suggest fixes based on what previous resolutions were. After an incident, the same agent could be used to provide a “best practice” resolution for future incidents. and generate a post mortem. Long term, agents might even be able to automatically fix common reoccurring issues
- End-to-End Legal Outcomes – automate certain end-to-end, transactional services provided by law firms such as immigration firm in a box, trademark filing or contract reviews.
- High-Consideration Research – LLMs are really good at quickly reading and synthesizing hundreds of pages of content on any topic. Is there a good consumer product experience to be built around building a dynamic, interactive, comprehensive query experience for high-intent, high-consideration purchases?
- Workforce Tetris – Recruiting, employment compliance/admin and logistics requires a backend database and scalable workflows, but the next generation of workforce software shouldn’t put that burden on managers or workers.
- Next-Generation Autocomplete – a system that learns your style and completes everything you need to write based on your history, with privacy and speed.
- The All-Seeing Eye – Hardware and storage should be rethought from the ground up in the age of semantic video understanding, and powerful on-device models. A full-stack security services firm could see more, cost less, and offer a step-function better experience.
- Always Pick Up the Phone – Voice generation quality and LLM capability are approaching the ability to handle many transactional calls. What’s missing is the last mile — distribution, customer journey design, guardrails and workflow automation.
- Developer in a Box – the ability to go from a human description of an issue to a draft solution, in code, to the problem.
- AI Static Analysis Tools – internal tooling that automatically triages and prioritises issues identified by other systems.
- AI-Native MMPGs and Social – What would a game world populated by AI’s be like? What if the next generation of entertainment is personalized generations?
- AI Video Generation, Editing and Understanding – Companies that democratise video production, editing, personalisation and understanding
- Web Content APIs – current web content APIs lack the flexibility & feature set required to power large scale web applications. There doesn’t exist a web search API that has access to page content, parsed outlinks from the page, or even edit history.
- The Tireless (Junior) Financial Analyst – global accounting, tax, financial reporting and compliance standards are all codified in natural language. Transform financial and accounting software from databases to context-aware, proactive processors.
- Autonomous HR (and IT) Helpdesk – A domain populated with process documentation, ever-changing compliance needs, complex policy application, forms, and natural language communication is ripe for attack by LLMs.
- Technical Support – a “debugging copilot” for the engineers currently working in technical support.
These are just a small selection of ‘requests for startups’ or rather, opportunities for startups leveraging technology. The power of LLMs is advancing rapidly, meaning that we’re just at the beginning of this technology wave. Founders choosing to work on non-obvious, large niche problems, can still get an early mover advantage. It’s time to build! I also recommend Erik Torenberg’s podcast ‘Requests for Startups‘ for in-depth deep dives on various verticals.
Shameless plug: At Remagine Ventures, we back pre-seed startups at the intersection of tech, entertainment, gaming and commerce. If you’re an Israeli or UK startup building in these spaces and you’d like to get quick feedback and VC-friendly advice, don’t hesitate to get in touch.
Co Founder and Managing Partner at Remagine Ventures
Eze is managing partner of Remagine Ventures, a seed fund investing in ambitious founders at the intersection of tech, entertainment, gaming and commerce with a spotlight on Israel.
I'm a former general partner at google ventures, head of Google for Entrepreneurs in Europe and founding head of Campus London, Google's first physical hub for startups.
I'm also the founder of Techbikers, a non-profit bringing together the startup ecosystem on cycling challenges in support of Room to Read. Since inception in 2012 we've built 11 schools and 50 libraries in the developing world.
I'm a former general partner at google ventures, head of Google for Entrepreneurs in Europe and founding head of Campus London, Google's first physical hub for startups.
I'm also the founder of Techbikers, a non-profit bringing together the startup ecosystem on cycling challenges in support of Room to Read. Since inception in 2012 we've built 11 schools and 50 libraries in the developing world.
Latest posts by Eze Vidra (see all)
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