STARTUP X
The F42 brief #023
Here’s your Monday dose of The F42 Brief.
What’s inside »
For Founders Who Want to Get **it Done, check it out below 👇
📈 Trending Now
The one thing that’s making all the noise …. with a bit of fact checking.
💸 New Funds looking to give you cash
Who has launched a new fund to invest in startups last week.
💡Innovator Spotlight
Founders that are doing stuff differently.
🛠️ Tool of the Week
Something useful to up your startups velocity.
📌 Note to Self
Stuff I constantly remind myself about, don’t want make the same mistakes again.
📈 Trending Now
AI Startups Are Playing in the Wrong Sandbox
Came across an interesting interview with Sarah Guo this week. She’s ex-Goldman, ex-Greylock, and now running her own AI-focused VC firm, Conviction. She’s all in on AI being a generational shift—not exactly a bold claim these days, but she’s got a fair point.
What stood out was her take on San Francisco still being the place to be. She argues that if you want to be at the forefront of AI, you need to be there in person.
I get the logic. Proximity to the right people, access to capital, and network effects all matter. But let’s be real: AI isn’t coming out of just one city anymore.
💡 The AI gold rush has already gone global. The best AI innovation isn’t just happening in SF—it’s happening in Beijing, London, Berlin, Dubai, Bangalore. And the biggest AI buyers? Not VCs. It’s corporates, governments, and institutions sitting on billions in inefficiencies.
Most AI founders are building products that improve workflows instead of AI that owns the workflow.
AI meeting notes? Nice, but incremental. AI that negotiates contracts and closes deals? That’s transformative.
AI-powered slide decks? Cool, but limited. AI that analyzes diligence, generates investment memos, and drafts term sheets? That’s where the gazillion-dollar market is.
A chatbot for customer service? Overdone. AI that runs end-to-end procurement, logistics, or corporate finance? That’s real leverage.
AI for startup fundraising? VC dollars are just one thin slice of the pie. The real game is in corporate venture, government grants, and alternative capital markets.
SaaS vs. SAS: AI’s Real Shift
Most AI startups today are just another SaaS tool—wrapping AI around an existing workflow and calling it innovation. But the real money is in SAS: Service-as-Software.
SaaS = Selling a tool. (More dashboards, more subscriptions.)
SAS = Selling an outcome. (AI that replaces expert workflows and moves money.)
The biggest winners in AI won’t be features you subscribe to—they’ll be decision engines that automate high-stakes work.
This isn’t a question of whether AI is a big deal—that’s obvious. The real question is: who’s actually using it to change the way capital, resources, and decisions move?
💸 New Funds Looking to Give You Cash
This section has moved to a new publication out every Wednesday, subscribe now so you don’t miss all the new early stage funds being launched.
💡 An all new Accelerator programme
For Founders Who Want to Get **it Done
Forget big numbers, bright lights, and TechCrunch headlines—they don’t determine startup success. Failure rates haven’t budged, and raising capital is brutal no matter what the hype says.
Getting in the door with investors is tough. But the real failure happens once you’re through that door—when they dig into your business and see the gaps. If you haven't nailed the basics, the red flags will show. Fusion42 fixes that.
This isn’t just another accelerator. It’s a 10-week execution co-pilot designed to drive relentless progress. Every session, tool, and system is built for immediate application—no fluff, just hard results.
Masterclasses That Matter:
✅ Problem Fit – Build something people actually need.
✅ Getting Users – Turn interest into traction.
✅ Building a Real Business – One you can grow and scale
Workshops That Drive Action:
✅ Practical, hands-on builds you can implement immediately.
Hard Accountability:
✅ No passengers. You execute, iterate, and sell.
24/7 Support:
✅ Global community always on. Never hit a roadblock alone.
What You’ll Achieve:
📘 Your Startup Playbook – A ready-to-scale blueprint covering every key business aspect.
📈 Go-To-Market Execution – A laser-focused plan that turns strategy into customer acquisition.
💼 Fundraising Readiness – A bulletproof, investor-ready strategy—no warm intros required.
🤖 AI-Powered Growth – Custom AI systems to automate, optimise, and scale.
🎤 Pitch Mastery – Intensive pitch practice to refine and perfect your delivery.
And it all leads to ⚡FUSED⚡—our final investor pitch event where you’ll showcase your refined pitch live, impress investors, and secure the capital you deserve.
Early Registration Now Open
Early registration is recommended to lock in your spot—for you, your co-founders, team members, startup colleagues, friends, and even your mother (it’s never too late). Once registered, you'll receive your Startup Checklist to kick things off.
Over the coming weeks, we’ll share more details on Panels, Masterclasses, Workshops, and the full agenda. 🚀
💡Innovator Spotlight
The Founder Who Used AI to Bootstrap to Millions
Most founders still think scaling fast = raising VC money. But Grant Lee, co-founder of Gamma, proves that’s total rubbish.
Instead of chasing investors and burning through cash, he built a profitable, AI-powered startup with a tiny team—and it’s now pulling in tens of millions in revenue.
How AI Let Gamma Grow Without the Usual Startup Chaos
Gamma builds software for creating presentations and websites, and it’s growing like crazy. In just a few years, they’ve:
✅ Hit tens of millions in annual revenue
✅ Grown to 50 million users
✅ Stayed profitable from day one
✅ Done it all with just 28 employees
Most startups would need a team of 100+ to pull that off, but Lee automated the hell out of everything using AI:
🛠️ Customer Service: AI-powered chat with Intercom
🎨 Marketing: AI-generated visuals with Midjourney
📊 Data Analysis: AI insights from Claude
📝 Customer Research: AI-powered notes via Google’s NotebookLM
💻 Coding: AI-assisted development using Anysphere’s Cursor
This isn’t just about saving time—it’s about speed, efficiency, and outpacing competitors without throwing people (and money) at the problem.
Flipping the Startup Playbook on Its Head
The usual Silicon Valley playbook says you need to:
❌ Raise millions.
❌ Hire fast.
❌ Burn through cash, then raise again.
Lee binned that idea and did the opposite:
✅ AI replaced what used to take entire teams.
✅ Profitability first—so they weren’t begging for funding every 18 months.
✅ Let the product do the selling—no massive sales team needed.
The New Startup Model: AI-First, Not Headcount-First
Lee’s playbook is how smart founders will build in the next decade:
🚀 Small, fast, AI-powered teams will beat bloated startups.
🤖 If AI isn’t running half your ops, you’re doing it wrong.
💰 If you can be profitable before raising, you’re in control—not investors.
This is the new way to build—lean, AI-driven, and built for growth, not fundraising cycles.
If you have not joined the Fusion42 Community on Telegram — A space for Founders, Framers, and Funders who show up to get **it done,
it is probably time to do so.
For the ❤️ of Startups
🛠️ Tools of the Week.
AI That Thinks for Itself: How Multi-Agent AI Can Supercharge Startups
Running a startup means wearing too many hats—fundraising, strategy, sales, product, customer support—all at once. It’s overwhelming, and there’s never enough time.
I’ve seen too many founders using AI the wrong way—firing off questions to ChatGPT and hoping for magic. That’s not how you build an advantage.
The real game-changer? Multi-agent AI. Instead of a single chatbot giving you generic answers, imagine a team of AI agents working together—one doing research, another fact-checking, another summarising insights—all without you lifting a finger.
That’s the future of AI. And smart founders are already using it to automate high-value decision-making.
Why Founders Waste So Much Time (And How AI Fixes It)
I get it—most startups don’t have the luxury of dedicated analysts or data teams. But multi-agent AI can take over tasks that usually eat up hours.
💡 Investor Research:
Instead of manually digging through spreadsheets, reports, and LinkedIn profiles, AI can score, filter, and match investors based on actual funding patterns.
💡 Sales & Customer Outreach:
Why send generic cold emails when AI can analyse a prospect’s behaviour and tailor the outreach dynamically?
💡 Market Analysis & Pivot Decisions:
Rather than guessing, AI can identify emerging trends, test hypotheses, and forecast outcomes so you don’t waste time chasing dead ends.
This isn’t about replacing people—it’s about freeing you up to focus on high-impact decisions instead of drowning in busywork.
How Multi-Agent AI Works
Most founders are stuck in the single-agent AI mindset—asking one model a question and taking whatever response it spits out. That’s not how human teams work, and AI shouldn’t either.
Multi-agent AI is built on a few key techniques:
1. AI That Critiques Its Own Responses (Self-Reflective Prompting)
Instead of assuming its first answer is correct, AI can review and refine its own work before delivering a final response.
✅ Used in finance, law, and research, where accuracy matters.
✅ Stops you from acting on AI-generated nonsense.
2. AI Brainstorming Multiple Solutions at Once (Tree of Thoughts – ToT)
Rather than going for the first idea, ToT lets AI explore multiple possible answers, compare them, and pick the best one.
✅ Perfect for strategic decisions, problem-solving, and product pivots.
✅ Works like a real brainstorming session, except with AI agents instead of people.
3. AI That Challenges Its Own Assumptions (Adversarial Prompting)
One AI agent plays devil’s advocate, forcing another AI to justify its answers—like a built-in fact-checker.
✅ Helps in investment analysis, risk assessments, and due diligence.
✅ Makes sure you’re not making decisions based on AI hallucinations.
4. AI Teams That Work Together (Multi-Agent Collaboration)
Instead of relying on one AI to do everything, you assign tasks to different AI agents—just like in a real team.
💡 Example Workflow:
✅ One AI gathers competitor data
✅ Another verifies accuracy
✅ A third structures the insights into a clear report
✅ The final AI refines and tailors the output for your business
💡 Why this matters: This approach is already reshaping finance, investment due diligence, and decision-making.
The Missing Piece: Taking AI from Good to Unstoppable
Most AI tools today still produce generic answers—because they don’t know your business, your customers, or your market.
1. A Database for Personalisation
Right now, AI forgets everything as soon as the chat ends. That’s useless for business.
✅ Solution: Store user data in a database (e.g. RavenDB, Pinecone, or Weaviate) to personalise every AI response.
✅ What this enables: AI can adapt based on user history, preferences, and market insights.
✅ Example: An AI-powered investor briefing tool that remembers funding history and adapts reports accordingly.
2. Fine-Tuning AI So It Actually Understands Your Business
Most AI models have never seen your startup’s data, so why would they give useful responses?
✅ Solution: Fine-tune AI on your industry’s language, customer behaviour, and business rules.
✅ What this enables: AI that thinks like your business, not just a generic chatbot.
✅ Example: A multi-agent AI investment tool that learns from past market trends and adapts over time.
Without personalisation and fine-tuning, you’re just hoping AI understands your business—and hope isn’t a strategy.
The Best Tools to Start Using Multi-Agent AI Today
You don’t need a PhD in AI to start using this—here are tools you can plug into your business right now:
1. CrewAI – Build AI Teams That Work Together
🔹 What it does: Lets you create AI agents that collaborate and specialise in different tasks.
🔹 Use case: Research, due diligence, competitor analysis.
🔹 Try it: CrewAI on GitHub
2. LangChain – Connect AI Models & Automate Workflows
🔹 What it does: Chains multiple AI models together and integrates external data sources.
🔹 Use case: AI-powered research assistants, workflow automation.
🔹 Try it: LangChain Documentation
3. SuperAGI – Automate AI-Driven Decision-Making
🔹 What it does: Creates autonomous AI agents that run long-term tasks.
🔹 Use case: AI-driven competitor tracking, lead scoring, and business intelligence.
🔹 Try it: SuperAGI on GitHub
4. n8n – No-Code AI Workflow Automation
🔹 What it does: Helps non-technical founders build AI-powered workflows without needing to code.
🔹 Use case: Automate lead generation, content creation, and investor tracking.
🔹 Try it: n8n.io
5. Pinecone / Weaviate – AI Memory for Personalisation
🔹 What they do: Store past interactions and enable AI to remember and customise responses over time.
🔹 Use case: Personalised AI assistants that improve with every interaction.
🔹 Try them: Pinecone | Weaviate
Final Thought: Stop Using AI Like a Toy
Most startups dabble in AI but don’t use it as a competitive advantage. If you’re still asking ChatGPT random questions, you’re wasting time.
🔹 Are you treating AI like a chatbot?
🔹 Or are you building a system that actually moves your startup forward?
The winners in AI won’t just be the ones who use it—they’ll be the ones who integrate, refine, and scale it into their operations.
🚀 Start thinking of AI as part of your team—not just a tool.
📌 Note to Self
Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.
If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.
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For the ❤️ of startups
✌🏼 & 💙
Derek