From Chaos to Unicorn: The Rebel Playbook for AI Entrepreneurs
Author: Farhad Reyazat – PhD in Risk Management Author’s Profile as a Fintech Entrepreneur and Academic
Citation: Reyazat, F. (2025). From Chaos to Unicorn: The Rebel Playbook for AI Entrepreneurs. WWW.REYAZAT.COM. https://doi.org/May 2025
Modern entrepreneurship often demands thinking that is different from traditional business advice. For AI founders, embracing bold, even counterintuitive mindsets can unlock opportunities and growth. Here are six mindsets — each explained with stories and data — to inspire early-stage AI entrepreneurs to stretch beyond the conventional path.
“Yes, We Can” Mindset
This mindset means tackling challenges outside your current expertise with a fearless attitude. Rather than saying “we can’t” because a skill is missing, you say “Yes, we can” and figure it out. Researcher John Mullins recounts Arnold Corrêa’s story: Corrêa had never done video production or ad sales, but when a client’s video crew bailed, he told them, “Sure, we can,” and quickly learned on the job. By persistently saying “Yes, we can” to new tasks (producing content, selling in-store ads, licensing media, etc.), Corrêa completely reinvented his business. Today, Atmo Digital Media is “one of Brazil’s largest digital out-of-home media companies” with ~18,000 screens in stores and waiting rooms.
AI entrepreneurs can apply this spirit by stepping into new domains or tech stacks, even if it feels unfamiliar. For example, an NLP team might say, “Yes, we can,” to build an image-recognition tool if a customer needs it, then hire the right experts or rapidly upskill. The key is confidence and resourcefulness: believe you can solve a problem, then marshal resources (learning, partners, libraries) to get it done. Key actions for AI founders:
- Embrace new domains. Don’t limit yourself to your initial specialty. If a customer problem requires a new approach (e.g., applying your AI model in healthcare or robotics), say “Yes, we can” and hire or learn the missing expertise.
- Iterate and learn quickly. Take on unfamiliar tasks in small steps, using rapid prototyping and feedback. Each “can-do” attempt builds your capability and credibility.
“‘Yes, we can’ when you’ve never done something. It’sn’t just unconventional thinking,” Mullins notes — it’s how entrepreneurs grow. Corrêa’s story shows that saying “Yes, we can” to each new challenge can reinvent a company, and similar bold shifts have built many AI startups’ success.
“Problem First, Not Product First” Mindset
Too often, engineers build cool tech first and hope a market emerges. Counter-conventionally, solve a specific user problem before perfecting your AI product. In today’s more challenging funding climate, this is critical. Seed funding has dipped significantly (seed-stage rounds were down 33% year-over-year in late 2024 ), making investors “more drawn to startups that can offer comprehensive solutions to address clear market pain points” . In other words, entrepreneurs who start with a real pain and build enough tech to solve it tend to win.
Examples in AI:
• RunwayML (video editing): Runway co-founder Cristóbal Valenzuela started by tackling a narrow problem that creatives faced: time-consuming video edits. RunwayML offers “web-based video editing tools that utilize machine learning to automate what used to take video editors hours, if not days, to accomplish.” In other words, they didn’t start by building a general AI video engine; they pinpointed and solved the exact pain of manual editing. The approach worked: investors agreed their niche solution was valuable, and Runway raised a $35M Series B in 2021 to “leapfrog an entire industry”.
• Hugging Face (NLP models): The Hugging Face team began with a chatbot for teens, but quickly realized the real need was for developers: easy access to NLP models. When Google released the complex BERT model in 2018, Hugging Face immediately built a simpler PyTorch version and open-sourced it. This wasn’t a flashy new product launch — it was solving a clear developer pain (making state-of-the-art NLP accessible). Their library became wildly popular, and Hugging Face has grown into a $4.5 billion+ company.
• Anthropic (AI safety): Anthropic’s founders left OpenAI to address an urgent problem: AI alignment and safety. They didn’t just chase the biggest model; they built Claude to be safer and more controllable. Investors responded: Anthropic raised $3.5B in early 2025 and a total of $18.2B so far, with a pitch of building AI “to tackle complex projects” alongside humans and prioritize interpretability and alignment. Their fundraising highlights that focusing on a real societal problem (safe AI) can become an enormous opportunity.
Action Steps to Break the Mold:
• Talk to users first. Identify a customer pain (e.g., doctors spending too much time on notes, lawyers overwhelmed by documents, etc.) before writing a line of new AI code.
• Build a minimum solution: Start with a simple prototype that solves the core problem, even if it uses existing tools or a simpler model. Avoid adding bells and whistles until the core issue is fixed.
• Validate early and often: Show a prototype to real users or early clients to ensure you’re on target. The faster you confirm the problem/solution fit, the sooner you can grow.
By keeping users’ needs at the centre, AI startups avoid wasted effort and capital. It’s easier to raise money and gain traction when customers see you solving something important (vs. chasing technology for its own sake). As one analyst quipped about generative AI chat, solving “the right problem” can make growth explosive: e.g., ChatGPT hit 100 million users in just two months after launch because it addressed a broad audience’s thirst for easy AI chat.
“Think Narrow, Not Broad” Mindset
Contrary to the idea of building for everyone at once, start with a particular niche or user group and dominate it before expanding. Legendary entrepreneurs do this: Phil Knight’s Nike started selling running shoes only to track athletes and grew from there. Mullins’ research notes that Airbnb first hosted guests at the Democratic National Convention before growing into a global marketplace. The lesson is the same for AI startups: pinpoint one focused use case or customer type, win that space, then branch out.
For example, target an elite user who will value your tool above all else, even if they’re a small group. Are there professionals with a very specialized workflow that your AI can revolutionize? Become the tool for them first. You can broaden once you’ve earned credibility (and revenue) in that niche. Consider bold niches: maybe an AI scheduling assistant for busy surgeons, or a compliance checker for a specific financial regulation. By solving a narrow, urgent problem (or delighting a discerning user), you generate strong early advocates and data for growth.
Phil Knight said it best for Nike: “After starting with running shoes, he built his base. Only then did he expand when the time was right. Start niche, take small steps. Grow into your bigger vision.”
AI Founder Field Manual:
• Choose a tight niche: Define your initial market very narrowly (e.g., “CPAs dealing with fraud detection” or “UX designers prototyping interfaces with AI”). The narrower, the easier it will be to gain traction.
• Solve it thoroughly: Become indispensable to that group. You should expand to adjacent markets or broader features only after dominating the niche (and gathering feedback).
• Leverage niche success: Use your early revenue and case studies from the focused segment to prove to investors or partners that you can scale.
“Ask for the Cash and Ride the Float” Mindset
This mindset is about funding your growth with customer money, not just investors’ checks. It means securing pre-orders, deposits, or subscriptions before entirely building the product. Elon Musk famously did this with Tesla Model 3: even before production, Model 3 had almost 400,000 paid reservations by April 2016. Those $1,000 deposits (over $400 million) funded development, giving Tesla a huge cash buffer — effectively, customers paid Musk to finish the car. The lesson: let paying users help finance you.
In AI startups, “riding the float” can take forms like offering paid pilots, freemium trials with credit card holds, or early-access subscriptions. For instance, if you have a generative AI tool, launch a beta program where users pay a modest fee or commit to a long-term contract once the product matures. These commitments not only validate demand but also provide upfront capital. Even crowdsales or early “founder” memberships can create a float.
Moves that Matter for AI Mavericks:
• Pre-sell your product: Before full launch, offer early customers a discounted subscription or lifetime access for an upfront fee.
• Collect deposits: If building a SaaS or device (e.g., an AI-enabled gadget), ask interested buyers for a non-refundable deposit to gauge demand and fund manufacturing.
• Monetize pilots: Work with a few lead clients on paid proof-of-concepts. Using those fees to cover R&D and demonstrating revenue from early clients also makes fundraising much easier.
As Tesla asked customers to put down money before the Model 3 existed, your AI startup can ask for real commitments early. Small advances (like a locked-in contract or paid trial) can cover development costs. As a benchmark, when ChatGPT launched as a free public beta, it attracted millions of users in weeks; now OpenAI charges for API usage, essentially converting that initial user flood into paid revenue. Think about capturing cash early instead of financing entirely on investor goodwill.
“Beg, Borrow, but Don’t Steal” Mindset
When resources are tight, entrepreneurs resourcefully leverage what’s available. The guiding principle is to use external assets (other people’s equipment, networks, money) without unethical shortcuts. Mullins tells the story of Luxy Hair founders Mimi and Alex Ikonn: they needed inventory but had no capital. So Alex “logged onto Alibaba.com and found Chinese manufacturers” that could ship small lots; he “found a logistics provider” to fulfill orders; he even used rotating credit-card promotions (6-month interest-free offers) to fund the first $20,000 purchase of hair extensions. In six months, they launched with essentially borrowed resources, and today, Luxy Hair has millions of YouTube followers and multi-million-dollar sales with just a tiny team. They never stole anything; they borrowed packaging designs, open-source e-commerce templates, and free marketing (Mimi’s YouTube) to accelerate growth.
For AI startups, this means using open-source code, cloud credits, and partnerships instead of building or buying everything outright. For example:
• Use open-source models and libraries (Hugging Face’s Transformers, PyTorch, etc.) instead of re-inventing them.
• Leverage free or discounted cloud compute and storage credits (AWS Activate, Google Cloud credits for startups, academic grants).
• Partner or collaborate: compute on a university GPU cluster, or swap equity for services with a tech lab.
• Utilize community knowledge: adopt best practices and community data sets.
One inspiring case: Hugging Face itself exemplifies “borrowing” compute. After raising $235M in 2023, HF’s CEO Clem Delangue noted their good fortune in having funds to “invest in the community.” They announced a program to donate $10 million of GPU computing to small AI startups. In effect, they’re sharing borrowed resources (the corporate GPU pool) to advance the whole ecosystem.
Hugging Face’s CEO (pictured) emphasizes community support by donating GPU resources to help other AI startups.
Hugging Face’s community spirit shows that even well-funded AI companies use shared resources. An early AI founder can stretch limited funds by aligning with partners and open platforms. Always be on the lookout for “free” or cheap resources: public APIs, open datasets, academic papers, even crowd-labeled data.
Next-Level Plays for AI Trailblazers:
• Leverage open-source and pre-trained models. Start with platforms like Hugging Face or TensorFlow and fine-tune existing models rather than training from scratch.
• Use cloud and academic credits. Apply for startup or research credits on AWS/GCP/Azure, or borrow university GPUs if possible.
• Swap equity or future revenue for services. For example, work out a deal to defer costs with a data labeling firm or hosting provider.
• Focus on software and partners. Build with as little upfront hardware as possible and stay lean.
By “begging and borrowing” wisely, you keep overhead low and preserve runway. The Luxy Hair founders built a $ million business with just a $20k inventory order. Similarly, your AI venture can kickstart development through partnerships and the shared infrastructure of the AI community (often open and supportive) rather than cashing out everything.
“Act Before Asking for Permission” Mindset
Finally, many successful startups move fast and deal with regulations later. Uber’s co-founder, Travis Kalanick, famously told regulators his plan to expand aggressively anyway. Investopedia notes that “the idea for Uber was so disruptive it was illegal in many cities, but the company pushed ahead with a global expansion anyway.”. In SF in 2010, Uber was told to stop offering rides and faced fines — yet by the end of that year, it rebranded and continued launching everywhere. They essentially asked for forgiveness instead of permission.
In AI, regulations often lag behind technology. A similar bold approach can give a startup a head start. For example, generative AI companies launch models and then handle intellectual property or safety concerns later. (Recall how several startups released AI image generators and only later negotiated licenses or added content filters.) Another case: drone delivery or self-driving car projects sometimes begin test flights or pilots under minimal permits, trusting that they can work with regulators afterward.
Founder Fuel: Power Moves to Launch and Scale:
• Understand but don’t wait. Know the legal landscape (data privacy, AI ethics, crypto law, etc.), but if rules are unclear, proceed carefully rather than halting. Many regulations are reactive.
• Pilot discreetly. Before going fully public, you might launch a beta in a permissive region or within a research partnership.
• Be ready to adapt. If regulations catch up, adjust promptly (add opt-ins, audits, disclosures) but leverage your early mover advantage to build market traction or user base first.
The rationale is that many rules for AI are still evolving. Moving quickly lets you define your niche and show real-world benefits that make regulators more sympathetic. Uber’s aggressive expansion made it a household name by the time legal challenges arrived, and in AI, a similar act-first, clarify-later stance can allow you to “break the code” of the market before the rulebook is written.
Taken together, these six mindsets encourage AI entrepreneurs to push boundaries. Say “yes” to new challenges, solve real problems for a focused group, secure early customer cash, creatively use every available resource, and move swiftly even in uncertainty. Each has been proven by startups from Atmo Digital to Hugging Face to Uber. Adopting them allows an AI founder to turn constraints into fuel and build a thriving venture in unexpected ways.
Always remember: if conventional wisdom says “wait” or “stay small,” one of these counter-conventional mantras might be the push needed to achieve something great.
References
1. Mullins, J. (2022). Break the Rules! The Six Counter–Convention Mindsets of Entrepreneurs That Can Help Anyone Change the World.
2. PitchBook. (2024). Global Venture Capital Market Report Q4 2024.
3. TechCrunch. (2023, August 24). Runway raises $35M to make video editing with AI easier.
4. VentureBeat. (2023, March 7). Hugging Face valuation hits $4.5 billion.
5. Wired. (2019, November). The Rise of Hugging Face: From chatbot to NLP’s GitHub.
6. CNBC. (2025, March). Anthropic raises $3.5B from Amazon and others.
7. Bloomberg. (2016, April 1). Tesla Model 3 pre-orders near 400,000.
8. Forbes. (2018). How Mimi Ikonn Built a Million-Dollar Hair Business with YouTube and Credit Cards.
9. Hugging Face. (2023, July). $10M GPU Grant Program for Startups and Researchers.
10. Statista. (2023). ChatGPT user growth figures.
11. Investopedia. (2022). The History of Uber.
12. Knight, P., & Bowerman, B. (1980). Nike Origins: Oral history archive. Nike Inc.
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