Embracing the Unknown 2/2: Takeaways from AI startup school — seminars
I was granted exclusive online access to the AI startup school seminars. Here are some awesome and very transforming things I learned from the top startup and tech speakers.
In order to read about application to AI startup school and my thoughts on entrepreneurship and risk, read part I:
Embracing the Unknown 1/2: Applying to AI startup school — reflections
As I navigated my own journey, my relationship with risk and entrepreneurship, I found myself in following AI startup school seminar series every week. This transition, from introspective exploration to external insights, was a shift in setting and in perspective. I discovered that my personal relationship with risk and uncertainty echoed in the stories and lessons of speaking entrepreneurs and innovators. The difference being they made the step and engaged a venture into the unknown, armed with resilience and a thirst for knowledge.
I am not allowed to share exact content of the seminar series, therefore I would like to convey the messages and thoughts that impacted me the most over the 9 lectures of this special EF talks.
All lectures took a form of an interview with the speaker(s). The speakers were a unique blend of famous figures of AI startup scene, young entrepreneurs and VC investors.
You will find within my notes, the golden phrases of the speakers, some words about open source, future of AI, future of startups and entrepreneurial journey.
Takeaways
Being in too early is the same as being wrong
Eiso Kant — Poolside
If the product you develop isn’t tailored to the public, or if the technology you invest in is too immature to add value, your venture is unlikely to succeed. Eiso Kant, CTO and co-founder of Poolside, shared insights from his experience in founding a startup focused on developing cognitive abilities through neural networks in 2016. Similarly, Arthur Mensch from Mistral AI highlighted the importance of capitalizing on opportunities at the opportune moment— not too early, and not too late. If the technology isn’t sufficiently mature, the business might struggle. It’s possible to choose the right technology but at an inopportune moment
Faster you acknowledge the lack of correlation between effort and impact faster it stops you complexes.
Life is unfair, get over it.
Matt Clifford, co-founder of Entrepreneur First
Matt Clifford, co-founder of Entrepreneur First shared a wealth of knowledge about AI business models and entrepreneurial spirit, as well as life in general. Having witnessed the rise and fall of many startups and investing in numerous ventures, he has gathered a trove of stories and insights. One of the most impactful statements was about acceptance. The acceptance of the fact that we don’t always get back what we give. That it is not because we work without rest on a project, it will be successful. Sometimes, factors like luck or intuition play a decisive role in a company’s fate.
Open source
The figures in the AI field, such as Emad Mostaque of Stability AI, Arthur Mensch of Mistral AI, and Karim Beguir of Instadeep, are strong supporters of open source. They see significant business opportunities in building around open-source models, with community engagement and customer acquisition being strong arguments in favor of this approach. Arthur Mensch believes in enhancing open-source business with exceptional customer service and thriving through partnerships. Karim Beguir finds great reward in community contributions to their work. The contribution, which strengthens the product and unlocks business potential in scaling open source to meet client needs.
Stability AI’s business model involves providing access to advanced generative AI models through a subscription membership. This membership includes all available models, similar to Amazon Prime. This approach aims to make generative AI models predictable and easy to use. Additionally, the company offers consulting services for top organizations in need of expertise in generative AI. The goal is to revolutionize the industry by providing model base and support while adapting to the market’s growth
Leading players in the AI space have varying perspectives on open vs. closed models. OpenAI initially started with open models but has become cautious due to concerns about misuse. Google is open in some areas but avoids open sourcing models. Meta embraces openness, especially in language models. Microsoft supports open AI but has a mixed approach. Amazon focuses more on infrastructure. Apple is very secret about anything they make, with some rare exceptions. Smaller labs and Japanese companies are aggressively pursuing open models.
The balance between open and closed AI models is shifting, with more players recognizing the benefits of openness.
The future of AI
Many speakers emphasized the shift towards ‘quality over quantity’ as an emerging trend. This could take a form of providing the best data to achieve better model performance. Or, the future of AI will likely see a shift towards specialized models and swarms of models.
The importance of developing robust evaluation frameworks and corrective mechanisms was also repeated in many talks. The significance of exploring multimodality and enhancing model inference capabilities beyond current paradigms should also not be neglected.
Entrepreneur journey
The speakers discussed extensively their journeys with business ideas and the people they collaborated with. A speaker highlighted that a ‘moment of audacity’ is essential for taking the leap into entrepreneurship.
Another important step is finding a co-founder. This person should be both your best friend and your challenger, offering support and constructive disagreement. It’s commonly recommended to seek a business partner with complementary skills. However, a few speakers mentioned they did not follow this advice, and it still worked out for them as they are complementary on a different level, even though they have similar hard skills.
To build a successful business, the right idea involves identifying a niche, such as industry-specific issues, novel technologies, or regulatory gaps. For instance, Mistral AI identified a gap in the AI startup landscape and aimed to expedite progress in foundational models.
They also advised against becoming too lazy in a stable job. The quicker you immerse yourself in entrepreneurship, the better.
Another vital skill mentioned was the need to rapidly obtain feedback. The speaker encouraged aspiring entrepreneurs to begin creating, even if the idea isn’t perfect, and to engage with potential customers for feedback. Prioritizing swift iteration over waiting for the perfect final product is crucial. An environment that facilitates rapid learning and exposure is key.
Additionally, the speakers underscored the importance of embracing failure and perseverance throughout the entrepreneurial journey. Cultivate and pursue your ambition, and think beyond traditional boundaries.
To elevate the culture of entrepreneurship, Matt Clifford recommended carefully studying successful individuals. Understand their decisions and learn from their mistakes. He argued that reading books is more beneficial than spending time on platforms like Twitter and watching short videos.
Lastly, investors can act as true partners and guides on the entrepreneurial path, with strategic partnerships and foundational funding serving as catalysts for change.
What kind of startups will come next ?
Currently, the media sector holds great potential for impactful innovation. Entrepreneurs are encouraged to tackle problems that deeply concern them and focus on regulated industries where they can make significant contributions. Within the AI ecosystem, opportunities for value creation span from specialized chips at the computational layer to domain-specific applications at the user interface layer. It’s crucial for startups to avoid being confined to a single AI model and to maintain flexibility in selecting foundational models. One should keep in mind that
AI is tool not the solution
Mistral AI pinpointed real-world applications such as knowledge management, customer service enhancement, and developer productivity as promising avenues for AI deployment.
Is Paris the place to be ?
The series highlighted the growing prominence of European startup headquarters, with Paris emerging as a central hub. One speaker shared their decision to relocate to Europe after noticing that the most impressive talent profiles they encountered in Silicon Valley were predominantly from Europe. Despite London’s continued dominance in the AI talent pool, the rest of Europe is catching up. The idea to move to Europe faced skepticism from many in the U.S., citing concerns over regulatory frameworks, tax implications. Also, perceived European work culture does not have a good echo in U.S., including shorter work hours and challenges in dismissing employees. However, despite these concerns, the relocation proved successful for many startups, with the companies now thriving in their sector and benefiting from European talent and sometimes E.U. subventions.
As we’ve journeyed through the lessons of AI startup seminars, one thing becomes clear: the path of entrepreneurship is as diverse as the individuals who embark on it. Paris, with its emerging status as a startup anchor, symbolizes this beautifully. It is a testament to the evolving landscape of innovation. The insights shared by these pioneers aren’t just lessons; they’re a compass, guiding us towards a future. In that future where AI isn’t just a tool. Where AI can be a canvas for the boldest visions.
In this dynamic world of startups and AI, our greatest asset is perhaps our ability to adapt, learn, and above all, dream audaciously.
