To have a successful startup, you need: a great idea, a great market, a great team , a great product, and great execution. We thought it might help us scale Y Combinator if we could distill the most generalizable parts of this advice into a sort of playbook we could. 2 Startup Playbook. Table of Contents. 8 Great Ideas to Jumpstart Your. New Business's Marketing Efforts. 3. Owner's Checklist for Starting a New Business. 6.
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Your goal as a startup is to make something users love. If you do that, then you have to figure out how to get a lot more users. But this first part is. According to the Kauffman Index of Entrepreneurial Activity, more than new businesses were created in in the United States alone—each one of. Access a free summary of Startup Playbook, by Sam Altman and other business, leadership and nonfiction books on getAbstract.
He lives in Westchester, New York, with his wife and three sons. We want your feedback! Click here. Subjects Business Nonfiction. Business Nonfiction. Publication Details Publisher: David S. More about David S. The Startup Playbook Embed.
Media The Startup Playbook. Case studies on AI applications to adjacent industries or to your specific industry. Basic technical understanding of AI, including major classes of algorithms and their requirements. Basic understanding of the workflow and processes of AI projects, roles and responsibilities in AI teams, and management of AI team.
Curriculum: Deep technical understanding of machine learning and deep learning; basic understanding of other AI tools. Understanding of available open-source and other 3rd party tools for building AI and data systems. Additionally: Ongoing education to keep up-to-date with evolving AI technology Develop an AI strategy An AI strategy will guide your company toward creating value while also building defensible moats.
Once teams start to see the success of the initial AI projects and form a deeper understanding of AI, you will be able to identify the places where AI can create the most value and focus resources on those areas.
Some executives will think that developing an AI strategy should be the first step. In my experience, most companies will not be able to develop a thoughtful AI strategy until it has had some basic experience with AI, which partial progress in steps will give you.
The way you build defensible moats is also evolving with AI.
Here are some approaches to consider: Build several difficult AI assets that are broadly aligned with a coherent strategy: AI is enabling companies to build unique competitive advantages in new ways. It thus becomes difficult for a competitor to replicate all of these assets simultaneously. This data helps the companies build a more accurate search engine product A , which in turn helps them acquire more users B , which in turn results in their having even more user data C.
This positive feedback loop is hard for competitors to break into. Data is a key asset for AI systems. But having more data almost never hurts. AI teams are using very sophisticated, multi-year strategies to acquire data, and specific data acquisition strategies are industry- and situation-specific.
For example, Google and Baidu both have numerous free products that do not monetize but allow them to acquire data that can be monetized elsewhere.
Recognize what data is valuable, and what is not: It is not true that having many terabytes of data automatically means an AI team will be able to create value from that data. Avoid this mistake by bringing an AI team in early during your process of data acquisition, and let them help you prioritize what types of data to acquire and save.
Create network effect and platform advantages: Finally, AI can also be used to build more traditional moats. For example, platforms with network effects are highly defensible businesses.
If AI allows you to acquire users faster than your competitors, it could be leveraged into building a moat that is defensible through platform dynamics. More broadly, you can also use AI as a key component of low cost strategy, high value, or other business strategies. Develop internal and external communications AI will affect your business significantly.
To the extent that it affects your key stakeholders, you should run a communications program to ensure alignment. Here is what you should consider for each audience: Investor Relations: Leading AI companies such as Google and Baidu are now much more valuable companies in part because of their AI capabilities and the impact that AI has on their bottom lines.
Explaining a clear value creation thesis for AI in your company, describing your growing AI capabilities, and finally having a thoughtful AI strategy, will help investors value your company appropriately.
Government Relations: Companies in highly regulated industries self-driving cars, healthcare face unique challenges to stay compliant. Developing a credible, compelling AI story that explains the value and benefits your project can bring to an industry or society, is an important step in building trust and goodwill.
This should be coupled with direct communication and ongoing dialogue with regulators as you rollout your project. AI engineers want to work on exciting and meaningful projects. A modest effort to showcase your initial successes can go a long way. Internal Communications: Because AI today is still poorly understood and Artificial General Intelligence specifically has been over-hyped, there is fear, uncertainty and doubt.
Many employees are also concerned about their jobs being automated by AI, though this varies widely by culture for example, this fear appears much more in the US than in Japan. A historical note, important for your success Understanding how the internet transformed industries is useful for navigating the rise of AI. There is a mistake that many businesses made navigating the rise of the internet that I hope you will avoid as you navigate the rise of AI.
What defines a true internet company is: Have you organized your company to do the things that the internet lets you do really well? An internet company may even have hundreds of experiments running at the same time; this is very hard to do with a physical shopping mall. Internet companies can also ship a new product every week and thus learn much faster than a shopping mall that might update its design only once per quarter.
Internet companies have unique job descriptions for roles such as product manager and software engineer, and those jobs have unique workflows and processes for how they work together.
Deep learning, one of the fastest growing areas of AI, is showing parallels to the rise of the internet. Sufficient understanding of AI: There should be general understanding of AI, with appropriate processes in place to systematically identify and select valuable AI projects to work on. Turning your great company into a great AI company is challenging but feasible with the support of great partners.
My team at Landing AI is committed to helping partners with their AI transformations, and I will continue to share additional best practices.