Pre-DoorDash and Early Days at DoorDash:
Toby Espinosa joined DoorDash six years ago, when the company was not even the largest food delivery company in its hometown of Palo Alto. His first job at DoorDash was launching new markets, traveling to cities like Phoenix, Atlanta, and Vancouver to turn on the service and expand the company’s reach.

Discovery of Enterprise Restaurants:
During the early days of DoorDash’s market expansion, Espinosa and his team noticed a pattern: whenever they turned on a large enterprise restaurant chain, such as McDonald’s or Chipotle, they saw a surge in consumer activity and merchant performance.

Building the Enterprise Restaurant Business:
Recognizing the potential of enterprise restaurants, Espinosa and his team built the enterprise restaurant business at DoorDash from scratch. Over the next four years, they grew the team from three people to 120 and expanded the business to include grocery, retail, and international partnerships.

Key Principles and New Projects:
Espinosa emphasized the importance of being merchant-first and considering all sides of the marketplace, including dashers and consumers, in DoorDash’s approach to growth. He recently worked on a new project, a new business that DoorDash is excited to launch in the future.

Problem-Solving Approach:
DoorDash’s core mission is to empower local economies by solving problems for local businesses and consumers. The company identifies key problems through customer feedback, market research, and user research. Once a problem is identified, DoorDash evaluates its size, potential business opportunity, and competitive advantage. If these factors align, the company moves forward with developing a solution.

Stakeholder Considerations:
DoorDash recognizes the importance of balancing the needs of its three key stakeholders: merchants, customers, and dashers. The company strives to solve problems that benefit all sides of the marketplace. Sometimes, solving a problem may start with focusing on one stakeholder group, but as the business scales, DoorDash ensures there is a check and balance to address the needs of all stakeholders.

Examples of Problem-Solving:
DoorDash Drive: A white-label capability that enables merchants to fulfill their own deliveries or own the consumer channel on their website. DashPass: A subscription service that offers unlimited delivery for a flat monthly fee, addressing the pain point of delivery fees for customers. Pickup: A service that allows customers to pick up their orders from restaurants, which has become the largest pickup marketplace in the country.

Conclusion:
DoorDash’s success in developing new products and services stems from its problem-solving approach, consideration of stakeholder needs, and ability to identify and address key market opportunities.

Scale’s Mission and Approach:
Scale’s mission is to accelerate the development of AI applications and help AI be applied to as many use cases as possible. The company sees AI as the greatest secular shift in technology and aims to remove hurdles in the way of AI adoption.

The Bottleneck of Data:
Scale identified data as a significant hurdle in AI development, particularly the challenge of obtaining great data sets. The company started in the autonomous vehicle industry, which required massive amounts of data and had stringent safety requirements.

Customer-Centricity and Problem-Solving:
Scale’s approach is to be customer-centric and continually address the most challenging problems faced by its customers. The company believes that by solving these problems, it can help its customers accomplish their missions and further its own mission. Scale’s goal is to build beautiful and elegant solutions to customer problems and keep iterating on this process.

Transition from Data Annotation to AI Readiness and Infrastructure:
Scale has transitioned from being a data annotation partner to a full AI readiness and AI infrastructure partner for its customers. This transition involves building products and solutions that address customer problems and help them leverage AI effectively.

Identifying Customer Problems and Building Solutions:
Scale identifies customer problems that can be solved using its technology, expertise, or operational capabilities. The company has launched various products, including Scale Nucleus for data management, Scale Document for document extraction, and products for e-commerce, marketplace, and autonomy customers.

Starting with Self-Driving Cars as a Meaty Problem:
Scale’s initial focus on the self-driving car use case proved to be a challenging but valuable starting point. Solving this problem allowed the company to develop strong capabilities that could be applied to other domains.

New Business Considerations:
Scale is open to exploring new businesses that align with its mission of empowering AI and helping customers achieve greater impact. The company evaluates new business opportunities based on whether they further its mission and help AI achieve greater impact in the real world.

AI at DoorDash:
AI is integral to DoorDash’s operations and helps the company solve problems and scale new businesses. DoorDash uses AI for tasks such as predicting demand, optimizing delivery routes, personalizing customer experiences, and detecting fraud.

Core Business Delivery:
Delivery involves multiple steps that need to go right. DoorDash has a visual at its headquarters depicting the complexity of the delivery process.

Problem Solving at DoorDash:
SWAT teams are assigned to solve problems, such as white label business issues or long dasher waits. Small teams brainstorm and create MVPs to test solutions using a “duct tape” approach. Successful solutions are scaled into the platform for massive output.

The Importance of Data:
Rapid scale and small changes powered by technology and data yield significant results for DoorDash. The focus is on finding cents and minutes to optimize the massive logistics and marketplace systems.

AI in Core Business and New Use Cases:
AI can bring gains to core businesses and new use cases built on top of them. Scaling small tweaks in algorithms allows DoorDash to offer grocery, alcohol, and other delivery services. This logistics engine can pull in data to assign alcohol-enabled dashers for relevant deliveries.

Launching New Products with AI:
DoorDash launches new businesses that may not initially have the scale of the core business. AI can be applied to new businesses to identify growth opportunities and potential challenges. AI can help DoorDash understand customer preferences and tailor products accordingly.

AI Implementation Considerations:
AI will eventually solve most problems, but the timing is crucial for successful implementation. Businesses should consider the time frame for AI implementation, as it can range from months to years.

DoorDash White Label Business:
DoorDash’s white label delivery service allows other companies to use its logistics system to fulfill their own delivery needs. The white label business initially focused on small order fulfillment, such as Chipotle orders.

Challenges with Grocery Delivery:
Grocery delivery presents different challenges compared to small order fulfillment. Larger basket sizes, longer wait times, and different pickup locations require adaptations.

Lack of Labeling:
Initially, the white label system lacked labeling for different use cases, such as grocery delivery. Adding labels allows the system to handle various permutations and scale effectively.

AI for Business Applications:
AI is essential for scaling business applications and solving problems. Businesses should evaluate when to implement AI, considering the time frame for scalability.

Learning from AI Implementation:
Businesses can learn from the implementation of AI systems in various contexts. Asking questions and considering the scalability potential of AI is important.

AI for Scalability:
AI enables businesses to scale on steroids compared to traditional software. Businesses should consider AI implementation for both new revenue opportunities and small feature enhancements.

Dasher Wait Time:
Dasher wait time is a key metric that DoorDash tracks, as it affects the overall efficiency of the ecosystem. When Dashers have to wait for food at restaurants, it reduces their time on the road and can lead to longer wait times for consumers.

Solution for QSR Clients:
To minimize Dasher wait time for quick-service restaurant (QSR) clients, DoorDash implemented a feature that delays the firing of orders until the Dasher breaks a geofence. This ensures that the food is prepared just as the Dasher arrives, reducing wait times.

Advice for Scaling Solutions:
Start with a single use case or problem, focusing on a meaty problem with a large market. Over time, apply the solution to new use cases and scale it using technology. Avoid the “micro operator” mentality of solving problems in isolation and focus on scalability from the beginning.

DoorDash’s Approach to Scaling the Core Marketplace:
DoorDash has various ways to expand its business, but when it comes to scaling the core marketplace, the focus has been on expanding into new categories rather than new geographies. This is because expanding into new categories allows DoorDash to leverage its existing infrastructure and technology to serve a broader range of customer needs.

Benefits of Expanding into New Categories:
Expanding into new categories can increase the frequency of use for existing customers and attract new customers who may not have used DoorDash before. It also allows DoorDash to capture a larger share of the overall food delivery market.

Challenges of Expanding into New Categories:
Expanding into new categories can be challenging, as it requires DoorDash to develop new partnerships, integrate with different types of merchants, and address the unique needs of each category. It also requires DoorDash to invest in marketing and education efforts to inform consumers about the new offerings.

Key Insight:
DoorDash’s “and” philosophy drives its expansion strategy, seeking to grow geographically and deepen offerings in existing markets, targeting new use cases and customer segments.

Geographic Expansion:
DoorDash has expanded into new countries like Japan, and aims to enter additional markets.

Business Diversification:
DoorDash diversifies its offerings within its merchant cohort by launching new businesses for restaurants, such as white-label software and drive businesses. Additionally, it introduces new consumer-side businesses to cater to different use cases.

Expansion into New Categories:
DoorDash is venturing into new categories beyond its initial focus on restaurants and food. The company recently launched its convenience business, partnering with 7-Eleven, and plans to enter the grocery market. DoorDash’s vision is to address an increasing number of “eatable moments” in households.

AI in Logistics and Consumer Experience:
DoorDash uses AI to enhance its logistics platform, such as labeling Dashers for grocery delivery and ensuring alcohol deliveries are handled by Dashers who are 21 or older. The company leverages consumer data to personalize recommendations and improve the overall user experience.

AI in New Categories:
DoorDash faces challenges in applying AI to new categories due to a lack of historical data. The company works to overcome this by using operations and product strategies to encourage consumers to try new categories, allowing DoorDash to gather data and develop models for personalized recommendations.

AI Use Cases and Timelines:
DoorDash believes that every application will eventually have an AI use case, but the timeframe for implementation varies. The company acknowledges that it takes weeks or months to develop and deploy AI solutions.

Challenges in Scaling AI Systems:
DoorDash emphasizes the significance of time when scaling AI systems, as the process can be lengthy.

Data and Consumer Preferences:
It’s challenging to build accurate models when consumers lack the ability to express their preferences for specific features or options. The absence of data makes it difficult to determine consumer preferences accurately.

Adaptability and Changing Preferences:
Preferences and trends can change over time, necessitating adaptable models that can incorporate new information and adjust accordingly.

Looking at the Extremes:
When using technology to scale businesses, it’s important to focus on extreme cases rather than averages. The extremes often provide valuable insights for improving and refining the technology.

Training the Next Generation:
The next generation of operators should be trained to analyze extreme cases and identify areas for improvement. This approach can help businesses identify opportunities to optimize technology and scale more effectively.

Learning from Self-Driving and Autonomous Customers:
Working with self-driving and autonomous customers can provide valuable insights into achieving high levels of safety and reliability. These customers have a strong focus on perfection and safety, which can be applied to other industries and use cases.

Key Lessons Learned from DoorDash’s AI Journey:
Embrace AI as an Integral Part of Business Strategy: AI is not a choice but a necessity in today’s business landscape. The question is not whether to use AI but when and how. Focus on Edge Cases and Extremes: Average data and metrics can be misleading. Instead, businesses should focus on identifying and addressing edge cases and extreme scenarios to gain a deeper understanding of customer behavior and system vulnerabilities. This approach becomes increasingly important as AI handles routine tasks, leaving only complex and nuanced situations for human intervention. Prioritize the “And” Mindset: Successful AI implementation requires individuals who can seamlessly combine deep technical expertise with a broad understanding of business needs. These individuals can dive into intricate problems, grasp underlying principles, and simultaneously develop scalable solutions using technology and human resources. Finding and nurturing such talent is crucial for driving innovation and achieving business success.

Additional Insights:
Learning from Diverse Use Cases: The self-driving car industry provides valuable lessons for e-commerce companies. The focus on edge cases and operational efficiency in autonomous vehicles can be applied to e-commerce to enhance customer satisfaction and drive business growth. The Power of the “And” Mindset: Individuals who possess both deep technical knowledge and a strong understanding of business dynamics are invaluable assets in AI implementation. They can bridge the gap between technology and business, ensuring that AI initiatives align with overall strategic objectives. Scalability and Human Capital: AI should not be viewed as a replacement for human capital but rather as a tool to augment human capabilities. The goal is to leverage technology to automate routine tasks and free up human resources to focus on more strategic and creative endeavors.