Murat Baday Lecture 9

Core Traits of Succesful Entrepreneurship

• The most effective way to become an entrepreneur is to develop exceptional listening and analytical skills.

• Failing to listen to others leads to a narrow echo chamber where an individual only pursues their own ideas.

• Observational skills and the willingness to ask numerous questions are essential for growth.

• Understanding the pitfalls and strengths of a project is critical; entrepreneurs should view every presentation or feedback session as if it were their own graph or data point.

• Feedback provided to one person often applies universally to others in similar positions.

Project Study: AI Booster for Small Businesses

• Concept: Empowering small businesses via modern AI tools to facilitate faster, easier, and smarter growth.

• The Problem: Small businesses are losing clients because they lack the knowledge to implement AI tools properly. Statistics indicate:

  • $90\%$ of businesses believe AI is crucial for survival.

  • Only $30\%$ actually know how to utilize it effectively.

  • Significant capital is wasted on marketing and automation due to excessive time spent on manual tasks.

• The Solution: A hands-on training program focused on simple tools like ChattyPity (ChatGPT) and Canva. It aims to help businesses automate processes and boost sales without requiring large investments, expensive software, or specialized developers.

• Timeline: Measurable results are expected within $1$ to $2$ weeks.

• Comparison of Competitors:

  • Coursera: Offers online education but is often criticized for being too theoretical, expensive, and lacking practical application.

  • Fiverr: Provides chatbot and AI automation services but lacks training or skill-building for the clients themselves.

• Workflow:

  1. Choose a real small business (cafe, shop, or course provider).

  2. Identify areas for improvement such as marketing, customer service, Instagram profiling, or finding major/minor operational problems.

  3. Build simple solutions (chatbots, automated emails, AI-generated posts).

  4. Implement and track performance improvements.

  5. Document successes for participant portfolios.

• Industrial Research (2023 Statistics):

  • Salesforce Research: $90\%$ of small businesses believe AI will impact their industry, but only $30\%$ currently use it.

  • McKinsey Report: Small businesses adopting AI tools can increase efficiency by up to $40\%$.

Project Study: AI Car Spotting Application

• Concept: An interactive app centered around car spotting and identification using AI.

• Problem Statement: Car enthusiasts and spotters lack a straightforward, rewarding method for documenting and sharing their finds.

• Solution Features:

  • AI-assisted identification of any car.

  • A unique sharing platform to create an interactive social community.

  • Chat programs and portfolio-building features.

  • Using AI systems to locate nearby car shows.

  • A focus on a stylish, simple User Interface (UI).

• Competitive Landscape:

  • Car Spotting (Monotrend): Basic functionality centered only on collecting cars.

  • CarSpot (Google Apps): Allows spotting and collecting but lacks a social sharing component.

  • Brand Spotters: Criticized for having a "cheap" interface and lacking social media-style sharing.

• Market Strategy: Leveraging social media for free advertising, pursuing sponsorships following growth, and utilizing word-of-mouth marketing.

Project Study: JobSprout

• Concept: A platform designed to help teenagers secure internships and prepare for future careers.

• The Problem: Two out of three teens ($2/3$) interested in internships do not pursue them because they do not know how to find opportunities, they have heavy course loads, or they find the pay to be insufficient.

• Platform Features:

  • Simplified guides for resume building.

  • Advice on passing interviews.

  • Application templates and supplemental mini-courses.

  • Interaction and advice from teens who have successfully secured internships.

  • Direct cooperation with organizations and companies to facilitate acceptance.

• Competitors: Platforms like LinkedIn or Indeed are seen as confusing for newcomers or teenagers.

Project Study: Storify

• Concept: A flexible, digital-first short-term storage platform, described as "Airbnb for storage."

• Problem: Small businesses and individuals lack options for short-term storage; traditional facilities are often expensive, inconvenient, or inflexible.

• Solution: Connecting those needing storage with those who have available space through a mobile app or website featuring instant booking and smart matching.

• Market Opportunity: The short-term storage market is valued at over $50,000,000,000$ and continues to grow. Key segments include individuals during moves or renovations and small e-commerce businesses.

• Business Model:

  • $15\%$ commission on every transaction.

  • Subscriptions and premium listings.

  • Promoted storage spaces.

• Financial Ask: Requesting $250,000$ for $20\%$ equity to fund technical development and a rollout across three major cities.

• Projections: Targeting $10,000$ active users within $12$ months.

Investment Stages and Financing

• Pre-seed Stage: Primarily funded by family and friends. The focus is on generating a Proof of Concept (PoC). Success rates are generally low, and it is difficult to attract large institutional investors at this stage.

• Seed Stage: Funding typically ranges from $1,000,000$ to $3,000,000$ (sometimes up to $5,000,000$ or $6,000,000$). This stage focuses on prototype development and building a Minimum Viable Product (MVP).

• Series A: Serious funding ranging from $3,000,000$ to $10,000,000$. At this point, the company is officially evaluated. The focus is on validating technology and commercializing the prototype. Success rates for companies reaching this stage rise to $40\%$ or higher.

• Series B and Beyond: Funding of $5,000,000$ to $20,000,000$ or more. These rounds focus on building partnerships and scaling a product that is already selling well.

Types of Investors

• Angel Investors: Individuals who mostly invest in seed and pre-seed funding, though some participate in Series A.

• Venture Capitalists (VCs): Companies that manage funds from others (called LPs or Limited Partners, such as retirement funds or hedge funds) and invest them into startups.

• Corporate/Company Funding: Direct investment from established corporations, particularly useful in sectors like Clean Tech.

• Government Funding: Available for specific industrial or technological advancements.

Due Diligence for Investors and Entrepreneurs

• Less than $1\%$ of startups succeed; thorough due diligence is required for both investors and founders.

• Executive Team: Assess if the team can scale the business. A founder's background is a primary indicator; repeat successful entrepreneurs have higher success probabilities.

• Market: You can throw money at technology, but you cannot throw money at a market if it does not exist.

• Product/Service: Evaluated based on its disruptive potential.

• Legal Side: Check for "poison pills" in agreements, such as exclusive contracts that limit market expansion, and ensure Intellectual Property (IP) is protected.

• Financials: Looking at the fundamental health of the business.

• Fraud Awareness: High-profile failures like FTX and Theranos show that even prominent VCs (like Sequoia) and accredited investors (like Rupert Murdoch or Larry Ellison) can be victims of fraud.

Specific Business Models and Key Performance Indicators (KPIs)

• Enterprise: Traditional software sales with licenses and annual maintenance fees (e.g., Oracle, Microsoft). KPIs: Bookings, total customers, customer concentration risk.

• SaaS (Software as a Service): Pricing per seat per month (e.g., Salesforce, ServiceNow). KPIs: Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), Gross MRR Churn, Cost of Acquisition (CAC).

• Subscription: Fixed fees for access (e.g., Netflix, Adobe). KPIs: MRR, Compounding Monthly Growth Rate, Churn, CAC.

• Transactional: Fees taken per transaction (e.g., Stripe, Square, Adyen). Square disrupted the market with a transparent model of $2.5\% + 30\text{ cents}$. KPIs: Gross Transaction Volume (GTV), Net Revenue, User retention by cohort.

• Marketplace: Matching buyers and sellers (e.g., Airbnb, Uber, Etsy). KPIs: Gross Merchandise Value (GMV), Net Revenue, Revenue Growth, User retention frequency.

• Advertising: "Free" services monetized through user data and ads (e.g., Meta, Google). KPIs: Daily Active Users (DAU), Monthly Active Users (MAU).

• Hardware: Selling physical goods (e.g., Fitbit, GoPro). KPIs: Monthly revenue, Gross margin, CAC.

• Usage-Based: Charging based on specific consumption (e.g., AWS, Snowflake). This is the primary model for AI due to variable costs.

• Deep Tech: High-risk, high-upfront investment (e.g., Biotech, Space tech like Boom Supersonic). Often relies on pre-selling or government contracts to fund development.

The Impact of AI on Business Models

• AI introduces a conflict with fixed-price SaaS or Subscription models because AI has incremental costs per token.

• Text-based AI is manageable, but voice and video AI are significantly more expensive to process.

• In the future, fixed-price models will likely decrease in favor of usage-based models to account for these processing costs.

Investment Lessons from Public Markets

• Public markets offer a way to practice due diligence with liquid assets and available data.

• Success Cases (IPO to Peak):

  • Apple: $136,000\%$

  • NVIDIA: $207,000\%$

  • Tesla: $11,000\%$

• NVIDIA Case Study: The company nearly went bankrupt multiple times. They had only $18$ months of cash left when Microsoft changed graphics standards. They survived by inventing new emulation processes and later boomed due to crypto mining and AI demand, eventually reaching a valuation over $3,000,000,000,000$.

• Risk Cases:

  • Plug Energy: $-98\%$

  • Silicon Valley Bank: $-100\%$

  • First Republic Bank: $-100\%$

Questions & Discussion

• Question (Ayesha): For an Economics major moving to universities like San Diego or UCLA, what are the connections and advice for the future?

• Response (Daniel): Economics is a difficult field because predicting the future is nearly impossible. Current unemployment data from the government may not reflect the reality of layoffs because there is typically a six-month lag between a person being laid off and appearing in unemployment statistics (due to severance). The best advice is to look at metrics and underlying data to build a statistical understanding of the future, while acknowledging that events like COVID-19 or geopolitical conflicts can disrupt any model.

• Response (Instructor): Students should focus on two main components for their final presentations: Market Strategy and Business Model (how they will make money). Investors will not participate in a project that lacks a clear revenue path.