AI Engineer World’s Fair 2025 - Tiny Teams
By AI Engineer
StartupAIBusiness
Share:
Key Concepts
- Tiny Teams: Small, highly effective teams achieving significant results.
- Generalist vs. Specialist: Hiring individuals with broad skillsets and adaptability versus those with narrow expertise.
- Player-Coach Model: Leaders who actively contribute to the work while also mentoring and guiding the team.
- Product-Led Hiring: Attracting talent through the appeal and utility of the product itself.
- Profit-First Mentality: Prioritizing profitability as a key decision-making metric.
- Super Tools: Leveraging existing tools in unconventional ways to maximize their utility.
- Harvester and Cultivator Model: Organizing engineering teams into product-focused (Harvesters) and infrastructure-focused (Cultivators) roles.
- Benchmarks as Memes: The idea that benchmarks, like memes, spread and influence the development of AI models.
- Benchmark Saturation: The phenomenon where models become overly optimized for specific benchmarks, diminishing their real-world relevance.
- High Agency: Individuals who take initiative, ownership, and solve problems independently.
Stack Blitz's Bolt.net: Scaling with a Small Team
The Unexpected Success of Bolt
- Stack Blitz, after seven years as a company with $0.7 million ARR, launched Bolt.net.
- The team was less than 20 people at launch.
- Initial expectations were modest, aiming for $100,000 ARR by year-end.
- Bolt exceeded expectations, more than doubling ARR in a short period.
- The growth was remarkably smooth, despite the product being a stripped-down MVP.
Team Structure and Philosophy
- Small Teams with More Context: Emphasizing a small number of people with broad understanding and agency.
- Empowerment and Speed: Enabling team members to build and implement without excessive bureaucracy.
- Low Burn Rate: Maintaining a lean operation to extend runway and increase opportunities for product market fit.
- Shared Core Values: Prioritizing low ego, high trust, user obsession, and resilience.
- Analogy to the Movie 300: The feeling of being a small, aligned team surrounded by overwhelming demands.
Prioritization and Firefighting
- The Fire Truck Analogy: Prioritizing high-impact areas and key infrastructure during periods of intense demand.
- Hard Decisions: Accepting that some issues will have to be left unresolved.
- Focus on the Essential: Concentrating on 10% of the tasks that yield the majority of the results.
Independent Thinking and Community Engagement
- Avoiding Hive Mind: Encouraging independent decision-making and critical evaluation of industry trends.
- Leading from the Front: Actively engaging with the community to build trust and gather feedback.
- Weekly Office Hours: Hosting live sessions to showcase progress and address user concerns.
- Community Building: Creating spaces for users to learn from each other and receive support.
- Hackathon.dev: Hosting the world's largest hackathon to drive product adoption and gather feedback.
AI-Powered Support
- Parah Help: Implementing AI-powered support tools to automate ticket resolution.
- SAM AI Assistant: Using Parah Help's SAM to resolve 90% of support tickets automatically.
- Custom AI Models: Training internal models to assist users within the product experience.
Key Takeaways
- Don't hire an army, hire a small number of Spartans.
- Focus on building a strong, aligned team with shared values.
- Prioritize ruthlessly and focus on the most impactful tasks.
- Engage with the community and build trust.
- Leverage AI to automate support and improve the customer experience.
Alie: The New Lean Startup
The Shift Towards Leanness
- Companies are becoming smaller, rounds are getting delayed, and profitability is being attained earlier.
- AI tooling is driving this shift, enabling small teams to generate significant ARR.
- The age of bloated teams and endless hiring rounds is over.
Alie's Success
- Building a portfolio of consumer software products.
- Scaling to $6 million ARR profitably with a team of four.
- Generating over half a billion views across social media.
Key Milestones
- January 2023: Launching Quizard AI mobile app with a viral TikTok video.
- Early Scaling: Utilizing the initial Codex model for AI outputs, cycling through multiple accounts.
- Fall 2023: Moving to New York City and launching a back-to-school campaign, hitting $1 million ARR and profitability.
- Spring 2024: Achieving number six in the education charts alongside companies like Dualingo.
- New Product: Unstuck AI: Reaching a million users in under nine weeks and generating over a quarter billion views.
- Third Product Launch: Launching a profitable product outside the education domain in three weeks.
The Lean Playbook
- Operating Principles:
- Hiring: Hiring 10xer generalists with multiple complementary spikes.
- Profit First Mentality: Prioritizing profits as a key decision-making metric.
- KPI Ownership: Aligning everyone in the company to a KPI.
- Continuous Process Refinement: Continuously improving processes based on feedback and failures.
- Super Tools: Building compounding benefits by investing in technical playbooks and operational blueprints.
- Organizational Structure:
- Harvester and Cultivator Model: Organizing engineering teams into product-focused (Harvesters) and infrastructure-focused (Cultivators) roles.
- AI Tooling Augmentation:
- Using AI to automate tasks and augment the capabilities of the team.
Super Tools: Launch Darkly
- Intended Use Case: Feature management platform.
- Extended Use Cases:
- Manual Traffic Load Balancer: Rerouting traffic to different LLM providers based on rate limits.
- On-the-Fly Infrastructure Changes: Changing the prioritization of ingestion processes.
- UI Modifications and Paywall Experiments: Running experiments without code pushes.
Key Takeaways
- Hire senior generalists who can work across the stack.
- Prioritize profits and focus on moving key metrics.
- Continuously refine processes and leverage super tools.
- Organize teams into product-focused and infrastructure-focused roles.
- Use AI to automate tasks and augment the capabilities of the team.
Gum Loop: Scaling Automation with a Small Team
The Gum Loop Journey
- Founder Max Broer Herbas spent six months building various software projects.
- Created the first UI for AutoGen, an open-source framework for AI agents.
- Realized that users needed a way to define complex workflows for AI agents.
- Pivoted to a workflow automation tool.
- Joined YC, raised a seed round, and then a series A.
- Scaled with under 10 people, focusing on hiring exceptional talent.
Hiring Approach
- Be Super Picky: Only hire candidates that are exceptionally exciting.
- Product-Led Hiring: Attract talent through the appeal and utility of the product itself.
- Work Trials: Bring candidates on for several days to assess their fit and skills.
Internal Operations
- Almost No Meetings: Prioritize deep focus time for building.
- Let People Build: Empower team members to take ownership and build without excessive oversight.
- Automate Everything Internally: Use Gum Loop to automate various business processes.
Culture
- What If We Built It Today?: Encourage rapid iteration and shipping.
- Make It Fun: Organize retreats and activities to offset the intensity of building.
- Be Intentional About Company Culture: Document values and expectations in a company handbook.
Key Takeaways
- Hire exceptional talent and give them the space to build.
- Automate everything possible to maximize efficiency.
- Create a fun and supportive culture.
- Be intentional about company values and expectations.
Benchmarks as Memes
The Power of Benchmarks
- Benchmarks, like memes, spread and influence the development of AI models.
- Benchmarks define what model providers are trying to get their models good at.
- A single person can come up with an idea for a benchmark that shapes the future of AI.
The Problem with Benchmarks
- Benchmarks are getting saturated.
- Models are becoming overly optimized for specific benchmarks, diminishing their real-world relevance.
- Benchmarks can be gamed or manipulated.
The Opportunity
- Create new benchmarks that are more relevant and meaningful.
- Shape the future of AI by defining what models should be good at.
- Empower people by giving them agency in the development of AI.
Characteristics of a Great Benchmark
- Multifaceted: Allows for a variety of strategies to succeed.
- Reward Creativity: Encourages innovative solutions.
- Accessible: Easy to understand for both models and people.
- Generative: Can be used to train future models.
- Evolutionary: Gets harder as models improve.
- Experiential: Mimics real-world situations.
AI Diplomacy: A Case Study
- A benchmark that uses the board game Diplomacy to assess the social and strategic abilities of AI models.
- Models must negotiate, form alliances, and betray each other to win.
- Revealed interesting personality traits of different models.
- Highlights the need for squishy, non-static benchmarks.
The Importance of Trust
- Benchmarks can help build trust in AI by showing people how it works and what it can do.
- People need to feel like they have a role in the development of AI.
- Benchmarks can empower people by giving them agency in the process.
Key Takeaways
- Benchmarks are powerful tools that can shape the future of AI.
- Create benchmarks that are relevant, meaningful, and empowering.
- Focus on building trust in AI by involving people in the process.
Chat with this Video
AI-PoweredHi! I can answer questions about this video "AI Engineer World’s Fair 2025 - Tiny Teams". What would you like to know?
Chat is based on the transcript of this video and may not be 100% accurate.