do things dont scale ai first world
It sounds like you're referring to the concept from the tech/startup world often summarized as "Things Don’t Scale," and how it relates to AI and the first world.
"Things Don’t Scale" is a popular essay by Paul Graham, co-founder of Y Combinator, where he argues that early-stage startups often succeed by doing manual, unscalable tasks that help them grow gain initial traction before automating and scaling those processes.
If we combine this idea with AI and the context of the "first world" (developed countries), there are a few ways to interpret and contextualize it:
- AI Development and Early Deployment Require Manual Effort:
- AI Adoption Varies Across Geographies:
- Product/Service Design Influences Scalability:
If you want, I can provide a summary of the "Things Don't Scale" essay itself, or discuss in more detail how AI startups in developed countries should approach early growth and scaling challenges. Just let me know!
Of course. That's a fantastic and very insightful phrase. It perfectly captures a major strategic shift happening in tech right now.
Let's break down what "do things that don't scale, AI-first world" means.
The Two Core Concepts
1. "Do Things That Don't Scale"
This is a famous piece of startup advice, popularized by Paul Graham of Y Combinator. It means that in the very early stages, you shouldn't try to build a perfect, automated system to serve 10 million users. Instead, you should manually do the work to serve your first 10, 100, or 1000 users.
- Examples:
- Airbnb: The founders went to their users' apartments in New York to take professional-quality photos themselves.
- Stripe: The founders manually set up bank accounts for their first users to bypass complex integration processes.
- A new food delivery app: The founder might personally deliver the first 50 orders to talk to customers and restaurants.
- Why it works: It forces you into direct, human contact with your customers. You learn their real problems, their frustrations, and what they truly value. This is the fastest way to achieve Product-Market Fit.
2. "AI-First World"
This describes the current technological landscape. It means that for any new product or service, the default assumption is to ask: "How can AI solve this core problem?" The building blocks (APIs from OpenAI, Anthropic, Google, etc.) are now so accessible that AI isn't just an add-on feature; it's the foundational engine of the product.
- Examples:
- Not AI-First: A traditional CRM that adds a "AI-powered insights" tab.
- AI-First: A CRM that is just an AI interface. You talk to it like a colleague ("Find me all clients in the Midwest who haven't purchased in 6 months and draft a re-engagement email").
- Not AI-First: A note-taking app that can transcribe audio.
- AI-First: A note-taking app that automatically summarizes your meeting notes, extracts action items, and suggests related documents.
The Powerful Combination: "Do Things That Don't Scale" in an "AI-First World"
This is where the magic happens. The old "do things that don't scale" was about manual human effort. The new version is about manual AI effort.
You use AI not to build a giant, automated product from day one, but as your super-human "co-founder" to manually deliver a magical experience for your first users.
How It Works in Practice:
Let's say you want to build a service that creates personalized weekly fitness newsletters for gyms.
- Old Way (Manual Human):
- Talk to 10 gym owners.
- Manually write 10 different newsletters each week in Google Docs.
- Manually email them to each gym owner.
- New "AI-First" Way (Manual AI):
- Talk to 10 gym owners to understand their needs.
- For each gym, you personally use ChatGPT/Custom GPTs/Claude to manually craft a unique newsletter. You tweak the prompts for each one, add personal details, and ensure quality.
- You manually send them out.
Why the "AI-First" Manual Method is a Superpower:
- Unbeatable Speed & Personalization: You can deliver a level of personalization and quality that would be impossible for a human to do at scale, but is trivial for a human-guided AI. You're not building the factory; you are the factory, with an AI as your tool.
- Faster Learning: By being the "human in the loop," you learn exactly what prompts work, where the AI fails, what outputs customers love, and what they ignore. This is priceless data for when you do start to automate.
- De-risks Automation: Instead of guessing and building a complex, automated AI pipeline that might be wrong, you first build the perfect "manual" process. Then, you simply automate the steps you were already doing by hand. You are reverse-engineering your scalable product from a working, manual prototype.
- Focus on the Core Problem: It forces you to focus on the value (the amazing newsletter) and the customer, not on the complex engineering of a fully autonomous system.
Concrete Examples of This Strategy Today
- A "Personalized Children's Book" Startup: The founder doesn't build an app. They take orders via a simple form, then uses Midjourney and ChatGPT to manually create a unique, high-quality book for each child. They learn what characters and stories resonate before writing a single line of automation code.
- A "Legal Document Review" Service: The lawyer doesn't build a SaaS platform. They have clients email them documents, which they run through a custom AI prompt (using an API) and then manually check and refine the output before sending the analysis back. They learn the edge cases and client language.
- A "Social Media Manager" Tool: The founder manages the social accounts of 5 small businesses by hand. They use AI to brainstorm posts, write captions, and generate images, but they personally curate, edit, and post them. They learn the workflow before productizing it.
Conclusion:
"Do things that don't scale, AI-first world" is the modern playbook for building a breakout company. It means using AI as your primary, hands-on tool to manually deliver an incredible and deeply personalized experience to your earliest customers. This process teaches you everything you need to know to eventually build a scalable, defensible, and truly valuable AI-powered product.
You're right to point out that many things don't scale well in the AI-first world, especially when viewed from the perspective of the "First World" (developed, industrialized nations). Here's a breakdown of what doesn't scale and why, focusing on the AI-first implications:
1. Infrastructure & Resources (Beyond the Obvious):
- Data: AI thrives on data. While First World countries generate massive amounts of data, access, quality, and context within that data aren't always scalable:
- Data Silos: Data is often locked in proprietary databases, making it difficult to train large, general-purpose AI models.
- Data Bias: Existing datasets often reflect the biases of the populations and systems that generated them. Deploying these models globally can lead to unfair or inaccurate results for underrepresented groups. This perpetuates inequality.
- "Clean" Data: Even in developed countries, achieving consistently clean, accurate, and well-labeled data at scale is a constant challenge.
- Compute Power: Training and running complex AI models require significant computational resources (GPUs, specialized chips). While these are becoming more accessible, the cost is still a barrier, and the environmental impact of the energy consumption is a growing concern. Not sustainable or equitable.
- Energy: AI deployment can increase energy demand significantly. Scaling AI without renewable energy sources will exacerbate climate change, impacting the entire world disproportionately.
2. Human Capital & Expertise:
- AI Talent: The demand for AI specialists (data scientists, machine learning engineers, AI ethicists) far exceeds the supply, even in First World countries. Scaling AI relies on a growing workforce, which requires significant investment in education and training. Skills gap creates disparity.
- Domain Expertise: Building effective AI solutions requires deep domain knowledge in the specific area of application (e.g., healthcare, finance, agriculture). Scaling AI across diverse industries requires a diverse and specialized workforce.
- AI Literacy: A broader understanding of AI concepts and capabilities is needed among the general population. This will help people use AI effectively, understand its limitations, and participate in discussions about its ethical implications. Digital divide becomes more pronounced.
3. Ethical & Societal Considerations:
- Bias & Fairness: As mentioned earlier, AI models can perpetuate and amplify existing biases. Scaling AI without addressing these biases can lead to discriminatory outcomes in areas like hiring, lending, and criminal justice. Reinforces existing inequalities.
- Privacy & Security: AI systems collect and process vast amounts of personal data. Scaling AI without robust privacy safeguards can lead to data breaches, surveillance, and manipulation. Erosion of trust in technology.
- Job Displacement: AI and automation have the potential to displace workers in a variety of industries. Scaling AI without addressing the potential for job displacement can lead to increased unemployment and social unrest. Economic disruption.
- Regulation & Governance: Effective regulation is needed to ensure that AI is developed and deployed responsibly. This is a complex and evolving area, and many countries are struggling to keep pace with the rapid advancements in AI. Risk of unchecked power.
- Cultural Context: AI solutions developed in one cultural context may not be appropriate or effective in another. Scaling AI requires careful consideration of cultural differences and the potential for unintended consequences. Cultural imperialism through AI.
- Explainability & Transparency: Many AI models are "black boxes," making it difficult to understand how they arrive at their decisions. Scaling AI without improving explainability and transparency can erode trust and make it difficult to hold AI systems accountable. Lack of accountability.
4. Specific Applications and Context:
- Healthcare: While AI can improve diagnosis and treatment, access to healthcare varies greatly across the globe. Simply deploying AI solutions without addressing underlying inequalities will not solve the problem of unequal access to healthcare.
- Education: AI can personalize learning, but access to technology and internet connectivity is still limited in many parts of the world. Scaling AI in education requires addressing these digital divides.
- Agriculture: AI can optimize farming practices, but access to data, technology, and expertise is limited in many developing countries. Scaling AI in agriculture requires providing farmers with the resources they need to adopt these technologies.
- Infrastructure: Developing countries often lack the robust infrastructure needed to support AI-powered systems. Scaling AI requires investing in infrastructure improvements.
Why These Don't Scale Well (Key Principles):
- Context Matters: AI solutions are not universally applicable. They must be tailored to the specific context in which they are deployed.
- Equity & Fairness: Scaling AI without addressing existing inequalities will exacerbate those inequalities.
- Human-Centered Design: AI should be designed to augment human capabilities, not replace them entirely.
- Transparency & Accountability: AI systems should be transparent and accountable, so that people can understand how they work and hold them accountable for their decisions.
- Sustainability: AI should be developed and deployed in a way that is environmentally sustainable.
In conclusion:
The "AI-first world" presents tremendous opportunities, but also significant challenges. Simply applying solutions developed in the First World to other contexts without careful consideration of the factors outlined above will likely lead to failure and unintended consequences. A more nuanced and equitable approach is needed, one that prioritizes human well-being, environmental sustainability, and responsible innovation. This requires a shift in perspective from "how do we scale this AI solution?" to "how can we collaboratively develop AI solutions that address the specific needs and challenges of this community or region?"
💡 Try this comparison yourself:Compare AI models side-by-side on SNEOS
Analysis
This comparison demonstrates the different approaches each AI model takes when responding to the same prompt. Here are the key differences observed:
Response Characteristics
ChatGPT: Provides a detailed response with 14 sentences.
DeepSeek: Provides a direct response with 66 sentences.
Gemini: Provides a detailed response with 63 sentences.
Key Takeaways
- Each model brings unique strengths to this type of query
- Response styles vary significantly between models
- Consider your specific use case when choosing between these models
Try This Comparison Yourself
Want to test these models with your own prompts? Visit SNEOS.com to compare AI responses side-by-side in real-time.
This comparison was generated using the SNEOS AI Comparison ToolPublished: October 03, 2025 | Models: ChatGPT, DeepSeek, Gemini