The 1g of Tech Model: Value in the Smallest Units
Introduction: The Economics of Atomic Value
A key principle of business resilience is the ability to deliver complete value in the smallest possible units. Consider the simple economics of butter sales during economic downturns.
A vendor selling 100g of butter for $10 faces a fundamental constraint - customers who need butter but can't afford $10 simply cannot buy. However, a vendor who can sell 1g of butter for $1 continues making sales even when money is scarce. The difference isn't just pricing flexibility - it's that both the 100g and 1g portions deliver complete, standalone value to the customer.
This represents a principle of atomic value: the ability to break down offerings into their smallest complete units while maintaining full utility. The vendor selling 1g portions isn't compromising on quality or creating an incomplete product - they're delivering exactly what the customer needs in the moment they need it.
This principle raises a compelling question for technology development: What would atomic value look like in software? How do we break down complex product visions into their smallest complete units of value, and what advantages emerge when we do?
The 1 Gram (1G) of Tech Model Defined
The 1G model is about building truly compelling technology by starting with one complete, exceptional solution rather than many interconnected mediocre features. The key insight is that the strongest all-in-one products are built by creating features so complete and valuable that users would pay for each one independently, then combining these powerful components into an irresistible whole.
Instead of building half-finished features that only deliver value when bundled together, start with one feature that's so good it could be a standalone product. This becomes your bedrock. Then add other equally complete solutions. Features can depend on each other and work together - but each component must have independent standalone value. The result is an all-in-one product where each component is magnetic on its own, making the combined offering irresistible.
The 10G Problem
When people have ideas for solutions, those concepts are often abstract and represent what we call 10G problems - large, complex problems that seem to require solving many different pain points simultaneously.
The 1G approach: instead of trying to solve all pain points at once with one massive solution, identify and solve each pain point individually with complete, standalone solutions that work together. Each solution must be complete and valuable on its own, passing this test: "Could this feature exist as a standalone product and users would still pay for it?"
Building 1G solutions allows the market to validate each component immediately - users either pay for complete value or signal what needs improvement. Building 10G solutions at once makes market validation nearly impossible until everything is complete, requiring much longer development cycles and expensive refactoring when pieces don't deliver complete value.
Breaking down abstract concepts into specific units of problems allows you to identify exactly which pain points you're dealing with. Each pain point must have a quantifiable cost in either time or money. You can then create complete solutions for each pain point, which combine into a powerful comprehensive solution.
Multiple features that each deliver complete standalone value create something far more compelling than trying to solve all pain points with interdependent pieces. Users get multiple complete solutions that work together.
This approach uses monetization viability as the primary design constraint for feature definition. If a feature can't theoretically survive as its own product, it's either too small, too dependent, or doesn't solve a complete problem on its own.
How to Apply the 1G Model
Identifying 10G Problems
Common signs:
- Believing your product "only works" when multiple features are built together
- Struggling to explain the value of individual components
- Planning feature releases that depend on other unreleased features
- Finding it difficult to price individual capabilities
Breaking Down Into 1G Components
Each component must pass the pricing viability test: "What would someone willingly pay for this specific outcome if it were the only solution available?"
Requirements for each component:
- Solve a specific, deeply felt problem with a measurable cost in time or money
- Deliver complete value independently, even when part of a larger product
- Pass the standalone test - could this exist as its own product?
Operational Benefits
- Focused features require less development time and resources
- Time-to-market is dramatically reduced
- Maintenance and updates become more manageable
- Teams can focus deeply on mastering one problem space
Economic Principles
- Features must be independently monetizable - not just part of bundled pricing
- Monetization acts as a validation filter - if users won't pay for a feature independently, it needs improvement
- Sustainable with smaller user bases rather than requiring massive scale
- Each feature must justify its own development cost through direct value creation
Case Studies: From 10G Problems to 1G Solutions
Healthcare Practice Management
The 10G Problem
"Small practices need complete practice management software"
1G Feature Breakdown
catches errors before submission
reduces no-shows
automated claim management
Result
Each feature valuable independently, together they create comprehensive solution
Education Workflow Management
The 10G Problem
"Teachers need complete classroom management tools"
1G Feature Breakdown
creates custom materials in minutes
automated updates and messaging
performance insights
Result
Teachers can start with one tool, add others as needed
Plumbing Business Operations
The 10G Problem
"Plumbers need full business management software"
1G Feature Breakdown
automated documentation
automated ordering
automated booking system
Result
Each solves a specific pain point, together they streamline entire operation
Breaking 10G problems into 1G solutions creates stronger products: each component delivers immediate value while building toward a comprehensive offering that becomes increasingly difficult for users to leave.
The 1G Economic Advantage
Minimal Maintenance Burden
1G solutions are lightweight and focused on solving specific problems, resulting in simpler codebases that are easier to maintain. This dramatically reduces ongoing costs after the initial build, often requiring just a few hours of maintenance per month.
Linear Scaling Potential
While a 1G solution might only need 15-100 customers to be profitable, nothing prevents it from growing to serve hundreds or thousands of customers. The beauty is that revenue scales linearly while costs increase at a much slower rate.
Exceptional Capital Efficiency
Consider our healthcare example: At 25 practices, it generates $59,700 annually. If scaled to 100 practices, it could generate $238,800 with perhaps only an additional $15,000 in infrastructure costs – a return that far exceeds traditional investment opportunities.
The Best of Both Worlds
The 1G model offers the stability of profitability at small scale (reducing risk) while preserving the upside potential of traditional tech models. It's not about thinking small – it's about building solutions that don't require massive scale to be viable.
How 1G Creates Better MVPs
The 1G model provides a superior framework for scoping and building Minimum Viable Products. Most MVPs fail because they're defined as incomplete versions of large products - "build 30% of everything." The 1G approach flips this to "build 100% of something small."
Traditional MVP scoping creates products that can't be properly monetized because they're incomplete, provide weak value propositions that users tolerate rather than love, and require multiple iterations before reaching market viability. This makes it difficult to validate individual components and often leads to products that feel half-finished.
Using 1G methodology, your MVP becomes complete and monetizable from day one because users pay for getting full value for a specific problem. This creates immediately market-testable products with clear success/failure signals based on willingness to pay. The pricing viability test prevents feature creep and ensures tight scope, resulting in genuinely minimal products - you're building the smallest complete solution, not the largest incomplete one.
The key shift is in the scoping question itself. Instead of asking "What's the minimum we can build?" ask "What's the smallest complete problem we can solve that people would pay for?" This moves MVP development from building incomplete features that might work together, to building one complete feature that definitely works alone. The result is stronger products with clearer value propositions and immediate validation opportunities.
The 1G methodology doesn't replace MVP thinking - it makes MVP scoping dramatically more effective.
Mathematical Foundation of the 1G Approach
The 1G model is mathematically sound. Building multiple small solutions is often more effective than a single comprehensive one.
Probability Theory Perspective
If you're selling butter, would you rather:
- Invest all your money in one large store that has a 20% chance of success
- Set up five small butter stands in different neighborhoods, each with a 40% chance of success
With the first approach, you have a 20% chance of success. With the second, your chance that at least one stand succeeds jumps to 92.2%.
Detailed Probability Calculation
How we arrive at the 92.2% probability:
Step 1: Define what we're calculating
We want to find P(at least one of five stands succeeds)
Step 2: Use the complementary approach
P(at least one succeeds) = 1 - P(all five fail)
Step 3: Calculate probability of all failing
P(all five fail) = (1 - 0.4)^5 = 0.6^5 = 0.07776
Step 4: Calculate final probability
P(at least one succeeds) = 1 - 0.07776 = 0.92224 ≈ 92.2%
This dramatic increase from 20% to 92.2% is a direct mathematical result of spreading risk across multiple smaller solutions.
Now, formalized mathematically:
- A large solution S₁₀ₖ has a probability of success p₁₀ₖ
- A small 1G solution S₁ₖ has a probability of success p₁ₖ
- Given that small problems are more precisely defined, we can assume p₁ₖ > p₁₀ₖ
- Instead of betting everything on one S₁₀ₖ, we fund N independent S₁ₖ solutions.
The probability that at least one succeeds is:
As N increases and if p₁ₖ is significantly higher than p₁₀ₖ, the probability of at least one success approaches 1.
For our concrete example where a large solution has a 20% chance of success, while each small solution has a 40% chance:
- The probability of success with the large solution remains 20%
- The probability of at least one success among five small solutions is: 1 - (0.6)^5 = 0.922 or 92.2%
This mathematical reality explains why breaking a big problem into multiple 1G solutions reduces risk and increases the overall probability of return.
The combined mathematical evidence from probability theory creates a robust foundation for the 1G model's effectiveness. The math confirms what intuition suggests—smaller, focused solutions distributed across multiple problems create more resilient businesses with higher chances of success.
Conclusion: Building Solutions That Matter
The 1G model changes how we think about building technology. Instead of starting with grand visions and building toward them with incomplete features, start with complete solutions to real problems and build outward from strength.
This approach solves the fundamental challenge facing most product development: how to create immediate value while building toward something larger. Traditional methods force you to choose between focus and comprehensiveness. The 1G model gives you both - focused solutions that combine into comprehensive offerings.
The methodology is simple but powerful: identify pain points with quantifiable costs, build complete solutions that people would pay for independently, then combine multiple complete solutions into irresistible products. Each step creates value, each step can be validated by the market, and each step builds toward something stronger.
Most importantly, this isn't theoretical. You can apply 1G thinking to your next feature, your next product, or your next business idea. Ask yourself: "What's the smallest complete problem I can solve that people would pay for?" Then build 100% of that solution rather than 30% of something larger.
The technology industry has convinced us that building great products requires massive scale, huge teams, and years of development. The 1G model proves otherwise. Sometimes the most powerful approach is also the simplest: solve one problem completely, then solve the next one.