The 1G Tech Model: Value in the Smallest Units of Technology

Introduction: The Butter Analogy

In 2022, during a conversation with a tea company founder in Nairobi, a simple yet profound business insight emerged: "A business that can survive hard times either sells food or can sell in the tiniest possible amounts."

The example was straightforward. When economic conditions deteriorate, people who sell butter in 100g packages for $5 struggle because customers can't afford the full amount. However, sellers who can offer just 1g of butter for $1 still make sales. People can usually find a single dollar even when they can't find five.

This principle represents fundamental business defensibility. If you've been selling 10g of butter, you sell 1g of butter during bad seasons when people have less money. Selling 1g of butter is better than selling no butter at all.

This insight raises a compelling question: What would 1g of technology look like? How can we break down tech into its smallest useful pieces, and what happens when we do?

The 1 gram (1G) of Tech Model Defined

The 1G model is about rethinking how we build and invest in technology. Instead of creating massive, all-in-one solutions, we focus on small, targeted solutions that solve one specific problem exceptionally well. But critically, each solution must have standalone monetizable value.

This principle works on two fundamental levels:

1

Solution Granularity

Instead of selling an app with many features, sell just one feature as a standalone tool with its own direct monetization model. This isn't just technical decomposition—it's about identifying which capabilities can generate value independently. Each component must solve a problem that users are willing to pay for directly.

2

Problem Granularity

Instead of trying to solve many problems in an ecosystem, define a "1g problem" that has a clear cost in either time or money. The beauty is that since the problem is 1g and has a measurable cost, the solution is also 1g and has a measurable value. But you can also have a 1g solution that addresses a critical aspect of a much larger 10g problem.

The relationship between problems and solutions in the 1G model is nuanced:

A 1g problem will always have a corresponding 1g solution (focused and complete)
A 10g problem can be addressed through multiple 1g solutions working together
A 1g solution can exist without necessarily solving a complete 1g problem - it might be addressing just one aspect of a larger problem

This approach challenges conventional thinking in three critical ways:

1

It requires every component to have standalone value, not just technical independence

2

It demands direct monetization of each component, not bundled or "freemium" models

3

It focuses on problems with measurable costs that translate to clear willingness to pay

This differs significantly from standard software modularization or microservices, which break down solutions technically but still sell them as bundled packages. In the 1G model, each piece must survive as its own business.

Core Principles of 1G Tech

1

1G of Solution Focus

Instead of building an entire product suite, sell one essential feature as a standalone solution with its own direct monetization
Each tool must solve a specific, deeply felt problem with a measurable cost in time or money
Each component must be able to survive as its own business, not just as part of a larger whole
Better to solve one problem completely than to address many poorly

2

1G of Problem Definition: A Logical Framework

The 1G approach follows a clear logical progression:

A 1G problem must have a quantifiable cost in either time or money
A 1G solution must have a direct price (typically around 1% of the cost it eliminates)
A well-designed 1G solution can completely eliminate a 1G problem
A 10G problem can be broken down into multiple 1G problems, each with its own 1G solution
Multiple 1G solutions can work together to solve larger problems while remaining independently valuable

This logical framework ensures that every solution creates measurable value that can be directly monetized. If a problem doesn't have a quantifiable cost, or if a solution doesn't eliminate enough of that cost to justify its price, it fails the 1G test and needs refinement.

3

Operational Efficiency

Focused solutions 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

4

Economic Viability Through Required Monetization

Solutions MUST charge a fee - they cannot be free (except for abstract solutions)

The recommended pricing guideline is approximately 1% of the time or money saved

This monetization requirement ensures profitability from day one

Sustainable with smaller user bases

Direct connection between value created and pricing

Acts as a filter—if users won't pay, the solution needs improvement

Defensible even during economic downturns

Practical Applications and Investment Economics

The 1G model creates compelling business opportunities across virtually any sector where specific problems can be identified and solved. Modern AI tools have dramatically reduced development time and costs, making these focused solutions more accessible than ever before.

Let's explore how these solutions perform economically, using realistic examples that demonstrate why this approach is increasingly attractive to both creators and investors.

Case Studies: Small Scale, Meaningful Returns

Healthcare

Problem: Small practices lose $10,000+ annually to billing errors
1G Solution: A focused billing verification tool that catches the most common mistakes
Initial Investment: $15,000
Monthly Fee: $199
Customers Needed: 25 practices
Annual Revenue: $59,700
Break-even: ~3 months

Plumbing Business

Problem: Inefficient record-keeping costs plumbers hours of admin time weekly
1G Solution: A specialized record-keeping system just for plumbers
Initial Investment: $5,000
Monthly Fee: $49
Customers Needed: 15 plumbers
Annual Revenue: $8,820
Break-even: ~7 months

Education

Problem: Teachers spend 4+ hours weekly creating custom worksheets
1G Solution: A worksheet generator that creates perfectly tailored materials in minutes
Initial Investment: $8,000
Monthly Fee: $29
Customers Needed: 100 teachers
Annual Revenue: $34,800
Break-even: ~3 months

These examples demonstrate a fundamental shift in tech economics: small, focused solutions can reach profitability in months rather than years, with minimal customer bases. Each solution delivers measurable value to users while creating sustainable businesses with compelling returns.

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.

Democratized Investment

Perhaps the most revolutionary aspect of the 1G model is how it opens up technology investment to everyday people, not just venture capitalists or tech companies.

Sarah, Dentist & Investor

Non-technical investor in healthcare tech

Sarah, a dentist with no technical background, can invest $10,000 in two 1G solutions addressing problems she understands from her professional experience. Within 18 months, both investments can generate positive cash flow, returning her initial capital while continuing to provide quarterly income.

Investment Profile

Initial Investment

$10,000

Time to Cash Flow Positive

18 months

Annual Return (Year 3)

$7,200 (72%)

Technical Expertise Required

None

Why 1G Investments Are Accessible

Lower capital barriers – $5,000-$15,000 vs. hundreds of thousands for traditional startups
Understandable business models – Clear value propositions that non-technical investors can evaluate
Faster feedback loops – Quick time to profitability means faster learning and reinvestment opportunities
Portfolio potential – Even with modest capital, investors can build diversified portfolios of several 1G investments

The AI Multiplier Effect

Modern AI tools have dramatically altered the development landscape, making the 1G model even more viable by reducing costs and accelerating timelines.

Development Factor	Before AI	With AI Assistance
Development Time	2-3 months	2-4 weeks
Engineering Team Size	2-3 developers	1 developer
Code Quality & Maintenance	Varies by developer	Consistently higher quality
Technical Expertise Required	High	Moderate to Low

Force Multiplier

AI tools enable individual engineers to accomplish what previously required entire teams, drastically reducing the initial investment needed for 1G solutions.

Reduced Engineering Debt

AI assistance can help generate cleaner, more maintainable code from the start, further reducing the long-term maintenance burden of 1G solutions.

Lower Barrier to Entry

Non-technical domain experts can now leverage AI to create solutions with minimal engineering support, opening up technology creation to those who understand problems deeply.

The emergence of powerful AI tools creates a perfect environment for the 1G model to thrive. As development becomes faster and more accessible, we're entering an era where smaller, focused solutions created by individuals or tiny teams can successfully compete with massive platforms built by large corporations.

Addressing Abstract Solutions

Not all problems involve direct time or money loss, but the 1G approach can also accommodate abstract concepts like social connection, creative expression, or knowledge management by focusing on specific implementations with clear engagement metrics.

For example, a social tool focusing on just one specific type of connection (e.g., book recommendations between friends) could start as a 1G solution with a clear focus, even if the monetization comes later through channels like affiliate links.

These might be categorized as "experiments" where the return isn't immediate financial gain but user acquisition, knowing that users in an established system can eventually be monetized through advertising, premium features, or data insights.

How 1G Differs from Existing Approaches

While the 1G model draws inspiration from several existing concepts in technology and investment, it differs in fundamental ways that collectively create a distinct approach to building and funding technology.

1G vs. Minimum Viable Products

Complete vs. Incomplete: MVPs are deliberately incomplete versions of larger products, while 1G solutions are complete answers to narrowly defined problems.
Day One Monetization: Unlike MVPs that often delay monetization to focus on adoption and growth, 1G solutions require direct monetization from the start.
End Goal Difference: MVPs are stepping stones toward a larger product, while 1G solutions are valuable endpoints in themselves that can optionally be combined with others.

1G vs. Angel Investing

Accessibility: While angel investing typically requires significant capital and tech knowledge, 1G investments are accessible to domain experts with modest capital and no technical background.
Time to Profitability: Angel investments often expect years until profitability, while 1G investments target break-even in months rather than years.
Scale Expectations: Angel investors typically seek explosive growth potential, while 1G investments can yield attractive returns even at modest scales.

1G vs. Microservices

Business vs. Technical Focus: Microservices are primarily a technical architecture pattern, while 1G solutions are business entities with independent monetization.
Monetization Model: Microservices are typically bundled in pricing or part of a larger product, while each 1G component must have its own explicit value capture.
Independence Level: Microservices usually depend on other services in an ecosystem, while 1G solutions must be independently valuable to users.

1G vs. Traditional Startups

Capital Efficiency: Traditional startups require substantial funding before reaching profitability, while 1G solutions are designed to be profitable with minimal initial investment.
Problem Selection: Traditional startups target problems with massive market potential, while 1G solutions can address valuable niche problems that impact smaller audiences.
The 1% Pricing Rule: The specific guideline to charge approximately 1% of the value created is a unique framework that ensures sustainability while maintaining compelling value for users.

1G vs. VC Portfolio Approach

Solution Granularity vs. Company Diversification: VCs diversify by investing in different companies tackling different problems, but each company typically still aims to build comprehensive solutions. The 1G model introduces granularity within the solution itself—breaking down problems at the design level into their smallest solvable components.
Single-Focus Monetization: While VC portfolios diversify investments across companies, each company in the portfolio typically still aims for a comprehensive product suite with bundled pricing. 1G solutions insist on independent monetization for each problem component.
Democratized Participation: The VC model requires specialized knowledge, networks, and typically millions in capital to build a diversified portfolio. The 1G approach allows individuals with domain expertise and modest capital ($5K-$15K) to participate in technology creation and investment.
Growth vs. Value Philosophy: VCs fundamentally operate on a "growth philosophy" where they expect most investments to fail but a few to achieve massive scale. The 1G model operates on a "value philosophy" where each individual solution is expected to be profitable on its own merits without requiring extraordinary scale.
Problem Selection Criteria: VCs select problems based primarily on market size and growth potential. The 1G approach selects problems based on clearly measurable costs (in time or money) that translate to willingness to pay, regardless of whether the problem affects millions or thousands.
Cultural Accessibility: The VC model has developed its own culture, language, and expectations that can be alienating to domain experts outside the technology industry. The 1G model provides a framework that's accessible to professionals in any field who understand specific problems deeply.

While individual elements of the 1G model may share similarities with existing approaches, the integration of these principles into a cohesive framework creates a distinct paradigm for technology development. This approach is especially relevant today, as AI tools reduce development costs and time-to-mmarket, making small, focused solutions more viable than ever before.

The Economics of 1G Technology

Think about how technology is typically built today: large initial investments, losing money on each customer at first, and hoping for explosive growth later. This model naturally filters which problems get solved based on their potential to generate huge returns, not on how important they are.

The Pricing Rule: A Non-Negotiable Component

A 1G solution MUST follow this pricing principle—it is not optional but fundamental to the entire approach:

1

Identify explicitly why someone is losing either time or money

2

If a person or business is losing X amount of money, demonstrate how your solution prevents that loss

3

If they're losing time, show how your tool saves Y days of work

4

Set your fee based on the value provided, with a guideline of approximately 1% of what you're saving them

The requirement to charge a fee (not necessarily exactly 1%) is non-negotiable for several critical reasons:

Validation mechanism

The requirement to charge forces you to build solutions that deliver genuine, measurable value. If users won't pay for the value created, the solution isn't truly solving the problem well enough.

Quality filter

This principle ensures only viable 1G solutions are implemented. If you can't define and capture value clearly enough to set a price, the solution needs further iteration.

Psychological commitment

Paying even a small amount fundamentally changes how users value and engage with a solution. Free tools are often undervalued and underutilized.

Sustainable foundation

Starting with a clear monetization model from day one creates businesses that don't rely on future speculation or massive scale to become viable.

The 1% figure serves as a helpful guideline: it's small enough that the value proposition remains overwhelmingly positive for users, yet sufficient to create sustainable economics for solutions that deliver meaningful value. Actual pricing may vary based on market conditions and specific contexts, but solutions must never be free (with the exception of certain abstract solutions where monetization may come through indirect means).

Since the initial cost to begin is already so low, and the solution is focused, there's a low upfront cost. This makes the risk-reward calculation much more favorable than traditional approaches.

Rethinking Freemium in the 1G Model

The 1G approach fundamentally changes how we think about freemium offerings. In traditional software businesses, freemium models provide incomplete solutions to entice users to upgrade. This creates friction and often leads to poor conversion rates, as users must be convinced that the paid version is worth the additional investment.

In the 1G model, freemium takes on a different role:

1G problems should never use freemium

If you're solving a genuine 1G problem with a measurable cost, you should charge for it directly. The value is clear, and freemium only undermines this clarity.

Free offerings should be complete solutions

Unlike traditional freemium, any free tool in the 1G ecosystem should provide complete value for a specific micro-problem, not a crippled version of a larger solution.

Free tools as marketing vehicles

The legitimate role of free in the 1G ecosystem is as a marketing tool for your broader collection of solutions. These free tools solve "nice-to-have" problems that aren't painful enough for users to pay for, but still deliver genuine value.

From free to ecosystem

Once users experience the quality of your free tool, they're more likely to consider your paid offerings when they encounter problems with quantifiable costs.

This rethinking of freemium avoids the common trap of building products that solve problems that aren't painful enough to monetize directly. Instead of trying to upsell incomplete solutions, the 1G approach treats free offerings as complete solutions to minor problems that serve as gateways to your ecosystem of paid solutions for more significant problems.

Traditional Tech Model	1G Model
High upfront investment	Small, targeted investment
Long timeline to revenue	Profitability from day one
Betting on one big success	Building an ecosystem of small, proven solutions
Focused on "unicorns"	Focused on consistent returns
Thousands/millions of users needed	Can be profitable with dozens of users
Freemium = incomplete solution	Free = complete solution to minor problem

Comparing Traditional Tech and 1G Business Models

Mathematical Foundation of the 1G Approach

The 1G model isn't just intuitively appealing—it's mathematically sound. Let's explore why building multiple small solutions is often more effective than a single comprehensive one.

Why This Math Matters to You

Before we dive into formulas and calculations, let's address a simple question: Why include mathematics in an essay about technology business models?

The math in this section isn't just theoretical—it provides powerful decision-making tools that can dramatically affect your success. Whether you're an investor deciding how to allocate capital, an entrepreneur choosing which products to build, or a business leader setting your technology strategy, these mathematical principles reveal why the 1G approach isn't just philosophically appealing but objectively advantageous.

Many business failures stem from misunderstanding the true probabilities of success. We often intuitively grasp that "not putting all eggs in one basket" is wise, but we rarely quantify exactly how much safer diversification makes us. The calculations below show not just that the 1G approach reduces risk, but by precisely how much—revealing that what seems like a small change in strategy can produce a dramatic improvement in outcomes.

These aren't academic exercises—they're practical tools that explain why venture capitalists diversify investments, why conglomerates often outperform specialized companies during economic downturns, and why the 1G model offers a compelling alternative to traditional technology development approaches.

Probability Theory Perspective

First, let's understand this through everyday terms. If you're selling butter, would you rather:

1

Invest all your money in one large store that has a 20% chance of success

2

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

Let's break down exactly 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% isn't arbitrary—it's a direct mathematical result of spreading risk across multiple smaller solutions.

Expected Value Analysis

We can further illustrate the advantage using expected value (EV) calculations:

Single Large Solution

Success probability: 20%

If successful, return value: R

Expected Value: 0.2 × R = 0.2R

Five Small Solutions

At least one succeeds: 92.2%

If successful, return value: R

Expected Value: 0.922 × R = 0.922R

Even if each small solution generates only a fraction of the return of the large solution, the approach remains advantageous as long as the combined expected return exceeds that of the large solution.

Now, let's formalize this 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:

P(at least one S₁ₖ succeeds) = 1 - (1 - p₁ₖ)^N

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.

Game Theory Analysis

From a game theory perspective, we can model technology investment as a strategic game:

Assume V is the total capital available for investment
Investing in S₁₀ₖ requires C₁₀ₖ, whereas investing in multiple S₁ₖ solutions requires C₁ₖ per unit
If C₁₀ₖ > N × C₁ₖ, then spreading investment across multiple S₁ₖ solutions diversifies risk and increases expected return

To illustrate this with numbers:

Development Costs

One large solution: $100,000

Five 1G solutions: $20,000 each

Total: $100,000

Revenue Needed

For one large solution: Often millions

For each 1G solution: Can be thousands

From a Nash equilibrium perspective, rational investors should allocate capital across multiple smaller bets rather than a single risky investment, particularly when the probability of success for each small bet is higher than for the large bet.

This is why venture capital firms diversify their investments across multiple companies instead of putting all resources into a single startup.

Computational Complexity Argument

The 1G approach also offers advantages in terms of problem-solving complexity. From a computational complexity standpoint:

Solving a 10G problem directly requires complexity O(f(P))
Breaking P into N independent 1G subproblems reduces problem complexity to parallelizable O(f(P₁ₖ)) operations
This follows divide and conquer principles, where smaller problems are easier to solve and faster to deploy
The cognitive load on developers is also reduced, leading to higher quality implementations

The practical implication is that 1G solutions reach completion faster not just because they're smaller in scope, but because they're fundamentally less complex to solve. Unlike MVPs, which are incomplete versions of a larger solution, each 1G solution is complete for its specific problem scope.

The Completeness Distinction

It's important to distinguish the 1G approach from MVP (Minimum Viable Product) approaches:

MVP Approach	1G Approach
MVPs are incomplete versions of a large product, designed to test assumptions	1G solutions are complete solutions to small, well-defined problems
MVPs are stepping stones toward a larger goal	1G solutions are valuable endpoints in themselves, even as they can be combined with other 1G solutions

This distinction is crucial because it affects everything from development approach to market strategy to economic viability. A 1G solution can be profitable on its own at a small scale, while an MVP typically requires evolution into a larger product to become financially sustainable.

The combined mathematical evidence from probability theory, game theory, and computational complexity 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: A Call to Rethink Technology Creation

The 1G Tech Model represents a fundamental rethinking of how we create and distribute technology. By breaking down problems, solutions, and business models to their simplest forms, we open vast new opportunities for innovation that serve more diverse needs.

This isn't just about how we price software—it's about who can create technology, which problems get solved, and how solutions develop over time. If proven effective, this approach could transform technology development from an activity dominated by venture-backed companies into a more distributed process that addresses a fuller range of human needs.

The principles are simple:

1

Break down problems to their smallest meaningful components
2

Create focused solutions that solve one specific pain point exceptionally well
3

Build sustainable business models around direct value creation
4

Measure success by impact and profitability, not just scale

The market will ultimately determine whether this principle holds up at scale, but the questions it raises about our current approach to technology are worth considering regardless of the outcome.

In a world increasingly dominated by massive technology platforms, perhaps the path forward isn't bigger, more comprehensive systems—but rather smaller, more focused ones that collectively create greater value by solving specific problems exceptionally well.

The 1g of Tech Model: Value in the Smallest Units