This weekend, amidst the exhilarating backdrop of a homecoming football game at my alma mater—where the Fighting Illini dominantly bested Central Michigan's Chippewas—the profound intersection of history, innovation, and future potential was palpably alive. As each touchdown cannon boomed, memories of technological milestones achieved here—from the Arpanet's first node beneath the Foreign Language Building to the birth of Mosaic that would evolve into Netscape upon Marc Andreessen’s graduation and YouTube—echoed a legacy of innovation. But the game was not merely a nostalgic affair; it also pointed towards the future of technological evolution and the massive potential yet to be tapped.
Imagine an early-stage startup, such as TraceMachina, harnessing AI, which is still in its infancy, to address the nuanced demands regulated by bodies like the FDA and intersecting with various governmental departments—from Agriculture to Energy. TraceMachina, with its NativeLink product, represents not just a business but a gateway to addressing global challenges through high-powered computational solutions initially designed for the space industry. Here lies an arbitrage opportunity unlike any other, suggesting that the era of lean, potent startups—the one or two-person unicorn setups—is not just feasible but imminent.
This vision extends into a staggering proposition: solving persona use cases across 1,000 North American Industry Classification Standard (NAICS) codes could scale up to a $1 trillion market opportunity. This isn't just about high returns; it's about revolutionary impacts, akin to what Amazon Web Services has achieved in cloud computing. The idea of generating a 1,000,000x return on ideas seeded at $1 million value per idea fundamentally shifts our understanding of investment and scale.
But how does a startup to scale such heights? By perfectly balancing the capitalization table of virtual assets. Imagine a world where every asset, from IP to goodwill, aligns perfectly with market needs—debits equal credits in a beautifully stable equilibrium, mirroring the stability of inert gases like hydrogen, which power the stars through fusion, the ultimate renewable energy source. This equilibrium is not just theoretical but a strategic necessity for startups like TraceMachina, ensuring that their growth is as explosive as it is controlled.
Going a step further, here's a breakdown of the product cost structure and weightings:
- Product Engineering (25%)
- Product Management (25%)
- Product Engineering Oversight (12.5%)
- Product Marketing Oversight (12.5%)
- Product Marketing (50%)
Product engineering, management, and marketing should be owned by the Chief Technology Officer (CTO), Chief Financial Officer (CFO), and Chief Marketing Officer (CMO), respectively, to achieve stability.
In the era of AI, in the spirit of the entrepreneurial brilliance of Steve Jobs, Bill Gates, Jeff Bezos, Jack Ma, and Strive Masiyiwa, given their keen product sense, the CTO, CFO, and CMO (with the latter being a Chief Medical Officer in the case of a healthcare company), can effectively be collapsed into one individual, armed with a tier-1 model, referred to as a Brigantine, such as OpenAI's GPT-4 Omni, as opposed to those for tiers 2 and 3 (see Exhibit A for further detail of these classifications).
Meanwhile, in order to achieve perfect harmony with its surroundings, the general ledger for every stable asset in the virtual world should be constructed, as follows:
Debits (DR)
+ Short-Term (50%)
+ Cash (25%)
+ A/R (25%)
+ Subtotal = Market = Product
+ Longterm (50%)
+ IP (25%)
+ Goodwill (25%)
+ Subtotal = Product = Market
+Total Debits (DR) = Credits (CR)
Credits (CR)
- Short-Term (50%)
- Payables (25%)
- Revolving Debt (25%)
- Longterm (50%)
- Equity (50%)
- Total Credits (CR) = Debits (DR)
Balanced Equation = Harmony = Stable Asset = 0
This is the capitalization table for valuing a stable asset, effectively a virtual asset-backed security, akin to the massless elementary particle, a photon, growing exponentially, moving at the speed of light, tightly correlated with the growth of its user base, measured in 3D, unencumbered by the challenges and constraints of the physical world, due to its quantum behaviors, is referred to as Wade’s Law, a proprietary concept owned by the Sir Roy G. Biv foundation for the benefit of all people.
The entries for recording transactions, ergo the exchange of energy measured in kilowatt-hour units, should be in the format that you might find for a renewable energy company organized as a nonprofit foundation like the numerous ones established and operating in the State of California in the United States of America working to provide power to their communities, while working towards the goal of carbon neutrality, a concept known as Community of Choice Aggregation (CCA).
By leveraging these frameworks, the idea for a two-sided marketplace that leverages AI to match user problems with solutions—organized in a setup like Airbnb's platform, visualized in 3D or even printed—promises optimized efficiency and a transformative approach to product-market fit.
And in this grand vision, real estate remains a central pillar. Just as farmland retains its value through its renewable nature, the virtual 'real estate' in cloud infrastructures promises a perpetual yield. This is not just about maintaining value but about a renewable generation of wealth and solutions, much like the renewable energy harvested from farmland. The idea can be thought of as a virtual land bank for generational wealth powered by data, energy in its purest form.
In essence, what TraceMachina and similar ventures propose is nothing short of a paradigm shift in how we view industry, investment, and technological evolution. For startup founders, product leaders, angel investors, venture capitalists, and advisors, this is a call to action. The opportunity to invest in and guide such ventures is not merely an investment opportunity but a chance to be part of shaping the future.
As we continue to celebrate our historic roots in technology and innovation, let us also embrace the limitless potential of the future—where a single idea, like a seed planted in fertile soil, can indeed cover the expanse of an entire field. Here's to building the infrastructures of tomorrow, today, which represents a new form of energy production and power utility that never runs out. Winners do the work!
Exhibit A - Classifications of AI Models
Here’s a thematic guide to help you select the best model, inspired by the successful ship classifications of yesteryear.
Ship Type Classifications Key:
- Brigantine (★★★★★) - Excellent balance of power and speed, best overall performance.
- Frigate (★★★★) - Strong power with good speed, reliable and powerful.
- Hybrid vessel (★★★) - Versatile with balanced performance, adaptable.
- Sloop (★★) - Excellent speed, moderate power, good for quick tasks.
- Galleon (★) - High power, lower speed, less suited for agile tasks but powerful in direct confrontations.
Detailed Recommendations with Ship Type Classifications:
Tier 1 - Brigantine
1. Claude 3.5 Sonnet - Brigantine (★★★★★)
- Consistently high performance across all benchmarks.
- General Performance: ★★★★★
- Code Performance: ★★★★★
2. GPT-4 Omni - Brigantine (★★★★★)
- Slightly lower in some general benchmarks compared to Claude 3.5 Sonnet but still top-tier.
- General Performance: ★★★★★
- Code Performance: ★★★★★
Tier 2 - Frigate
3. Llama 3.1 405B - Frigate (★★★★)
- Strong performance across all benchmarks, particularly in general and code evaluations.
- General Performance: ★★★★
- Code Performance: ★★★★★
4. Llama 3.1 70B - Frigate (★★★★)
- Very good performance, especially in general benchmarks.
- General Performance: ★★★★
- Code Performance: ★★★★
Tier 3
5. GPT-3.5 Turbo - Sloop (★★) / Frigate (★★★★)
- Decent performance, particularly in code benchmarks but mixed in general benchmarks.
- General Performance: ★★
- Code Performance: ★★★★
6. Llama 3.1 8B - Sloop (★★)
- Average performance with better results in general benchmarks than code.
- General Performance: ★★
- Code Performance: ★★
7. Gemma 2 9B IT - Sloop (★★) / Galleon (★)
- Lower performance in both general and code benchmarks.
- General Performance: ★★
- Code Performance: ★
Summary
Top Recommendations:
1. Claude 3.5 Sonnet (Brigantine - ★★★★★)
2. GPT-4 Omni (Brigantine - ★★★★★)
Strong Contenders:
3. Llama 3.1 405B (Frigate - ★★★★)
4. Llama 3.1 70B (Frigate - ★★★★)
Good Options for Specific Tasks:
5. GPT-3.5 Turbo (Sloop - ★★ / Frigate - ★★★★)
Suitable for Less Demanding Tasks:
6. Llama 3.1 8B (Sloop - ★★)
Least Recommended:
7. Gemma 2 9B IT (Sloop - ★★ / Galleon - ★)
This classification system provides a clear, thematic understanding of the models' strengths and weaknesses, helping you choose the most suitable model for your needs, just as a sea captain from yesteryear would select the best ship to retrieve the prize.
Copyright ©️ 2024 Sir Roy G. Biv
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