Something new
This document is a draft of the vision and strategy for a company I’m thinking of creating. It’s a work in progress and will be updated as I refine my thoughts.
Vision
Slowly, then abruptly, the internet has become a monoculture – an environment where the rich experiences of discovering valuable insights are drowned out by attention-farming websites peddling cheap, instant AI-generated content.
Many may accept a world where a handful of social media dominate daily life.
I don’t.
But I love the web.
Almost everything I love today—computers, design, writing—I discovered through the many pages and extraordinary people that found their way to my web browser. All I needed was curiosity.
But now, we, the architects of the modern web—designers, engineers, product managers, data scientists—are meticulously creating so-called ecosystems. In reality, these are highly concentrated and controlled environments seeking the Tyranny of the Marginal User.
Today, we’ve strayed far from the initial vision of linking documents to share knowledge. It’s now nearly impossible to seek information without being bombarded by requests to buy, like, or follow—all while being incessantly tracked.
Imagine a web that empowers rather than commoditizes people.
I’m not saying these enshittificating platforms are inherently bad. But we deserve better.
How?
Social networks understand a key dilemma: content is growing exponentially, but user attention remains finite. The result? A glut of content with no meaningful destination.
Their solution? Filter content by analyzing your behavior and your friends (now just people you follow) to infer your interests—an excellent approach to maximize engagement.
But this isn’t what many of us want. We, the curious and truth-seeking, know that true discovery comes from learning fundamentals, encountering contrarian beliefs, and engaging with opinionated experts—all in a low-dopamine format that prioritizes substance over sensationalism.
In a world where algorithms prioritize engagement over enlightenment, we’re cultivating a community of curious minds who believe in the power of information—when it’s properly distilled and thoughtfully presented.
We’re creating a space for focus.
We believe in a data-driven future where AI progress is presented in this low-dopamine, high-substance format transforming how professionals engage with and understand the data world.
This is our journey to rewild the internet.
Strategy
Defining what’s a good strategy
At its core, strategy is the unique way of sustainable value creation. Strategy’s main purpose lies in value proposition— the underlying logic behind creating and capturing value.
Planning is not a strategy. Planning is, by definition, the method to execute the strategy.
In this document, we will talk about strategy (i.e. setting the goals) rather than planning (i.e. how to achieve them).
Although a company’s strategy should be stable over time, the strategy changes as we build and learn. However, all good strategies have at least these properties:
- All good strategies are a set of bets that you believe will allow you to win (achieve your vision).
- All good strategies pass the “can’t-won’t test” so competitors can’t copy it without sacrificing their existing business.
- All good strategies define the market (problems) and your playing field’s constraints.
- All good strategies explain what competencies, resources, and systems you need.
- All good strategies are reviewed periodically (i.e. quarterly), at the definition of the Objectives and Key Results (OKRs).
Our Strategy
Our mission is to create a comprehensive, curated mapping of the data landscape, providing high-quality, distilled information in a low-dopamine format.
We aim to become the go-to platform for professionals, entrepreneurs, investors, and organizations seeking to navigate the rapidly evolving world of data.
This strategy will be revised periodically, but it’s in the 10-year time horizon.
Our bets
- Following data-related progress will be harder each year
- Specialized expertise is required to distinguish signal from noise while top experts focus on innovation rather than market mapping.
- AI development will move towards numerous specialized systems rather than a single AGI solution.
- Many AI entrepreneurs lack proper market research, leading to redundant efforts.
- VC-backed startups and tech-first corporations will chase bigger markets
- Major tech companies (e.g., Google, Meta) are prioritizing AGI development, leveraging existing data assets for potentially revolutionary impact.
- Other large tech firms (e.g., JPMorgan, Figma) focus on AI integration for business optimization.
- VC-backed startups target larger markets for quicker, more lucrative exits. They aim for acquisitions or IPOs within 4-6 years and often focus on vertical-specific AI solutions in B2B markets, resulting in slower sales cycles.
- Non-Tech Corporations and Government Involvement
- Large non-tech corporations feel pressure to enter AI markets, driven by FOMO (Fear of Missing Out) on disruptive innovations. They often lack the necessary technical skills for high-quality development.
- Governments, especially in LATAM, struggle with AI strategy development. They have situational awareness but lack technical expertise. Best technical talent often resides in the private sector of other countries.
- Focusing on the data mapping market offers long-term stability
- Data is cross-industry, providing insulation from vertical-specific downturns.
- Developing expertise in mapping the data landscape facilitates the identification of valuable opportunities and builds a network for future product distribution.
Follow-up
This document is a draft and will be updated as I refine my thoughts. The next steps are to:
- List all the assumptions behind the bets.
- List all the contrarian (right) beliefs about the data landscape to be mapped, and how to communicate them (i.e. minto + data-value pyramid).
- Define the North Star Playbook to measure the success of the strategy.
- Define capabilities for an early MVP.