For raising future-ready, fully human kids in a machine-run world.
1. We Do Not Raise Workers. We Raise Creators.
Jobs are automated. Originality isn’t. We raise kids to build, make, explore, and ship—starting now. If they can imagine it, they can prototype it.
2. We Teach Them to Think From Zero.
No parroting. No copy-paste minds. We raise first-principles thinkers who can dismantle and reassemble reality from scratch. Knowledge is everywhere—clarity is rare.
3. Communication is the Key Currency.
Machines calculate. Humans connect. We raise kids to speak clearly, write persuasively, and move people with words. Storytelling is leverage.
4. Emotion is Not a Weakness. It’s the Superpower.
We help them name feelings, navigate complexity, and lead with empathy. Emotional fluency will beat IQ.
5. We Expose Them to Chaos, Not Protect Them From It.
Discomfort is the gym of life. We don’t overplan. We don’t rescue. We don’t edit their path. They get to stumble, recover, and rise.
6. We Focus on Portfolios, Not Report Cards.
Grades are old-world currency. Proof-of-work is the new resume. What did you build, write, change, or learn this month?
7. We Teach the Language of the Machines.
Our kids won’t be passive users—they’ll be fluent operators. Code. Logic. Systems. Prompts. AI agents. They command tools, not just swipe screens.
8. Nature, Music, and Silence Are Sacred.
In a noisy, digital world, attention is oxygen. We protect their capacity for awe, stillness, and self-reflection. They must be able to hear themselves think.
9. We Raise Rebels With a Cause.
We don’t raise obedient citizens—we raise ethical disruptors. They speak up. They critique. They imagine alternatives. The future is built by system-benders.
10. We Walk the Path With Them.
We don’t pretend to know everything. We co-learn, co-build, co-fail, and co-reflect. Growth is not delegated.
11. We Teach Them Agency and Taste.
Agency: They take initiative. Solve their own problems. Drive their own projects. Taste: They know what’s good—music, writing, design, ethics. They develop discernment. We give them room to choose, build, refine, and develop standards. Because the ability to act with direction and to know what’s worth building—that’s the edge.
We are not preparing our children for our world. We are preparing them to lead theirs.
Why Now Is the Time to Raise Creators, Not Workers
I’ve been thinking a lot lately about what kind of world my daughters will live in when they’re my age. The short version? It won’t be the world I grew up in. Not even close. But that is not a surprise for you innit?
Information is instant. Answers are automatic. Machines calculate better than any human ever could.
AI agents are not just “coming” — they are already here, orchestrating work at speeds and scales we can’t yet fully grasp. So what does that mean for our kids?
It means memorizing facts is useless. It means passing tests is irrelevant. It means following instructions like a good soldier is the fastest route to becoming obsolete. My kids speak five languages bilingually, but still fail english tests at primary school because of the convoluted grammar and pre-conceived scorecards. (Got that, Germany?) – Exactly the same way when I was a college student: My classmates who did not know how a router worked would pass “Foundations of computer networks”, and me, doing taking my journey on white hat hacking would not. Tests are made to conform. The future is for the non-conformists.
The new currency isn’t knowledge. It’s creativity, discernment and agency. The Future Belongs to fucking Builders.
For the past few months, I’ve been training my 8- and 10-year-old daughters — not to be “students,” not even just to be “coders” (to hell with the 2010’s craze on ‘get your kids to code’) — but to be builders. Makers of things. Designers of systems. Explorers of problems. Leaders of their own ideas.
One of them is designing a Google Maps–enhanced virtual reality app, layering street data with interactive storytelling. The other is creating a collection of kid-designed table games and trading cards to teach about the animal kingdom — and iterating based on early user feedback from family and friends. They are also creating comics and stories where they job is to curate, validate and distribute. They created the idea. Iterated over it and designed it.
Both have access to an army of coders, designers, AI assistants (and yes, some heavy-duty Dad help when needed to show them what’s possible and push them when they got lazy). But they are the CEOs of their projects.
They brainstorm. They prototype. They pitch. They test with users. They revise. They’re 8 and 10 and they already think more like founders and product managers than most MBA students I know. Sure, there’s a lot of father bias here, but you get the idea.
Why I’m Sharing This? I’m not telling you this to brag. (OK, maybe a little. They’re amazing.) I’m telling you because every kid deserves this chance to do this and now they can. And every parent, regardless of your location or income, has the power to make it happen. But only if we change how we think about education.
The Old Way is Dead. Here’s what our kids really need instead:
First Principles Thinking Teach them not to memorize answers, but to break problems down to their atomic parts and rebuild from scratch.
Creative Confidence Let them build bad prototypes. Let them draw weird ideas. Let them invent solutions nobody asked for. Praise the process, not the polish.
Communication as a Superpower Help them sell their ideas — not just with flashy slides, but with ethos, pathos, and logos. Can they explain why it matters? Can they make you care?
Emotional Mastery Teach them that emotions aren’t obstacles — they’re fuel. Joy, fear, pride, frustration — all part of the journey.
Resilience in Chaos Throw curveballs. Change plans. Let things break. Model how to adapt. Protecting them from discomfort is the real danger.
Building Proof, Not Chasing Praise No more chasing grades. No more empty certificates. “Show me what you made this month” — that’s the metric.
Machine Literacy They must understand code, AI, systems. Not to become robots themselves — but so robots serve them.
Sacred Stillness Make time for awe. Forests. Music. Silence. Not every minute needs a screen. Not every thought needs a comment.
Ethical Rebellion Raise rebels. Kids who challenge dumb rules. Who redesign broken systems. Who stand for something bigger than themselves.
Walking Beside Them Model it. Build with them. Fail with them. Reflect with them. Grow with them. This is not outsourced work. This is the path.
Why This Matters Now More Than Ever
Because if we don’t, someone else will shape their minds. The algorithms. The noise. The systems optimized for compliance, not creativity. You are either raising your child to be an architect of the future, or you are letting someone else use them as a cog in their machine. There is no neutral ground anymore.
What It Looks Like (and Feels Like)
This isn’t about building perfect apps or winning science fairs. It’s about how building things rewires them:
They face uncertainty — and instead of freezing, they ask better questions. They get feedback — and instead of crumbling, they adapt and improve. They hit dead ends — and instead of quitting, they pivot. They imagine something new — and believe, deep down, that they can build it.
You should see their faces when an early prototype works. Even if it’s glitchy. Even if it’s messy. The spark is unmistakable: “I made this real.”. This is the very reason I became a software developer. Because I could create worlds. Or why I play guitar. I can create and distribute emotions.
How You Can Start Today
Let them choose a real project. It could be anything. An app. A comic book. A scavenger hunt app. A Minecraft city. A new cookie recipe. Something they like and does not feel like “homework”. Make sure it is not your project or what you like. It does not matter what it is.
Help them frame it around a real user. “Who is this for?”, “What problem does it solve?”, “Would someone buy this, and if so, for how much?”, “How do you imagine yourself using it?”.
Give them superpowers (tools). Introduce them to kid-friendly tech: Scratch for coding. Canva for design. ChatGPT for brainstorming, ideas, storytelling. Replit, Bolt or lovable for coding agents. Kling or Runway for video. And monitor the technology because that list will be obsolete yesterday. Basically, give them shovels. Tons of them. But most importantly: show them how to use that. You have to become an inventor yourself as well.
Set a short feedback loop. Early, messy feedback is gold. Family members and friends = first test users. Let them share with their classmates and teachers. Impromptu presentations in the neighbourhood. Let them get used to presenting and to rejection.
Celebrate progress, not polish. A glitchy game, a half-finished map, a messy board game — that’s proof of learning in action. Bugs are just steps in the way.
Track XP, not grades. In our house, we track weekly “Creativity XP,” “Courage XP,” “Thinking XP.” Levels up. (And sometimes you get to design your own badge.) (Yep, here’s a old bearded d&d fan). I don’t fucking care about your grades.
Reflect Together. Every Sunday (or whenever you have family time), we do a “Weekly Mission Debrief.” What did you make? What surprised you? What’s one thing you want to explore next? Reflection is what cements the learning.
Final Thoughts
I don’t know what the job market will look like in 2040. Nobody does. Nobody can even imagine it. But I know this: The kids who can imagine something new, build it, test it, sell it, adapt it, and lead others through uncertainty — those kids will NEVER be irrelevant.
They won’t just survive the future. They’ll shape it.
That’s why we train now. That’s why we build now. That’s why we walk the path with them. Not just for their future. For ours.
(PS: If you want the templates, games, scorecards, and activities we use at home for this, DM me. Happy to share.)
Agentic AI is now everywhere and virtually every company wants to implement and use them. The rise of the digital colleague is slowly becoming a reality. GenerativeAI told the world that for these systems to work they need access to good, curated, audited data with proper lineage. So the companies that dedicated the last decade to “Big Data” built data pipelines—the intricate processes that ingest, cleanse, transform, and serve massive streams of information. But there’s a critical ingredient that has been flying under the radar for agents: skills. Just as data fuels decision-making, skills are the engine that drives intelligent actions. In our era of autonomous agents, a parallel “Skills Pipeline” is essential. It’s not enough to have data; our agents need a well-organized, continuously updated repository of expertise—a marketplace of skills—to truly execute complex tasks.
Why We Need a Skills Pipeline
Data is now Agentic Data
We’ve seen how agentic processes now live in every part of the data flow—from data ingestion, where AI agents help gather and pre-process raw inputs, to transformation, where they refine and reassemble information. ETL processes would rely on extensive rules that are difficult to maintain and support, while agents today can make decisions on low-risk environments and build those pipelines on the go.
But at the end of the pipeline, traditionally, the serving phase was dominated by machine learning, analytics, and even reverse ETL. The fourth leg will be agentic data. The way agents consume data is different from traditional machine learning models, and arguably it is still under development. Whether it is streaming APIs, discoverability or new standards such as the ones being proposed now (like MCP). And the bigger leg of our future isn’t just serving data—it’s serving skills. Without access to a robust skillset, even the best data-driven insights will fall flat.
Skills as the New Currency
Think of skills as the intellectual capital that agentic AI relies on and the ability to use that capital. They can come from different vendors, be developed internally, or sourced from global expertise—mirroring the way data flows freely from various origins.
Your company can own Terabytes of data but not have the capacity to use it properly or in a beneficial way in the same way that an investor could have access to huge amounts of capital but make terrible investment decisions. Imagine taking a random sample of humans from different geographies, experience and job profiles and show them a comprehensive financial statement and risk report of a given company. Most of those individuals will be able to read the statement and make sense of the general topic of the statement. Most people will understand basic concepts such as revenue or profit. But only a handful of them with specific training and experience in finance or accounting will be able to extract real insights from that report. Agents have the same problem. We develop agentic systems with “sensors” or access to the real world. Either with cameras and microphones or with access to vast number of datasets including the whole internet. We give them the ability to read extremely fast, understand structured data such as tables or even create quick programs to perform calculations. But the ability to truly understand the meaning behind the data needs to be taught.
Let me try to explain what I mean by this. Here is a bunch of text:
I am sure most of you will be able to read it. For the most part it uses well recognized characters (english or greek) and relatively basic arithmetics. Those who did math in high school will probably recognize vectors in the shape of a little arrow on top of letters, etc. You can read the characters. But it is likely that only a few of you (physicists mainly) will truly understand what this is (The Schrödinger-Pauli Hamiltonian full formulation). You have the data, the bits are there. Now, what is that and what do I use it for… is a completely different story.
Here is another example a bit less radical:
Most of you will identify this as a profit and loss statement. And the terms are relatively self-explanatory. Revenue, cost and expenses, net loss, dividends, etc… However only some of you may identify real insights from this. What type of accounting do they do? Is there any explanation to the increase of operating costs on G&A detached from the revenue growth?, Where does the gain from liabilities extinguished come from? Everyone can read this and get a general sense of what is going on (the company is making or losing money). But only those trained in the art of finance and accounting can truly extract insights from these documents.
These are the types of skills agents need. They may be intellectual property of a given company (internal models on how to ‘understand’ information for example), same as the data itself. They may be public knowledge, or they may be sold in some kind of external or internal marketplace, where organizations can buy, sell, and exchange specialized expertise just as they do with data. However, this marketplace won’t work on its own; it must have a systematic process to ingest, normalize, transform, and serve these skills to our intelligent agents.
In short, agentic AI systems need both high-quality data and robust skillsets. Data tells the agents what to do, while skills provide the know-how to act intelligently. Without a parallel skills pipeline, agents will be forced to work with subpar or fragmented instructions, severely limiting their performance. Companies must therefore invest in building pipelines that treat skills with the same rigor as data—ensuring both are standardized and readily accessible.
Architecting the Skills Pipeline
Imagine a “big data pipeline” equivalent—but for agentic skills. Data engineering pipelines are typically dividing between ingestion, transformation and serving of the data. Data usually comes from sources (whether it is internal systems as an ERP or CRM or external such as APIs or IoT devices). They need to be transformed and normalized using ETL or ELT systems and finally distributed and served for data analytics or machine learning systems. The industry of “Big Data” has perfected this pipeline with many different options and allowed the world to serve huge amounts of distributed information. From big batches of data to streaming capabilities.
Agents are already providing support in each phase of the pipeline (for example with data normalization, or web scraping), but they are also becoming one of the biggest consumers of the distribution end of the flow. While in the psat we had Analytics & dashboards and Machine Learning as its biggest consumers, not agents will be one of the main drivers of data.
One of the biggest challenges companies face to leverage LLMs is that they don’t have a comprehensive data strategy with the right governance and process (in many cases they won’t even have a Data Warehouse, Data Lake or any other system in place). So, they can’t feed their systems properly and securely with company information. This is the first challenge companies need to do to move from a proof of concept to production-grade AI deployments. Go back to the basics and do their homework.
Any company agent or digital colleague, whether developed in house or purchased or licensed will need a robust data access to the company system and with the right governance. In the same way that a manager or C-level executive has access to BI reports and dashboards, an agent will have access to certain systems. For example: a customer support agent should have full access to all the information about clients, previous interactions, product manuals and releases, company policies, etc. And be able to log in the output back into the system as in a “Reverse ETL” solution.
A new data engineering pipeline for AI agent integration must distinguish between how agents access data, how they acquire skills, and how they communicate. For data access, agents require a standardized ingestion framework that connects to diverse data sources. This includes structured databases, APIs, proprietary tools, and real-time event streams. Interoperability frameworks like Anthropic’s Model Context Protocol (MCP) or Langchain Agent Protocol are first steps to get seamless integration across different sources, enabling agents to query and retrieve the most relevant information dynamically. Additionally, standardized schemas and knowledge graphs allow agents to contextualize raw data into meaningful insights, ensuring consistency across multiple sources.
Skill acquisition, on the other hand, requires a distinct pipeline that mirrors data engineering best practices. Agents must ingest skills from multiple sources, including internal documentation, vendor APIs, training modules, and expert contributions. These raw inputs are transformed using semantic parsing and tagging techniques, ensuring alignment with a unified skills taxonomy. By normalizing skill descriptions—such as equating “negotiation” with “conflict resolution”—AI systems can ensure interoperability across different providers. This structured repository acts as a skills data lake, providing a scalable and queryable interface where agents can dynamically fetch, compare, or even update their abilities in real-time via API gateways. Skills will also be licensed and will need to be metered and evaluated. In the same way we sell data feeds today, we will be selling learnable skills.
Once skills and data are integrated, intra-agent communication becomes the key to operationalizing these capabilities. AI agents need a standardized agent communication language or emerging open protocols to interact efficiently. This includes requesting expertise from other agents, sharing context-aware information, and collaborating in multi-step workflows. New frameworks like P3AI are appearin to bring agent-to-agent interoperability, enabling agents to transfer skills, distribute tasks, and leverage specialized capabilities across distributed AI ecosystems. Such architectures prevent siloed models, ensuring AI systems can work as modular, adaptable components in a broader computational network.
Finally, the integration of these layers into agentic workflows ensures that AI agents can execute tasks dynamically. When an agent receives an objective—such as optimizing a supply chain or generating a compliance report—it queries both data and skill pipelines to determine the optimal execution path. By using feedback loops and performance-based learning, the system continuously refines both skill acquisition and data interpretation, ensuring the agent adapts to new business challenges. Ultimately, this approach establishes a scalable, evolving AI ecosystem where agents seamlessly ingest data, acquire new skills, and communicate effectively to drive autonomous decision-making.
Community Efforts and Early Days of Interoperability
The good news is that the community is already working on interoperability standards for agentic systems. Several initiatives are emerging to tackle the challenge:
1. LangChain with Agent Protocol
LangChain is a prominent framework for building LLM applications, with its Agent Protocol announced in November 2024 as a common interface for agent communication. It includes APIs for task execution, threads in multi-turn interactions, and storage for long-term memory, aiming to standardize interactions across frameworks.
Pros:
Offers a standardized Agent Protocol, making it highly interoperable with other agent systems.
Framework-agnostic, allowing developers using LangGraph, AutoGen, or custom frameworks to implement it.
Provides integration guides for connecting with other frameworks like AutoGen and CrewAI, enhancing ecosystem compatibility.
Includes LangGraph Studio for local development and debugging, facilitating practical implementation and an interface for pipeline creation.
Cons:
The Agent Protocol is relatively new, potentially leading to complexity for users unfamiliar with its concepts.
Adoption is still evolving, which may limit immediate community support and mature integrations.
2. AutoGen
AutoGen, developed by Microsoft, is an open-source framework for building multi-agent conversational systems, updated in January 2025 to include cross-language interoperability. It supports Python and .NET, with plans for additional languages, and focuses on agent collaboration and observability.
Pros:
Cross-language interoperability is a key feature, enabling agents built in different programming languages to communicate, aligning with MCP’s goal of system integration.
Offers a flexible architecture for multi-agent systems, with tools for observability and control, enhancing developer experience.
Supports diverse conversation patterns, making it suitable for complex workflows.
Cons:
May be perceived as more aligned with Microsoft’s ecosystem, potentially limiting its appeal for users outside this environment.
Setup and configuration can be complex for beginners, especially for cross-language implementations.
3. CrewAI
CrewAI is an open-source Python framework for orchestrating role-playing, autonomous AI agents, emphasizing team collaboration. It allows defining agents with specific roles, goals, and tools, and supports task delegation.
Pros:
Role-based architecture simplifies defining agent responsibilities, making it intuitive for building multi-agent teams.
Supports task delegation between agents, enhancing internal collaboration efficiency.
Can integrate easily with external tools as well as langchain tools and knowledge sources, providing some level of external system interaction.
An enterprise version provides drag & drop / no-code capabilities.
Cons:
Lacks explicit interoperability protocols, focusing more on internal orchestration than broad system integration.
May not match MCP’s level of standardization for connecting to external data sources.
4. Swarm
Swarm, an experimental framework by OpenAI, is a lightweight multi-agent orchestration tool launched in October 2024. It focuses on simplicity, with features like dynamic task handoffs and integration with other tools.
Pros:
Lightweight and easy to use, making it accessible for developers experimenting with multi-agent systems.
Interoperable with other tools and frameworks, such as LangChain and Anthropic, enhancing ecosystem compatibility.
Experimental status limits its production readiness, with potential for instability.
Limited documentation and community support compared to established frameworks like LangChain.
5. AWS Multi-Agent Orchestrator
Introduced by AWS, this framework is designed for managing multiple AI agents and handling complex conversations, with dual language support (Python and TypeScript) and extensible architecture.
Pros:
Supports multiple programming languages, enhancing flexibility for developers.
Extensible architecture allows integrating new agents or customizing existing ones, supporting interoperability.
Universal deployment options, including AWS Lambda, local environments, and other cloud platforms, make it versatile.
Cons:
May be tied to AWS services, potentially limiting its appeal for users outside the AWS ecosystem.
Relatively new, with less community adoption compared to LangChain or AutoGen as of March 2025.
6. Model Context Protocol (MCP) by Anthropic
MCP, announced by Anthropic in November 2024, is an open standard for connecting AI assistants to external systems, such as content repositories and business tools, to enhance context-awareness.
Pros:
Open standard promotes widespread adoption, encouraging community and industry buy-in.
Specifically designed for connecting AI to diverse data sources, improving response quality.
Backed by Anthropic, a leader in AI research, enhancing credibility.
Cons:
Relatively new, which may result in limited initial adoption and implementation by other frameworks.
Requires other systems to adopt the protocol for full interoperability, potentially slowing progress.
Comparative Analysis
Framework
Focus Area
Interoperability Level
Deployment Flexibility
Maturity Level
LangChain with Agent Protocol
Standardization, Communication
High (Protocol-based)
Moderate (Studio, Platform)
Evolving (New Protocol)
AutoGen
Multi-Agent, Cross-Language
High (Cross-Language)
Moderate (Local, Cloud)
Established (Updated 2025)
CrewAI
Role-Based, Collaboration
Moderate (Tool Integration)
Low (Python Focus)
Established (Open Source)
Swarm
Lightweight, Experimental
Moderate (Tool Integration)
Low (Experimental)
Experimental (2024)
AWS Multi-Agent Orchestrator
Extensible, Multi-Language
Moderate (Extensible)
High (Universal)
New (2024)
MCP by Anthropic
Data Source Connection
High (Protocol-based)
High (Open Standard)
New (2024)
LangChain and AutoGen offer high interoperability, aligning closely with MCP, while CrewAI and Swarm are more internally focused but still offer tool integration. AWS Multi-Agent Orchestrator and MCP emphasize flexibility and standardization, respectively.
Implications and Future Directions
The landscape of AI agent orchestration and interoperability is rapidly evolving, with frameworks like LangChain’s Agent Protocol and AutoGen’s cross-language support pushing the boundaries of what is possible. However, challenges remain, such as the experimental nature of Swarm and the newness of MCP, which may affect adoption rates. Future research should focus on standardizing protocols across frameworks to enhance interoperability, potentially leading to an “Internet of Agents” as envisioned by initiatives like AGNTCY, involving LangChain and Cisco. As well as Intel’s open source “Open Platform for Enterprise AI”. Additionally, vertical and niche frameworks can excel at specific tasks (for example: Bolt, Lovable and v0 for coding and many others for design and protoyping, legal agents, etc.).
For practitioners, selecting a framework depends on specific needs: LangChain and AutoGen for broad interoperability, CrewAI for role-based teams, and AWS Multi-Agent Orchestrator for AWS-centric deployments. MCP’s open standard could become a benchmark, but its success depends on community adoption. The ecosystem evolves rapidly. It is expected that new entrants will enter the space, as well as new frameworks provided by hyperscalers. There is not yet a globally accepted standard for agent intra communication and skill usage or data acquisition but it is likely to come eventually.
Challenges and Future Directions
Interoperability is complex. As we build these pipelines, we must navigate:
Diverse Data Formats: Skills come in various forms—text documents, video tutorials, certifications, and even informal knowledge-sharing platforms. Normalizing this heterogeneous input is nontrivial.
Evolving Standards: With frameworks like MCP still in their infancy, early adopters will need to remain agile and ready to adjust as standards mature.
Security and Privacy: Just as with data, ensuring that proprietary or sensitive skills (especially those developed internally) are securely managed is critical.
Looking ahead, the integration of a robust skills pipeline will not only empower AI agents but also democratize access to expertise, fostering a competitive internal marketplace where companies can choose the best skill sets available.
Conclusion
In the coming era of agentic AI, data pipelines are only half the story. For our intelligent agents to truly revolutionize how we work, they must be armed with both the raw data and the sophisticated skills required to process that data effectively. A parallel “Skills Pipeline” is essential—a system that ingests, normalizes, transforms, and serves skills from diverse sources, creating an internal marketplace of expertise.
While community efforts, such as the MCP initiatives and open API projects, are promising, interoperability in this space is still in its early days. The journey ahead will require continuous collaboration between developers, vendors, and industry bodies to establish robust standards. Companies need to thing in two parallel lines now: a data pipeline and a skill pipeline. Both involve proprietary and 3rd party intellectual property, complex interconnections and interoperability and one feeds the other.
It’s time to rethink our digital infrastructure. The future isn’t just about having more data—it’s about having the right skills, available on demand, to harness that data for intelligent, autonomous decision-making. Let’s build a world where our AI agents not only know what to do but know how to do it, powered by a dynamic, integrated skills pipeline.
First and foremost: I’m not a professional investor. I’m what some call a “business angel” – I give some money to entrepreneurs, usually for them to burn quickly and get nothing in return 🙂 But in the meantime, I learn a lot from the founders and their businesses, sometimes I teach them a thing or two from my experience, and we have a lot of fun together. So, take this with a pinch of salt.
I’ve reviewed all the “passes” I gave from the last two years on qualified deal flow. That is, projects that passed the triage and where I had at least two meetings with the founders and team. And I realized that over 75% of rejections were due to one main reason: the founding team. So, I figured I would write this to use as a reference for the next project I give a pass on.
These are the things I typically see:
– Lack of a Strong CTO Co-founder: I typically invest in tech-driven companies. If you don’t have a strong CTO co-founder, I will have a lot of doubts. Note: Giving that person 2% equity is not a co-founder. Every case is different, but generally, I like to see how the tech person is as involved as the business one. Note that strong does not mean necessarily with tons of experience. Strong in terms of commitment!
– A swarm of Cheerleaders and Butterflies: Startups tend to over-scale on people at the beginning, and some founders don’t realize they don’t need that much people (or advisors) but that they need to wear 20 hats at the same time. Sometimes, nice projects or convincing founders attract a lot of these. Early cheerleaders, advisors, and supporters take money, time, equity, or all three, but not always pull their weight. Beware of them specially if they don’t put money in. I have seen many companies going down the drain because of this issue.
– You don’t need a head of sales: As a CEO, you are the main sales rep, the marketing one, and ideally, the one talking to customers as well. You are trying to get to your Product-Market Fit (PMF). You won’t achieve that by delegating client touch. If you (and your founding team) are not out there selling your stuff, you got a problem.
In short. I like it when I see a founding team of pigs and no chicken in the fable that you can find in my book “The CTO Toolbox” (And that you can find here: https://lnkd.in/eBZzQrGv Sorry for the shameless promotion 🙂 ).
