If you know nothing about startups, this guide will show you what real startup problems look like.

If you have a technical background, you will find that 70% of this has nothing to do with code. That is exactly why so many technical founders crash.

01 | Product Is Only the First Step

Almost no company on earth is truly necessary.

The engine of human innovation has not been “only I can do this” for a long time. It is: I want to do this, and I hope other people will pay me for it.

Admit that, and you will be less likely to fool yourself with your own “sense of mission.”

You are not saving the world. You are trying to persuade a group of people who never asked for you to exist to hand you their money.

Every company is basically carrying three things at the same time:

  • Labor — the actual, repetitive, unsexy work. Writing code, debugging, running evals, cleaning dirty data, writing docs, doing ops, clearing tickets, replying to customer emails.
  • Administration — dealing with people. Teammates, cofounders, investors, compliance, hiring, customer success, the board.
  • Deception (the philosophical essence of marketing) — convincing someone who never asked for you to give you money. Fundraising pitches, Product Hunt, Twitter marketing, PR, recruiting scripts.

Technical people only want to do the first one 99% of the time.

That is why their companies often do not die because of the product. They die because of the other two.

Before you place your bet, have an honest conversation with yourself: among these three things, which one are you most willing to do? Which one do you hate most?

The real question is not “Can I build the product?”

It is: do I actually believe in its value, and am I willing to sell it?

02 | Motivation Matters More Than Method — Do Not Start Up Just to Look Cool

Every generation of young people has its own dream of becoming famous. In tech, this generation’s version is: build an AI Agent, launch an MCP server, make a developer tool, build an LLM-native SaaS, create an AI companion, or build a Devin competitor.

The problem is not that these directions are wrong. The problem is that the most dangerous engineering culture of this generation is this: you do not need to build something truly usable to look like you are building a startup. You only need to post a demo video, beat a benchmark, and gather some Twitter traffic, and people will call you an “AI Founder.”

The industry is messy right now. That is why products that actually land are getting harder to build: you are competing for the same users and the same investors against people who treat “looking busy” as “doing the work.”

You do not need to find the method immediately, but you must first find a thesis worth betting on: a contrarian judgment you are willing to bet is right.

A good thesis looks roughly like this:

  • “All AI coding tools today optimize for the solo experience, but 90% of production code is written through team collaboration. No one is seriously working on that gap.”
  • “Prompt engineering is not a long-term skill. Context engineering is. The people building tooling around context will win.”
  • “Agent frameworks are all chasing multi-agent orchestration, but 90% of the real value comes from one agent doing one thing extremely well. The market will flip.”
  • “The Chinese AI developer community is badly underestimated because big model companies are all chasing English ARR. There is a window here.”

If you cannot state your thesis, you are not building a business. You are performing. Your thesis should be sharp enough that a smart person can publicly disagree with you. If no one can disagree with it, you are just describing obvious nonsense everyone already accepts.

03 | Innovation Means Digging Into Needs Existing Products Do Not Meet

Valuable innovation is not about doing the opposite. It is about seeing which need the current solution still fails to solve.

Software products usually run into a few familiar constraints:

  • Too slow: users cannot wait.
  • Too expensive: the unit economics do not work.
  • Too hard to use: only experts can use it.
  • Too fragmented: the workflow breaks across different tools.
  • Unreliable: results are not reproducible, and when something goes wrong, no one dares to take responsibility.
  • Too dependent on manual work: once scale grows, the only answer is adding more people.

Your innovation should ideally land in one of these places.

Not: “Everyone else is building SaaS, so I will build a command-line tool.”

But: the target user works in the terminal every day. Forcing them to open a web page interrupts the workflow, so a CLI is the lower-friction entry point.

Not: “Everyone else is building multi-agent, so I will build single-agent.”

But: what users actually need is one task finishing reliably, not watching five agents chat with each other on the screen.

Not: “Everyone else is chasing public traffic, so I will build a private community.”

But: your product needs high trust and dense feedback. Fifty deep users are closer to real demand than 5,000 spectators.

Being contrarian has no value by itself.

Solving a real need does.

04 | Clarify the Vision and Mission First

Many software companies rush to write a slogan at the start.

“AI-powered X for Y.”

“The future of Z, today.”

“Empowering teams to…”

The biggest problem with these lines is not that they are tacky. It is that they are useless. They do not tell users what kind of world you are trying to create, and they do not tell the team how to make tradeoffs every day.

What a software company needs to clarify first is not whether the slogan sounds nice, but two things:

  • Vision: what you believe the future will become.
  • Mission: the concrete way you plan to push it there.

The team needs to know where the future is headed. Investors need to know which market you are aiming at. Users need to know what value you can actually provide.

For an early-stage software company, Vision and Mission do not need to sound grand, but they must be grounded.

That is the starting point of a startup with real potential.

05 | The First Product Is a Proof of Concept, Not the Ideal Product

Most people start by putting all their chips on “the perfect version in my head.” That is wrong.

The purpose of the first product is not to make money. It is to test whether your shaky core assumption holds up at all.

Proof of concept does not mean artificially making things harder for yourself.

That is not how software is validated. You do not need to touch the most complex enterprise workflow on day one, and you do not need to find the hardest people to please just to prove yourself.

A more realistic approach is to compress your core assumption into something the market can see.

For example, if you want to build an AI image tool, the first step may not be writing the full product. You can use existing image generation models to create the target effect, organize the outputs into a set of images, and distribute them on platforms like Xiaohongshu, Zhihu, Twitter, Hacker News, and Product Hunt.

What you need to watch is:

  • Does anyone save it, share it, or ask, “How do I use this?”
  • Is anyone willing to leave an email, join a waitlist, or enter a group?
  • Does anyone ask about pricing, APIs, or whether it can integrate with their workflow?
  • Is anyone willing to pay for or try a still-rough version?

That is proof of concept.

It is not about proving you can build the full product. It is about proving the idea gets a reaction in the real attention market.

If a set of images, a demo clip, a landing page, or a handwritten email cannot trigger any feedback, do not rush to write code. What you need to validate is not your engineering ability. It is whether demand exists.

The first product should be a minimal test instrument: put your core assumption into the real world at the lowest possible cost and see whether it sparks any reaction.

06 | The Four-Legged Chair

A three-legged chair can be the highest-quality chair in the world, but it is still a three-legged chair. No one wants to sit on it.

Do not build a “seemingly perfect business.” Add another leg so that when one breaks, you do not fall over.

This applies not only to companies, but to every product.

The first product is a proof of concept. The second and third products are too: you need to keep checking whether you are living on only one assumption, one channel, one type of user, or one cost structure.

Tech and AI founders are especially prone to building three-legged chairs. The common disease looks like this:

  • Single-model dependency: the entire product is built on one OpenAI or Anthropic model. They change pricing, policy, or rate limits, and your whole product dies.
  • Single channel: all traffic is bet on one Product Hunt / Twitter / Hacker News launch. If it fails, you go to zero.
  • Single customer type: you serve only individual developers or only large enterprises, with no room to maneuver in between.
  • Single revenue source: only subscriptions, only API usage, or only one-time purchases.
  • Single identity: you are only a “founder.” You do not have a second identity to lean on, like “content creator,” “open-source maintainer,” or “consultant.”

Adding another leg is not a distraction. It is how you stay alive until next year when one leg breaks.

07 | Three Ways to Make a Product Attractive

For any product to be “attractive,” there are basically only three directions. Pick one and stick with it. The worst move is wanting all three.

1. Generous and Comfortable

Make people feel like they are getting a good deal, being taken care of, and not being nickel-and-dimed. Typical plays:

  • Offer a very generous free tier (the early Vercel, Cloudflare, and Supabase route).
  • Make docs clear, error messages friendly, and community response fast.
  • Open-source your core, then monetize services and cloud hosting.

Good for products that build trust over the long term. The way it dies is being crushed by the cost structure of free users.

2. Exaggerated and Tempting

Visual impact, benchmark domination, explosive launches, extreme demo videos. Typical plays:

  • A launch video people cannot help sharing.
  • A benchmark number that is clearly ahead.
  • A controversial founder persona the media loves to cover.

Good for products that need short-term awareness. The way it dies is when the gap between the demo and the actual capability is too large. Once trust collapses, there is no second chance.

3. Hard-to-Explain Pull

You cannot quite say why, but you want to use it. The typical examples are command-line tools, minimalist products, and old-school tools that do not offer a “modern UI”: Vim, tmux, ripgrep, rsync, that kind of thing. They do not explain themselves. They assume you are willing to learn.

Good for serving deep users and building a cultural moat. The way it dies is getting eaten by a slightly friendlier competitor.

08 | The Logo Does Not Matter. The Ability to Give It Meaning Does

Technical founders spend too much time agonizing over logos, fonts, brand colors, and domain suffixes.

The truth is very direct:

When you are starting up, if you cannot genuinely identify with or oppose your logo, then what it looks like does not matter at all. As long as you like it, it is fine. No one else cares.

Apple’s logo is an apple with a bite taken out of it. Over time, they made it more boring: flatter, plainer, more “nothing.” It became more valuable anyway.

The value is not in the graphic. It is in your ability to keep giving the graphic meaning.

For an LLM company, if the product is good, seeing the logo will make you think, “That company gets things done reliably.” If the product is bad, even the most beautiful logo is empty.

Do not spend more than four hours on the logo in year one.

09 | Good Friends Are Not Cofounders

Tech people especially love “starting a company with good friends.” This is one of the most common and most expensive forms of self-deception in startups.

The internal loop of friend-based startups looks like this:

Mutual affection → no one wants to be the wet blanket → no one questions PMF → no one challenges the pricing strategy → no one asks “Where is the sales pipeline?” → resources, time, and energy get wasted → the company burns through its budget in perfect harmony → when everyone breaks up, they all feel the other person changed.

What you need is not a friend. You need:

  • Someone who can disagree with you constructively.
  • Someone who can push back efficiently and with depth.
  • Someone who can respectfully challenge every view you hold and is smart enough to push the argument to a conclusion.

In a tech company, this often means you need a cofounder who makes you uncomfortable: a business cofounder who asks, “Who will pay for this feature?” a product cofounder who tells you, “Engineers are not the users,” or a design cofounder who reminds you, “This UI will make users feel bad.”

10 | The Truth About Communication — Someone Has to Decide

Engineering culture easily falls into two extremes: the “democratic decision-making trap” and the “RFC rat race trap.” Every decision gets a meeting, every meeting gives everyone a voice, every voice is respected, and three weeks later nothing has been decided.

Many high-IQ people, and here “high-IQ” is a neutral description, are smart enough to always find problems, but not smart enough to solve them.

When people enter a meeting with that mindset, they only want to be heard. They do not care what they are saying, and they do not care whether the discussion is moving a decision forward.

The mature way to handle this is not fancy:

  1. State openly in advance: “In this company, I (or a specific person) am the idiot who has to put a hand on the table and make the call. You can all have opinions, but opinions do not change the final decision.”
  2. Let everyone speak once first. You cannot skip this step, or people will feel unheard.
  3. Once the decision-maker speaks, everyone should assume he has listened, processed it, and still holds the final decision rights.
  4. The decision-maker’s pain point and decision rights must be tied together. When the company loses money, he still has to pay the team out of his own pocket. Without that, decision rights are fake.

Communication is not democracy. Communication is making sure everyone knows when there is no more room for discussion.

11 | Positive Cash Flow Does Not Equal Profit

This is one of the easiest things for beginners to confuse, and it is especially deadly in the AI / SaaS era.

  • Positive cash flow: you have 100 yuan in the account on January 1, 200 yuan on February 1, and a net inflow of 100 yuan during that period.
  • Profit: after taking that positive cash flow on paper and subtracting debt still being repaid, assets that should be depreciated, future commitments you must pay, and marginal costs that have not yet settled, the result is still positive.

The most hidden cash-flow trap in AI startups is token cost:

  • You think you made money, but you did not account for the marginal cost of model APIs. This is especially true for agent products, where one task may call the LLM hundreds of times.
  • As user activity rises, your costs grow linearly or even superlinearly.
  • You think prompt caching will save you, but cache hit rates are never as optimistic as the demo.

Other hidden costs you still need to subtract:

  • GPU / server depreciation.
  • Vesting equity for the team. This is a real cost, not free money.
  • Retention bonuses, compliance spending, and hosting fees you owe over the next year.
  • The backstop cost of SLAs promised to customers.

If you do not know exactly which state you are in, you are probably not truly profitable.

Sustainable does not mean profitable either. A cash-flow-positive nonprofit can be “sustainable” for many years, but that may not be the state you want.

12 | Debt Is a Tool, Not a Shame

Many technical people are taught from childhood: do not take on debt, do not owe people money, and that makes you a good person.

That can make you a good person. It can also make you a bad businessperson.

Truly large organizations essentially live inside debt, commitments, and future delivery. When other people need you to fulfill promises in the future, you have truly entered the web of incentives.

Debt takes several forms in tech startups, and you need to learn how to use all of them:

  • Financial debt: VC funding is essentially a form of delayed-payment debt, a promise of returns. Debt financing, convertible notes, ARR financing, revenue share, and so on all belong here.
  • Commitment debt: roadmaps promised to customers, milestones promised to investors, vesting promised to the team, maintenance commitments promised to the open-source community.
  • Technical debt: this one is discussed too much, to the point that many people fear it excessively. The right amount of technical debt is the price of startup speed, not a moral stain.

Do not be the “zero-debt” good person. Learn to use respect, calm, and structure to make other people economically tied to your survival. If you owe someone for 10 years, they want you to last 10 years.

13 | Timing Is Part of the Product

Every industry has cycles. The cycles in AI and tech can be more hidden, but more deadly:

  • Major model version cycles: every three to six months, a new model comes out, and your entire set of prompts and evals may need to be rewritten.
  • Fundraising cycles: VC mood, interest rates, and macro narratives all affect your next-round valuation, and none of that has anything to do with your product.
  • Hiring cycles: in certain periods, such as big-tech layoff waves or graduation season, the quality of talent you can hire varies wildly.
  • Content high season vs. low season: around holidays, major model releases, and industry conferences, traffic distribution is completely different.
  • User willingness-to-pay cycles: B2B follows budget quarters; C2C follows consumer spending seasons.

You need to design the timeline as part of the product.

In peak seasons, do things that amplify momentum: launch, fundraise, hire, push content.

In slow seasons, do compounding work: write docs, pay down technical debt, build internal tools, organize SOPs.

Go one step further and store peak-season output for slow-season consumption: content written ahead of time, tickets completed ahead of time, demos recorded in advance, processes documented in advance.

Summary

Take business seriously;

find a demand worth betting on;

clarify the Vision and Mission;

validate the core assumption at the lowest possible cost;

make the product and company walk on multiple legs;

choose one type of product appeal;

keep giving symbols meaning;

stay away from friend-based partnerships;

learn to be the decision-maker;

tell cash flow from profit;

treat debt as a tool;

treat the timeline as part of the product.

The rest is daily labor, administration, and marketing, repeated every day.

Thanks for reading this far. This guide is not a path you can copy directly. No one learns startups by reading articles, and the tuition you owe will not be discounted by a single cent. But a few pits others have fallen into may help you fool yourself a little less and find a little more direction when you run into similar problems.