AI x Crypto Lesson 5: Everything you need to know about Tokenomics and Narratives

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Lesson 5: Tokenomics & Narratives

If there’s one thing I wish every beginner understood early, it’s this:

A token isn’t “good” because the logo looks nice, the community is loud, or the price is pumping. A token becomes real when the tokenomics makes sense and the narrative makes people care.

In your head, tokenomics is the “math side” and narrative is the “story side.”
In the real world, they’re tied together. If the story says “long-term project” but the tokenomics screams “cash grab,” people notice.

This lesson is how I personally look at tokenomics and narratives in a simple, practical way without turning it into a finance textbook.

Let’s dig in!

1) What Tokenomics Really Means (In Plain English)

Tokenomics is just “token economics”: The rules of how the token works:

• How many tokens exist (supply)

• Who gets them (distribution)

• How they enter the market (launch method)

• How people trade them (liquidity)

• Whether there are fees/taxes (buy/sell tax)

• Whether tokens are locked or unlocked (vesting/lockups)

• What the token is used for (utility)

A simple analogy that I like

Think of a token like a new game launching:

If the marketplace is empty (no liquidity), the game is unplayable. If the devs can print unlimited coins, the game economy collapses. Tokenomics is that same idea, but for crypto.

2) Coins vs Tokens (Tokenomics Looks Different)

This matters because beginners might mix them up.

Coins (native assets)

A coin usually powers its own blockchain:

• BTC $BTC ( ▼ 1.65% ) is the coin of Bitcoin

• ETH $ETH ( ▼ 2.27% ) is the coin of Ethereum

• BNB $BNB ( ▼ 2.42% ) is the coin of BNB Chain

Tokenomics for coins is often about:

• Issuance/mining/staking rewards

• Security incentives (miners/validators)

• Long-term monetary policy

Example: Bitcoin’s “21 million max supply” is basically a tokenomics story by itself. It tells people: “This is scarce.”

Tokens (built on top of a chain)

A token lives on a chain like Ethereum or BNB Chain:

• USDT / USDC (stablecoins)

• UNI (Uniswap governance token)

• CAKE $CAKE ( ▼ 0.54% ) (PancakeSwap token)

• SHIB $SHIB ( ▼ 1.35% ) / PEPE (meme tokens)

Tokenomics for tokens is more about:

• how tokens are distributed

• how liquidity is created

• how fees/taxes are designed

• what utility exists (or whether it’s purely meme/community)

In this course, we’re mainly operating in the “token world” because that’s where launches (like PinkSale → DEX listing) happen most often.

3) The Core Tokenomics Pieces I Always Check

3.1 Supply (Total, Circulating, Max)

Supply is where beginners get tricked.

If a token has 1 quadrillion supply and the price has many zeros, people think:
“Wow, if it hits $1, I’m rich.”

But if the supply is huge, “$1” might be mathematically unrealistic because the market cap would be insane.

Example:

• Token A: 1,000,000,000 supply (1B)

• Token B: 1,000,000,000,000,000 supply (1 quadrillion)

Even if both projects are equally popular, Token A can reach a higher price more easily because supply is smaller.

Supply doesn’t make something good or bad. It just changes how price behaves and how “cheap” a token feels.

3.2 Distribution (Who gets what)

This is one of the biggest “trust signals” in crypto.

A simple distribution might look like:

• 50% sold to early buyers (presale/fair launch)

• 40% for liquidity + exchange listing

• 10% team/marketing (ideally locked/vested)

If a project keeps too much for insiders and it’s not locked, it scares off serious buyers.

On PinkSale listings, you’ll often see clear buckets like:

• Tokens for presale

• Tokens for liquidity

• sometimes an initial market cap estimate

That’s the type of clarity I like.

3.3 Launch type: Presale vs Fair Launch

This matters because it changes the psychology and the mechanics.

• Presale usually has a soft cap and a hard cap (minimum and maximum raise).

• Fairlaunch often doesn’t have that same “ceiling.” A fairlaunch is recommended because you don’t have a hard cap limiting the raise.

In practical terms:

• Presale = structured fundraising window (more “ICO style”)

• Fairlaunch = tends to feel more open/organic, and can be simpler for beginners

For instance:

Presale:

One of the most famous crypto presale projects was Ethereum's back in 2014. The project raised over $18 million by selling 60 million ETH tokens at about $0.30 per ETH, in exchange for Bitcoin. This presale was groundbreaking, as Ethereum introduced smart contracts and decentralized applications, revolutionizing the blockchain space.

Ethereum's success led to massive growth, with ETH reaching its peak of $4,900 in August 2025 and is currently around $2,932 per token, offering huge returns for early investors.

Fairlaunch:

Bitcoin (BTC), created by the Japanese Satoshi Nakamoto, launched on January 3, 2009. The coin itself was worth pennies, and then gained attention when 10,000 BTC were used to buy two pizzas in 2010. Bitcoin's price then hit its peak around $126k in October 2025 and the current price is approximately at $87,000.

Bitcoin is now the largest cryptocurrency by market cap, currently over $1.7 trillion, and has played a key role in popularizing decentralized finance and blockchain technology.

Both of these projects have become the most successful cryptocurrency projects the world has ever seen. Ultimately, what matters most for long-term success is the execution and quality of the project itself, regardless of whether you choose a fairlaunch or presale approach. 😁 

3.4 Liquidity (this is non-negotiable)

If your token is going to trade on a DEX (PancakeSwap/Uniswap), it needs a liquidity pool.

I will re-explain this: A liquidity pool is a smart contract holding reserves of two assets so traders can swap. For example: your token + BNB if launching on PancakeSwap.

No liquidity means:

• nobody can buy or sell smoothly

• people assume it’s a scam or “untradable”

Why PinkSale matters for liquidity

Manually funding liquidity can be expensive.

PinkSale solves that by raising funds and automatically creating + locking liquidity based on the percentage you set.

This is also why you’ll see settings like:

• Liquidity percentage (example shown: 51%)

• Liquidity lock time (example shown: 364 days)

Those two numbers are major trust signals.

3.5 Taxes / Fees (buy tax, sell tax)

This is a big part of tokenomics for many new tokens.

Taxes are often used for:

• marketing budget

• liquidity growth

• dev/treasury funding

• rewards

A very beginner-friendly tax structure:

• Buy tax split into marketing + liquidity + dev (example: 1% + 1% + 1% = 3%)

• Same idea on sell tax

• It also mentions setting maximum allowed fees (commonly capped around 10%) because extremely high fees are a red flag for trust.

How I personally think about taxes:

• A small, clearly explained tax can be reasonable.

• A huge tax with vague explanations usually screams “exit liquidity machine.”

4) Narrative: The “Why People Care” Layer

If tokenomics is the engine, narrative is the fuel.

Narrative is the reason a stranger sees your token and thinks:

• “This is fun”

• “This is relevant”

• “This actually solves something”

• “This community is worth joining”

Here are the three narrative buckets I see most often

A) Meme narrative (community + attention)
Examples: SHIB, PEPE $PEPE ( ▼ 2.1% ) , etc... You can launch a simple meme coin (with or without light utility) and use trending topics to keep attention.

B) Utility narrative (if it does something)
Examples:

• UNI: governance around a major DEX

• AAVE: lending/borrowing utility

• LINK: oracle utility

C) Trend narrative (rides what people are already talking about)

Example: using a major social event (like elections) to build a meme token around what people already search, joke about, and share plus optional NFTs or collectible memes.

I like trend narratives because beginners often underestimate how much “attention” moves markets.

5) Matching Narrative to Tokenomics

Here’s a truth I’ve learned the hard way:

If your narrative and tokenomics don’t match, your project feels fake even if you didn’t mean it.

Example 1: “Community meme token”

If you claim “community-first,” but:

• team holds 40% unlocked

• liquidity isn’t locked

• taxes are huge

…then people will assume you’re planning to dump.

A cleaner meme token tokenomics usually looks like:

• simple supply

• fair launch or transparent presale

• locked liquidity

• either no tax, or a very small tax with clear usage

Example 2: “Serious long-term utility”

If you claim “we’re building big utility,” but:

• there’s no budget plan

• no roadmap

• no token role (what does the token actually do?)

…then the narrative collapses.

In that case, tokenomics should clearly support:

• dev/treasury funding (transparent)

• vesting for team tokens

• a believable utility timeline

6) The Exact AI Tools I Use for Tokenomics + Narrative

I’m going to be very specific here. These are beginner-friendly and practical for you.

Tool 1: ChatGPT (or Gemini): My tokenomics co-founder

What I use it for:

• turning rough ideas into structured tokenomics

• creating multiple tokenomics options (low-tax vs no-tax vs utility)

• rewriting complicated tokenomics into plain English

How I use it (simple workflow):

1. I describe the idea and the chain (BNB Chain or Ethereum)

2. I tell it the launch method (fair launch or presale)

3. I ask for 2–3 tokenomics models with pros/cons

4. I ask it to rewrite the final choice as a clean “Tokenomics section”

Prompt example I actually use:

“I’m launching a beginner-friendly meme token on BNB Chain and listing on PancakeSwap after a fair launch. Give me 3 tokenomics options: (1) no tax, (2) 2% total tax, (3) 4% total tax with rewards. For each option, explain pros/cons and what investors might worry about. Keep it simple and honest.”

Tool 2: Claude: “My long document & logic reviewer”

Claude is extremely good when you give it long text (like a tokenomics page, or later, even a contract).

What I use it for:

• checking if a tokenomics explanation sounds confusing or shady

• spotting contradictions (example: “community-first” but 30% team supply unlocked)

• rewriting tokenomics into something that sounds transparent and professional

Prompt example:

 “Here is my tokenomics draft. Please audit the logic like a skeptical investor. List anything confusing, any trust red flags, and suggest how to rewrite it more transparently.”

Tool 3: Perplexity - My research engine

When I’m validating a narrative, I want reality checks:

• Is this trend actually trending?

• Are people talking about it?

• Are there competitors?

Perplexity is great because it’s search-native and helps you gather sources quickly.

What I use it for:

• confirming whether a narrative (AI tokens, election memes, gaming, etc.) is hot right now

• collecting competitor examples fast

• finding how similar tokens positioned themselves

Prompt example:

“Find 5 popular meme tokens launched recently on BNB Chain and summarize their narrative, tokenomics style, and what made them viral.”

(Then I compare that with what I’m building.)

7) My “Beginner-Safe” Tokenomics Templates (with examples)

These aren’t rules, just frameworks I’ve seen work best for clarity.

Template A: Simple meme token (the lowest friction)

• Supply: 1B tokens

• Taxes: 0% or 1–2% total

• Liquidity: strong % + locked

• Story: fun + community

Example token types: Early SHIB-style meme phase, PEPE-style meme hype (no promise of utility).

Template B: Serious utility token (this is harder, needs real execution)

• Supply: depends (often smaller and more structured)

• Vesting: team + treasury locked

• Taxes: optional; or revenue model instead

• Utility: must be clear and provable

Example token types: UNI/AAVE/LINK style - tokens connected to a real protocol.

8) The big takeaway

If you have to remember one thing from this lesson, let it be this:

Tokenomics builds trust. Narrative builds attention. You will need both to succeed.

And the best part is: You don’t have to do this alone anymore.
With the right AI tools, you can brainstorm faster, write clearer, compare smarter, and catch obvious red flags before they become expensive mistakes without turning crypto into rocket science.

In lesson 6, I will move into risks and scams more directly because once you can read tokenomics and understand the narratives, the next step is learning how to spot the traps that target beginners.

If you enjoy this lesson, please let me know and check out these amazing news, contents, experiences and tutorials related to AI and Crypto from our team down below🔥✌️👇️:

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