It has become increasingly common for projects to launch with low floats and high FDVs.

These tokens usually experience rapid price appreciation due to limited liquidity available for trading at launch. Retail enters, only to get diluted by the constant unlocks.

We would recommend getting launched with a low FDV backed by the revenue flows of the Protocol. We would approach defining the optimal FDV by running a cash flows analysis first and defining the crypto-DCF (Discounted Cash Flow) model of the protocol first, and then reverse-engineer the FDV.

The best way to launch a token and grow a community alongside - is starting with a relatively low FDV now, growing organic volumes, building the product with a clear PMF and organic traction and by having a low FDV at the start - let the community be a part of the journey and generate returns.

@CoinstructLabs | Tokenomics

How to approach Staking Rewards distribution?

We would recommend keeping the Staking Incentives vesting for a longer period (24-32 month) in order to ensure the distribution stability, but at the same time decrease the Staking reward rate coming from emissions (that’s crucial) logarithmically every month. The best way is to think of it the same way as overcoming the “Network Effect” with increased rewards rate at the beginning to incentivize new comers, but decreasing inflation (as rewarding stakers of XYZ with XYZ is nothing else then inflation with postponed sell-pressure).

But how will the stakers will be incentivized, if the Staking Rewards emission rate will be decreasing over time? We would introduce Staking Pool Replenishing - when “inflationary” rewards in XYZ to stakers will be decreased, the APY should be substituted and increased (in the future) bu flows coming from Protocol Revenue.

Staking is indeed popular, but its effectiveness depends on the protocol’s revenue. If staking rewards come from protocol revenue, growth in revenue amplifies staking's positive effect. But when revenue plateaus or declines, staking can turn negative, as early stakers begin selling their rewards and new stakers aren’t entering.

@CoinstructLabs | Tokenomics

Innovational Tokenomics Model & TVL-based emissions

Max from Coinstruct sat down with Arthur Schaback, Founder of Clip Finance, formerly Founder Paxful - to discuss how Tokenomics can be connected to the core product.

What will be in the video:

-The negative impact of pre-determined Tokenomics and how to overcome it
-How Clip Finanace manages liquidity to maximize yields
-TVL-based emissions in Tokenomics
-Challenges of building innovational Tokenomics
and more..

Coming this Friday, October 25th. Stay Tuned!

@CoinstructLabs

Seeing a lot of bad takes on socials regarding Ethereum vs Solana right now.

It's about time we cut through the noise with a data-driven approach.

Let's compare the economics of the two networks across:

1. Value Accrual
2. Total Economic Value
3. Cost to Produce $1 Fee Revenue
4. Network Fundamentals
5. Performance & Valuation

It's both quantitative and qualitative.

1. Ethereum has done nearly $20b in all-time fees. Solana has done $495m, with 87% coming this year alone.

That said, Solana is catching up. It's done 41% of Ethereum's network fees (not including MEV) over the last 90-days.

If we include MEV, Solana has done 58% of Ethereum over the last 90 days and 77% over the last month.

2. In terms of Total Economic Value (Fees + MEV + Token Incentives), Solana ($1.19) has outpaced Ethereum YTD ($1.01b) over the last 90 days.

With that said, 79% of this is from Token Incentives (paid to stakers, dilutive as inflation to non-stakers).

If we only look at Real Economic Value (Fees + MEV), Ethereum ($2.6b) is still significantly outpacing Solana ($753m) YTD.

3. In terms of compensation to the supply side of each network, Ethereum has paid out $7b all-time to its validators (36%). $400m YTD.

4.Solana has paid out $247m all time. $212 YTD.

5. Cost to Produce $1 of Fees. Ethereum YTD = $1.28. Solana = $7.62.

@CoinstructLabs

CEX Insights for October

Spot trading volume on major exchanges rose by 17% MOM. The top three exchanges by growth rate were Coinbase (61%), Gate (36%), and Binance (24%). Derivatives trading volume rose by 25%. The top three exchanges by growth rate were Bybit (61%), Bitget (30%), and Mexc (25%).

Source — link
@CoinstructLabs

Coinstruct is proud to be Top-1 Tokenomics Provider on the market according to the rating by InnMind.

Read the article here: https://blog.innmind.com/top-web3-tokenomics-service-providers-to-watch-in-2025/

We've worked with more than 35 Web3 Protocols across various crypto verticals. Coinstruct is trusted by $5B chains and has 15+ professionals in the core team.

Join us on the journey to become global crypto corporation in the next 2-3 years⚡️

Hyperliquid Success Summary - a real case of token success and Product-Market Fit in Web3

Hyperliquid has amassed $1.58 billion USDC in assets. The airdrop distributed 274 million HYPE tokens, driving the token’s market cap to $4.75 billion and its FDV to $14.2 billion. Hyperliquid's futures open interest has grown 58.5% to $3.55 billion, and its daily trading volume hit a record $9.79 billion. Its Assistance Fund, backed by USDC trading fees, has repurchased 567,083 HYPE tokens and generated $82 million in profit, while the Hyperliquid Provider Vault (HLP) has earned $45 million in PnL gains with a 34% APR. With estimated annual revenue of $94.41 million, Hyperliquid is now one of the most profitable crypto protocols. Data Source.

Our great partners & clients AlphaMind is about to have a second IDO after the record-breaking success with EYWA sale.

🧑‍💻Details:

Less Than 24 Hours Left to Whitelist for $SPR IDO! ⏳

The SUPER.MEME IDO is your last chance to ride the meme-coin wave on Base chain!

Whitelisting ends TOMORROW as the IDO begins - don’t wait to lock in your allocation.

💫Why You Shouldn’t Miss This:
1. 50% TGE Unlock = Immediate liquidity!
2. Retail & VC Equal Conditions = A FAIR launch!
3. Powered by Base, the fastest-growing L2 ecosystem.
4. The meme sector is exploding—this could be your next big alpha!

Karma Tiers = Guaranteed Allocations!

🔗 Whitelist NOW: https://app.alphamind.co/ido/675725e3960e2d6663f82bd0

Are you paying attention to Bitcoin's short-term holder supply?

You should be.

Here's why:

During bull markets, *new money* (short-term holders) enters the market.

They buy tokens from long-term holders.

So, naturally, as the price rises, we tend to see a drop in long-term holder supply and an increase in short-term holder supply.

As this dynamic plays out, we get a view into the stability of the market structure.

After all, short-term holders are not *diamond handed.* So as their allocation rises, risk in the market rises with it.

------

What do we see today?

1. 16.6% of the supply is currently in the hands of short-term holders.

2. In the last cycle, BTC price peaked when short-term holders controlled 25% of the circulating supply.

3. Short-term holders are sitting on 25% gains on average (ST MVRV)

4. They were sitting on 45% gains in March. And the last cycle peaked when they were sitting on 80% gains.

Post is based on on interesting insight from Michael Nadeau (DeFi Report)

@CoinstructLabs

Coinstruct in 2024:
A Year in Rewind🪄🌲

-$800M cumulative TVL across projects served.
-35 tokenomics systems expertly crafted for Web3 ventures.
-6 world-class specialists added to our growing team.
-5+ new tokenomics-focused products developed.
-50+ partners joined the expanding Coinstruct Network.
-Expanded operations to Singapore, Hong Kong, and 5+ countries.
-6,000 followers gained across social platforms, amplifying our global reach.
-Achieved #1 Tokenomics Firm status in international rankings.

In 2025 we're excited to continue building-up our methodology and products, integrating AI-agents into our simulations, develop token dashboards for our clients, conduct insightful researches on the innovative token distribution models and demand-generation mechanics.

Thank you for being with Coinstruct! And wish you all a Happy New Year!🤍❄️

A meme of Token Liquidity

Many ppl ask us to explain ZK, soo this a simplest way to describe how the ZK rollup process works:

1. Users send many transactions to the Layer 2 operator.

2. The Layer 2 operator batches these transactions and generates a ZK proof.

3. The ZK proof is submitted to the main blockchain, which verifies the proof and updates its state accordingly.

@CoinstructLabs

Talking to a lot of AI infra projects & DeAI and agents, the space is moving extremely fast, here are some thoughts regarding Tokenomics for AI-based projects:

1. Connect network productivity to the main token. Platform fees should be charged for usage of the models alike data credits and a portion of this revenue should be directed to main platform token buyback&burns or buyback&LP, depending on the operational stage of the project.

2. Launch Token when you already have some traction & models being used or you have both supply & demand, if you are building in the DePin space aka marketplace equilibrium. So at the moment of TGE you have already accumulated some revenue that can be used to cope with listing sell-pressure.

3. Fixed Token supply models are actually not the worst for Web3 & AI, if you don't want to overcomplicate system design go for deflationary tokenomics.

@CoinstructLabs

Just curious to know, that Worldcoin ($WLD) generates almost $10M of sell-pressure daily via linear token release:)

Instead of fully performance-based emission approach, we would see a lot of tokens succed with mixed Supply model.

1. Fixed Supply - the most usable and straightforward emission model. X tokens, Y month vestings for Z pools.

However, the emissions are not tight to the protocol adoption & revenue generation. The Supply is predefined and time-based.

2. Performance-based emissions are different. The Supply can also be fixed, however, the emission rate is based on the Network adoption triggers: TVL, Volumes, MAU, ARR or others.

It can be chaos - as it's impossible to predict the product success.

So we believe in traditional fixed time-based approach for VCs/Liquidity + all necessary for TGE allocations, however, Network Incentives are best emitted using Performance-based distribution.

@CoinstructLabs

Just found how Ralph Merkle compared Bitcoin to life:

"Bitcoin is the first example of a new form of life. It lives and breathes on the internet. It lives because it can pay people to keep it alive. It lives because it performs a useful service that people will pay it to perform… It can’t be stopped. It can’t even be interrupted. If nuclear war destroyed half of our planet, it would continue to live, uncorrupted".

DPoS consensus protocol may not be adesirable approach for establishing a highly decentralized social media platform.

Just live with it now.

Curious why some blockchain platforms fail despite strong ideas?

Steemit, a once-promising decentralized social media platform, faced significant issues rooted in its tokenomics. From wealth concentration to reward system exploitation, the platform’s design flaws offer valuable lessons for future projects.

Explore how misaligned incentives can undermine long-term growth—and why careful tokenomics planning is critical.

Read the full analysis by Coinstruct

New Research Alert!🧑‍💻

Token Staking: Evaluating Economic Efficiency, Net Effects, and Sustainability

Many Web3 founders struggle with Staking: is it beneficial or distructive for a token economy?

We've created one of the most detailed analytical reports on staking with a focus on implementation in tokenomics.

What you can find here:

-Key staking models (fixed vs. dynamic, inflationary vs. real yield)
-Case studies of successful & failed staking implementations -How to optimize staking rewards without wrecking your tokenomics
-Comparison with other token utility models

Read the research: link

@CoinstructLabs

High Staking Ration for tokens (% of the circulation which is "locked"): Good or Bad for the tokenomics?

We're excited to draw parallel between tokenomics and traditional macroeconomy. In this context, we envision token staking as "saving".

As staked (hard staked) tokens are typically not spent on consumption; they are held to earn a return (reward or yield). This is akin to putting money in a savings account to earn interest.

From a macro perspective, a high staking participation rate means a high aggregate propensity to save in the token economy. For example, networks like Cardano or Sui have 60–80% of tokens staked, indicating the majority of holders are saving (investing in network security) rather than transacting​. The upside is a strong commitment to the network (and reduced circulating supply with lower token velocity), but the downside is potentially lower usage of the token for commerce if too few tokens circulate. Projects should seek a balance where enough tokens are staked to secure the network and signal holder confidence, while enough remain liquid to facilitate transactions and utility.

@CoinstructLabs

What if, we can estimate GDP, but in token economies?

GDP has proven to be the most effective metric in economic history - we're sure, that it can be vital in tokenomics design & monitoring as well!

In macroeconomics, GDP measures the total value of final goods and services produced, reflecting economic output. Similarly, a token economy’s “GDP” can be considered the total economic output or transactional value within that ecosystem. One way to quantify this is by using the Equation of Exchange (from monetary theory) tailored to crypto: 𝑀 × 𝑉 = 𝑃 × 𝑄. Where M is the total token supply in circulation, V is the token’s velocity (the average frequency each token is transacted), P is the price of goods/services in terms of the token, and Q is the quantity of goods/services exchanged. The term (𝑃×𝑄) represents the total value of transactions – effectively an analog to GDP for the token economy. Rearranged, velocity can be calculated as: V= P×Q / M.

This formula implies that a token’s Market Cap (which is 𝑀 × 𝑃) times its velocity equals the total transaction value in the network.

In other words, Token Velocity × Market Capitalization = “Token GDP” over the period. For example, if a protocol has 1,000 tokens in circulation (M) at $10 each and those tokens facilitate $20,000 worth of trades in a year (P×Q), then velocity 𝑉 = 20,000 / 10,000=2. This indicates the token supply turned over twice, and the network’s annual output (Token GDP) is $20,000.

@CoinstructLabs