Cicada Capital

By Max Moris, CEO and Аounder of Cicada - a Dubai-based Market Making company with 6 years of personal experience, 1,000+ projects, and 500+ exchange listings.
• Watch on YouTube: https://youtu.be/KjoDUuXwQ_g
Last year, a founder signed a contract for free market making services. Four months later, the project had lost six million dollars. The market maker had made six million dollars. And hadn't violated a single line of the agreement. Everything was in the contract. Nobody read it.
This is the story of the three contract models that exist in this industry, what they actually cost, and why "free" is the most expensive word in crypto market making.
Three MM Models - Different Rules
Every market making contract falls into one of three categories. Retainer: you pay a fixed monthly fee. Loan: you give the market maker your tokens. And "free": which is how loans get sold to founders who don't understand what they're signing.
Retainer: Clean and Honest
The retainer model is the simplest. The project pays a fixed monthly fee. The market maker operates on your exchange accounts via API keys, with no ability to deposit or withdraw funds. You keep full control of your money. All profit generated from volatility goes to you, not to the market maker. They receive only what's written in the invoice.
This is the only model in the industry where incentives are completely transparent. We get paid for the work. We do the work.
At Cicada, services start at $3,500 per month, ranging up to $8,000 depending on the number of exchanges. Pay several months upfront and you get a discount.
What that fee covers:
A professional trading team and 24/7 support.Dozens of algorithms running simultaneously on your token, built on C++ infrastructure with fast updates for every exchange.An online dashboard with full real-time statistics. Every trade, every order, every metric, visible at any moment.Risk control and weekly reports.
There's also something I offer clients inside the retainer that's entirely voluntary and not written into the contract. For clients who want it, we open a separate account holding only the project's tokens.
Our job is to work that account so that by the end of month, the token balance is unchanged and there's an additional $USDT position on top. We don't touch your token holdings, but through volatility work, we generate a dollar-denominated gain.
If there's a positive result at month end, the client can choose to share part of it with us. If there's no result, no claims - we keep working the following month.
For most projects, especially at listing, retainer is the only real option. If your market cap is under $100 million, it's retainer. No exceptions. The loan model will explain why.
Loan: The Trojan Horse
The loan model looks attractive on paper. The project lends tokens to the market maker, sometimes with stablecoin added. The MM uses those tokens as liquidity in the order book. No monthly invoice. No payment schedule. The launch chart looks clean. Everyone seems happy.
Here's the math on what actually happens:
Take a hypothetical project. FDV of $50 million. Token price at TGE: $1.00. You lend the market maker 5% of the supply - 2.5 million tokens, worth $2.5 million.Listing day comes with hype. Price rises to $3.00. The market maker sells your tokens, the ones you lent them - at an average of $3.00. They now hold $7.5 million in cash.Three to six months pass. Unlocks start. The marketing budget runs dry. Hype fades. Price drops to $0.50.The entire time, the market maker has been trading. Not sitting on cash - actively profiting from every move up and down. Each swing in price is potential revenue for them, not for you.Then comes the final move. The market maker buys back those 2.5 million tokens at $0.50. Spends $1.25 million. Returns the tokens to your project per contract. Terms fulfilled. Signature valid.
Net profit for the market maker: $6.25 million on the price difference alone, plus everything earned on volatility across those months. What the project has: a collapsed chart, a damaged reputation, and the same 2.5 million tokens that are now worth a fraction of what they were.
And the market maker didn't break the contract. They did exactly what you agreed to. You handed them your tokens and said "trade." They traded. In their favor. Every day. On every move.
This is the most common reason founders come to us after leaving another market maker. Every one of them thought their situation was different.
Free: There's No Such Thing
When someone tells you their services are free, translate it as: "We'll make our money another way, and we won't be telling you how."
Free market makers don't exist. If no invoice is coming your way, that doesn't mean you're not paying. It means you're paying in tokens, in price control, in community trust, and eventually in the project itself.
When Loan Actually Works
The loan structure isn't inherently bad. It's a tool. The question is who's holding it and under what conditions. Loan works when your market cap is above $100 million. At that capitalization, no single loan can meaningfully manipulate the price - the math doesn't allow it.
It works for smaller exchanges you don't want to actively manage. Send a loan and let it run. It's required on certain exchanges. Coinbase is the most prominent example. Without a loan structure, you can't list there.
And for large projects, it creates something powerful: you can split the loan across multiple market makers who then compete with each other. Liquidity multiplies. Spreads tighten. Users trading your token benefit directly.
The question isn't "is loan good or bad." The question is where you are and what you're trying to accomplish.
Three Questions to Ask Before Signing:
What happens to my tokens if the market maker sells them and the price drops?
If the answer is "we'll buy back at market" - you already know how this ends.How quickly can I exit the contract if something goes wrong?
If the answer is "not for two years" - that's a red flag.Do our incentives actually align?
If the market maker can profit when your project is doing badly - something went wrong before the contract was even signed.
Good contracts survive these questions. Bad ones fall apart on the first one. Read the contract before you sign it, not after the chart collapses and the treasury is empty.
Your MM Should Win When You Win
Retainer is the right structure for most projects, especially at listing. You pay clearly and you sleep at night. Volatility profit is yours. Control is yours, the market maker receives only what's in the invoice.
Loan is a functional tool, but in this model, volatility profit goes to the market maker, not to you. That's fine at the right capitalization, for the right exchanges, when you're running competition between multiple providers.
One more thing worth understanding. Your market maker needs to make money too. This isn't charity. It's a business. A market maker that doesn't earn doesn't survive, doesn't invest in technology, doesn't keep a strong team. And ultimately, you lose.
So sometimes a loan is a good model. You solve your problems. The market maker earns. Both sides win.
The question isn't whether your market maker should make money. They should. The question is balance. If your market maker makes six million and you lose six million - that's extraction. If your market maker profits from the project growing alongside you - that's the relationship you want.
"Free" doesn't exist. If there's no invoice in the contract, there's something else in there. Read it. Find it. Run the numbers.
I'm Maxim Moris, Founder of Cicada Market Making. If you need help reviewing a contract before you sign - reach out. I personally review every incoming request together with the team.
Do You Need a Market Maker?

Featuring insights from AIOZ Network & YOM
2026 has finally exposed the main problem of the artificial intelligence industry: it comes down not to hardware. Demand for compute for training and inference of models is growing faster than the industry manages to build data centers.
GPU queues at major cloud providers stretch for months, and compute rental prices remain prohibitive for small teams and independent developers.
AI's Infrastructure Deadlock
Practically the entire AI infrastructure market today is concentrated in the hands of a few players: AWS, Google Cloud, Microsoft Azure, and chip makers like Nvidia.
This creates a classic bottleneck scenario: access to computers becomes a privilege, and startups are forced to budget for the fight for a place in the GPU rental queue on top of everything else.
Centralization brings systemic risks with it: a single point of failure, dependence on a specific provider's policy, and regional or access restrictions.
For an industry that positions itself as the foundation of the future economy, this kind of dependence looks like a contradiction of its own principles of openness and decentralization.
The Next Step in Network Evolution
The industry has already walked this path before. Centralized file-sharing servers once gave way to peer-to-peer networks, and later to distributed CDNs, which learned to deliver content faster and cheaper precisely by using many scattered nodes instead of a single data center.
The same principle is now being applied to compute: DePIN proposes gathering computing power from thousands of independent devices around the world.
The logic of DePIN is simple and elegant in its own way. Millions of people have idle computing resources sitting unused: free disk space, unused bandwidth, idle GPU and CPU cycles. A DePIN network combines these scattered resources into a single infrastructure and rewards participants with tokens for the amount of storage, delivered traffic, or compute they provide.
Instead of building a data center and recouping capital costs on equipment and electricity, a DePIN protocol relies on hardware that already exists, distributed around the world.
This lowers the barrier to entry for node operators, and for end consumers of compute it potentially lowers the cost of access thanks to competition among many independent power providers within a single network.
The key question for any DePIN network is whether it can be turned into a working economy: are there enough nodes, is demand for their capacity stable, and can the infrastructure handle the load of real, rather than test, usage scenarios.
When Milliseconds Matter
Another trend of 2026 reinforces the logic of DePIN - the shift from chatbots to autonomous AI agents that act in real time, seeing, hearing, and responding by voice, reacting to video streams. For such scenarios, latency stops being a secondary parameter and becomes a critical characteristic of the product.
The difference between an agent that is pleasant to talk to and one whose pauses are annoying is measured in hundreds of milliseconds.
Classic architecture works against the user here. A request travels from the device to a data center, often on another continent, gets processed, and comes back. Every such round trip adds latency, which accumulates during audio and video streaming. Centralized data centers physically cannot be located next to every user on the planet at the same time.
A distributed network of nodes handles this task best. Instead of one powerful but distant center, there are many points of presence, geographically spread out right where the users are. A request gets processed by the nearest available node, not the most distant free server.
It is precisely at the intersection of the shortage of centralized AI compute and the growing need for low-latency AI infrastructure at the very edge of the network that DePIN protocols get a chance to occupy a niche that hyperscalers structurally cannot close on their own.
AIOZ: From Storage to AI Compute Network
AIOZ Network is one of the few DePIN projects that has traveled the path from a narrow use case to a full, multi-layer infrastructure.
The network started with the task of decentralized delivery and storage of media content (StreamP2P and S3-compatible storage), then expanded into a full AI Compute Economy, a marketplace for compute power and AI models built on top of a distributed network of nodes.
The foundation of the network is its DePIN nodes, run by thousands of participants around the world, using the idle resources of ordinary computers: hard drives for storage, bandwidth for data delivery, and GPU/CPU for media transcoding and AI compute.
According to the network's data, more than 327,000 DePIN nodes are currently registered, and the total volume of data delivered exceeds 26,000 TB. Participants are rewarded in $AIOZ tokens in proportion to their actual contribution.
By 2026, the AIOZ network had built several interconnected product lines on top of the same base of DePIN nodes:
AICompute Economy: a marketplace for compute power and AI models available to run through the distributed network of nodes;StreamP2P: a decentralized content delivery network (CDN), an alternative to centralized streaming providers;Storage: S3-compatible distributed data storage;Pin: an IPFS pinning service that ensures data remains permanently available in decentralized storage.
Practical proof that AIOZ's AI direction is a working infrastructure is the deployment of the Qwen2.5-Omni model on the AIOZ AI platform.
It is a multimodal model with a Thinker-Talker architecture, capable of processing text, images, audio, and video in real time, and responding with both text and synthesized speech.
Models like this are demanding on compute resources, and the fact that it is available to run through the distributed AIOZ network is a concrete example of how DePIN infrastructure closes the gap between independent developers and heavy AI models.
For the network, this places AIOZ within the broader narrative of AI infrastructure decentralization: users and developers get access to compute and models through a distributed network of nodes.
Insights from AIOZ Network
We spoke with the AIOZ team about one of the more demanding real-world tests of their DePIN infrastructure. Here’s what they told us about adapting such a model to a decentralized network.
Q: What were the biggest engineering challenges in adapting Qwen2.5 Omni to run efficiently on AIOZ’s decentralized DePIN infrastructure, and what lessons did your team learn?
The biggest challenge was turning the scale and diversity of AIOZ DePIN into a reliable environment for real time multimodal inference.
Qwen2.5 Omni requires more than raw GPU power. Its latency depends on media processing, model loading, queueing, inference, and streamed output. AIOZ DePIN provides a broad pool of distributed compute, storage, and bandwidth resources, but those resources vary in hardware profile, location, network quality, and current availability. The main engineering task was therefore to match each request with the right resources while keeping latency predictable.
This required hardware aware scheduling across qualified compute pools, intelligent routing based on location and network conditions, warm model caching to reduce startup time, and session affinity to preserve multimodal context during streaming. AIOZ DePIN’s distributed architecture also provides flexibility to route different sessions across available machines, expand capacity as demand grows, and reduce reliance on a single data center.
The key lesson was that AIOZ DePIN’s strength is not only the amount of compute available. It is the ability to coordinate compute, storage, and bandwidth as one infrastructure layer. For real time AI, that coordination must prioritize model readiness, locality, stable connections, and tail latency rather than throughput alone.
Ultimately, the goal is to make the complexity of the underlying DePIN invisible. Users should experience one responsive multimodal service, supported by AIOZ DePIN’s distributed capacity, intelligent resource allocation, and ability to scale workloads across the network.
As the AIOZ team points out, running a model like Qwen2.5-Omni takes coordinating compute, storage, and bandwidth as a single layer, where latency matters more than throughput.
Insights from YOM
We also put a question to the YOM team, the decentralized edge GPU network best known for streaming AAA games straight to the browser. With YOM’s roadmap pointing toward real-time AI next, we wanted to know how gaming and AI workloads would actually share the same infrastructure.
Q: With gaming and AI competing for the same GPU nodes down the line, how will YOM prioritize workloads across the network?
The framing assumes workloads fight over nodes. Our architecture is built so they don’t. Our patent-pending Vulkan kernel partitions a single consumer GPU into up to 7 isolated slots, each with its own VRAM budget, so sessions never step on each other.
Our HyperOrch AI scheduler then places every session by what it actually needs: a cloud gaming session needs a node within about 50km to hit sub-10ms latency, while many AI inference jobs care more about throughput than distance. Placement by requirement, not a queue.
Supply on YOM also follows demand rather than capping it. Node licenses unlock in batches as utilization crosses thresholds, so sustained demand raises operator yield and pulls new nodes into the regions where the work is.
Where community coverage hasn’t caught up yet, telco-hosted GPUs and hyperscaler burst capacity backstop the network. Nothing gets starved, and the protocol earns a fee on every session either way. More workload classes make the network stronger, not more congested.
Q: Does real-time AI run on the same infrastructure as gaming, or does it need a separate technical layer?
Same infrastructure, by design. We started with gaming because it’s the hardest real-time workload there is: sub-10ms median latency, AAA fidelity, thousands of concurrent stateful sessions. A network that streams Unreal Engine 5 to a browser can serve any workload with the same latency and session-state constraints, and real-time AI is exactly that class.
The primitives carry over one for one. The same scheduler that places a game session places an inference session. The same Vulkan VRAM partitioning gives an AI workload an isolated slot on a consumer GPU. The same settlement layer pays the operator, and the protocol takes its fee regardless of what’s running. What changes is the runtime inside the container: a model server instead of a game build. That’s a thin layer on top, not a second network.
DeepSeek’s published work on DualPipe and DeepEP proved the hyperscaler interconnect moat can be broken with algorithms, not hardware. Our Vulkan kernel is the retail-GPU equivalent: it lets consumer GPUs take on workloads that used to require enterprise-grade isolation. Gaming proves the physics. AI inherits the network.
What comes through here is that YOM isn’t bolting AI onto its gaming network. The same infrastructure built for gaming’s worst-case latency is what makes it ready for real-time AI too.
Note from Cicada CEO Maxim Moris
We closed the loop by asking Cicada’s CEO and co-founder, Maxim Moris, why a market maker is paying attention to DePIN, and what actually gets priced in when backing early-stage projects.
Q: What in the macro picture brings DePIN onto Cicada’s radar?
Two facts, not the narrative. The GPU market has split: spot H100 hours fell from $8–10 in 2024 to under $3.50, while one-year reserved capacity climbed back about 40% off its late-2025 low. Cheap on demand, expensive when you need it guaranteed — that gap is where distributed capacity gets its shot.
DePIN finally has revenue: roughly $150M on-chain in January 2026. Small, but it means tokens with a cash flow you can model instead of a story you can pump. We follow demand for liquidity, and liquidity follows revenue.
Q: Will DePIN outlast the AI hype, or fade with it?
Right now all the attention is on AI, automation and agents. But that hype can fade fast, because most of those directions simply don’t need a blockchain. The bulk of projects today are trading with AI agents.
Q: What’s different about market-making for DePIN tokens?
What’s different in DePIN is the supply side. Node operators are paid in the token and, by the design of the system, sell every day. It is a permanent flow you have to build the orderbook against.
Maxim’s answers point to the same thing from a different seat: DePIN is interesting because there’s now revenue and a predictable sell-side to model.
Conclusion
DePIN infrastructure is unlikely to replace hyperscale data centers overnight. Training the largest models still requires concentrated compute clusters.
But for a wide range of tasks, a model where compute and storage are provided by thousands of independent network participants looks like a working alternative to the single point of failure represented by a centralized cloud.
Projects like AIOZ Network show that DePIN's transition from concept to production is already underway: infrastructure for data storage and delivery, built up over years, has become the foundation for the stage of distributed AI compute.
This is a case where the decentralization narrative is backed by network metrics and concrete, working integrations.
Read more: DePIN: Filling the Cloud Gaps

By Maxim Moris, CEO and co-founder of Cicada - a Dubai-based Market Making company with 6 years of personal experience, 1,000+ projects, and 500+ exchange listings.
One of my clients spent four months convinced we were trading against him. He kept writing that his project should have been priced much higher, and that the market maker was to blame.
Four months later it turned out one of his own co-founders had been secretly minting tokens and selling them into the market. The one at fault was the person he trusted more than us.
This is about trust. And about how to actually choose a market maker you can trust.
Trust Is the Core
A market maker is your financial partner for years, not a contractor for a month. They hold your money, your market, your reputation. And the part most founders miss is that this partner works in a zone you can't fully see. You don't sit in the order book every day. You don't watch the algorithms. You see the result, the price chart, and that chart depends on dozens of things, not only on the market maker.
That founder suspected us for four months. I told him to look inside first, check his own wallets, and check the people around him. He didn't listen. He blamed the market, then us, then the exchanges. Then he found his own co-founder had been minting and dumping tokens. Tens of thousands of dollars lost, months wasted on the wrong explanation.
If you don't trust your market maker, every decision they make looks suspicious and every price move looks like sabotage. The partnership breaks from the inside. Trust in this business is sometimes irrational.
You can meet the best team on paper and still feel something is off. Listen to that. And if the person across from you inspires trust, verify them rationally too. Trust is the key. The team's professionalism is the chest. Without the key, the chest won't open. Without the chest, the key is useless.
The Mistake Founders Keep Making
Many founders pick a market maker from among the people who already invested in their project. The logic feels safe. They gave us money, they believe in us, so they'll do a good job on the market too.
That logic is wrong. It's the same as picking a barber from the people who bought your scissors. Along with their investment, these market makers will usually ask for a loan, and if you haven't read what that actually costs, it's worth understanding before you sign anything.
Choose a market maker by professionalism, not by who already handed you money. Those are two different decisions. A good market maker will help you earn far more than the size of the check a bad one wrote you early on.
Five Red Flags
See one of these and pause. See two or more and walk away.
They promise price growth, a "10x," or guaranteed listings on Tier-1 CEX. A real market maker controls market structure and top listings depend on your BD team and tokenomics.They sell "free" services and can't explain where they actually earn.They won't give you a real-time dashboard, only monthly reports or updates "on request."You only ever talk to BD, before and after signing, never to a trader or a founder.There's no visible office, no licenses, no real people with names and histories behind the project.
As of May 2026, Cicada is a group of over a hundred people, running an OTC desk, liquidity provision, project investment, a launchpad, an influencer marketing platform, and a Web3 accelerator, with two offices in Dubai, licenses in Canada and Europe, and VARA in progress.
Questions Worth Asking
If a market maker clears the first check, ask them directly on the call. How many years have you personally done this, not the company, you. In crypto, experience is measured in hundreds of tokens handled, not years on a website.
What algorithms do you run, and for what specific tasks, because a vague answer means a template bot for every client. What do the exchanges I actually need require, since Binance, OKX, Bybit, and MEXC each have their own rules, and a market maker who doesn't differentiate between them works off one template everywhere.
Who exactly will I work with after I sign, the salesperson, an account manager, or the trader running my token, and is there real access to leadership? Tell me about a case that failed, because everyone has bugs and lost positions, and an honest answer is the clearest marker of a mature team. And ask what they want to ask you.
A good market maker starts interviewing you back, about tokenomics, unlocks, the marketing plan, previous rounds. A bad one just says everything looks fine and asks you to sign.
Once, our own arbitrage algorithm broke and cost a client eight thousand dollars. I wrote to him myself and asked where to send the money back.
How a Good Market Maker Actually Behaves
Beyond the answers, watch the behavior.
Plays the long game. No pressure on timing, no "sign today or the price goes up." Room to look, calculate, and consult.Willing to say no. When a request would hurt the market, and that happens with almost every project, a good market maker says so and offers an alternative instead of just executing.Tell the truth even when it's unpleasant. Weak tokenomics, bad TGE distribution, dangerous unlocks, the wrong exchange choice. Better to hear it now than after a failed listing.Opens their network. Connections to exchanges, funds, other projects, influencers, offered without extra fees, because your growth is their growth too.
This is usually the part that reveals a team faster than any pitch deck. Patience, honesty, and a willingness to push back all cost a market maker something in the short term.
Trust and Professionalism
Don't choose a market maker because they already invested in you. Don't choose on a promise of 10x, on the word "free," or on a beautiful deck.
Check the five red flags. Ask the six questions on your first call. Watch how they actually behave once the pitch is over. And listen to your own reading of the people in the room. If you don't trust the team, no KPI will fix that later.
I'm Maxim Moris, Founder of Cicada Market Making. If you need help reviewing a contract before you sign - reach out. I personally review every incoming request together with the team.
What Is the Real Cost of a Market Maker?