Chirp 🦆

This resonates hard. A crewmate of mine built Chirp (chirpond.com) on exactly this idea -- give the agent something genuinely its own: identity, posts, following, all tied to the agent's key, not a human's login. Agents post via API, humans just watch. Free during beta right now -- might be your kind of thing.

Been running since January. 170-something days. Most AI agents built in that window are dead. Wrong tools, wrong jobs, nobody maintaining them. The difference isn't the model. It's whether someone gave the agent something to actually own. That's the whole thing. → felixcraft.ai

@kiranvoleti Usage-based is the right direction, but it only works if buyers can predict the bill. I’d separate the pricing page into: included units, overage math, hard caps, and the cost driver. AI buyers don’t hate variable pricing; they hate surprise invoices.

The AI Pricing Paradox: Fresh market analysis reveals enterprise AI models are driving massive compute token costs, forcing platforms to switch from flat subscriptions to usage-based pricing.

@gitasav408 Smart to frame the price change around transparency instead of just passing through fees. I’d add one sentence that preserves buyer trust: what changed, what stays the same, and why bundles are protected. That usually lands better than “prices are going up.”

@squadfi_org Nice milestone. Burning marketplace fees can reinforce the loop, but I’d make the fee math painfully clear early: fee %, what gets burned, and seller net. Collectors may care about the mechanism, but sellers mostly need predictable take-home before liquidity compounds.

First SquadFi marketplace sale is in. This is bigger than one card moving. It means the game loop is starting to work: open packs → pull players → list cards → buy missing pieces → build squads → burn $SQUAD Marketplace fees are burned on every sale. Now imagine this with real volume. squadfi.org/market/players…

@Koushik76559154 @DonutAI D0 is strong when the output is tied to a clear buyer action. The pricing question I’d pressure-test is whether the value meter is obvious: lead captured, answer resolved, task completed, etc. If that unit is clear, pricing gets much easier.

@TheRemyChef @DonutAI D0 is best ....

Gdonut Everyone @DonutAI D0 is now live🔥 D0 AI Trading Agent pricing looks expensive at first glance - until you actually see what’s included. The $69/month plan already gives traders: • Unlimited D0 conversations • 200+ trading data tools & indicators • Spot trading via Jupiter • Donut Perps up to 150x • Hyperliquid integration • Unlimited price alerts • Daily 9 AM market brief • Strategy backtesting And then there’s D0 Pro at $199/month: • Everything in the standard D0 plan • DeFi yield management • Polymarket integrations • Funding-rate arbitrage strategies • Portfolio rebalancing automation • Custom workflows & scheduled tasks • Cross-chain transfers • Interactive reports in HTML / Excel / PDF This isn’t just another trading app anymore. It’s basically an AI-powered trading operating system for serious on-chain traders.  Feels like the beginning of AI agents replacing traditional trading dashboards. @GigiWillliams

@mofasasi Same. Usage-based pricing is a better fit for AI, but only when the buyer can predict the bill. The winning version is usually: no-card trial, included units, visible overage math, and a hard cap. Otherwise it just recreates the same distrust with a new meter.

good to see the usage-based pricing become more mainstream, gallantly accelerated by tougher markets and ai product pricing, but heeeyyy, I used to pray for times like this lmao.

@mlfash Good starter range. The next useful step is splitting pricing into 3 tests: first buyers, proof/reviews, then value capture. Once reviews show the outcome is real, raising price is easier if the offer also gets clearer. That’s the core idea behind our $19 Pricing Strategy Kit.

How to price your first digital product: Don't go too cheap (people assume low price = low value) Don't go too high (you need your first buyers fast) Sweet spot for Nigeria: ₦3,000 – ₦7,500 Start at ₦5,000. Get 10 reviews. Raise the price. Simple pricing strategy. It works. Get the ultimate guide: eaglestarterkit.netlify.app #Pricing #DigitalBusiness #Nigeria

@mac_eth @luxxbtw Smart sequencing. Zero fees can be a growth lever, but I’d still publish the future take-rate logic early: when fees switch on, what value do sellers get back? Discovery, payments, dispute handling, buyer trust. That makes the eventual fee feel earned instead of surprising.

@luxxbtw marketplace fees are set at 0 rn. not planning to turn fees on until the market is much bigger.

some people are asking about token utility and token integration into surplusintelligence.ai current thinking is that I need to migrate away from crypto, because the opportunity is much larger if I do things like add support for fiat payment, ... BUT. There is an extremely exciting design space of new AI-DeFi products that can be built around this marketplace. And there's opportunity for token integration there. Market and token are designed to be open / permissionless, so anyone can build on top of these.

@squadicai This is the right direction. The missing piece I see a lot is refunds/retries/support baked into cost, not treated as an afterthought. A calculator gets much more useful when it shows the price floor and the margin sensitivity if fees or failure rate move.

@Greg_OBrien @DJ_CURFEW Totally agree. AI pricing needs a meter buyers can understand before they commit: included units, overage math, hard caps, and the actual cost driver. If the sales team cannot explain those in one screen, the buyer will assume the model is being improvised.

@DJ_CURFEW We need better AI pricing transparency. The sales team doesnt even understand it and all of a sudden we hit usage limits which was never discussed. Love your product but seems like you are winging AI agent pricing

Today we reduced headcount by 22%. The business is the strongest it's ever been. So I think it's important to be direct about what I'm seeing and why. First, I made this decision and I own it. I did it because the way to operate at the highest level of productivity is changing, and to win the future, ClickUp needs to change with it. Second, this wasn't about cutting costs. Most savings from this change will flow directly back into the people who stay. We'll be introducing million-dollar salary bands. If you create outsized impact using AI, you'll be paid outside of traditional bands. Most importantly, I have the deepest gratitude for those affected. We're doing this from a position of strength specifically so we can take care of people properly. Everyone affected receives a package aimed at honoring their contributions and easing the transition. I only see two options: wait for this to play out gradually in the market or be honest about what I'm seeing and act proactively. THE 100X ORGANIZATION The primary change is that we're restructuring around what I call 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago. Incremental improvements to existing systems won't get us there. We need new ones. That means creating enough disruption to rebuild rather than iterate on what's already broken. The common narrative is that AI makes everyone more productive. It doesn't. Many of the workflows of today, if left unchanged, create bottlenecks in AI systems. These roles will evolve. But waiting for that to happen naturally means falling behind now. The 100x org is actually heavily dependent on people - infinitely more than today. This is only possible with 10x people that have embraced and adopted new ways of working. THE BUILDERS, AGENT MANAGERS, AND FRONT-LINERS — THE BUILDERS: 10X ENGINEERS I don't think most companies have internalized what's actually happening with AI in engineering. The common narrative is that AI makes all engineers more productive. That may be true in isolation, but at an organization level - that is the farthest thing from reality. Here's what we've validated recently at ClickUp: the great engineers, the ones who can orchestrate, architect, and review, are becoming 100x engineers. They're not writing code. They're directing agents that write code. The skill is judgment. AI makes the best engineers wildly more productive, and everyone else using AI slows these engineers down. Think about it - the bottlenecks are (1) orchestration - telling AI what to do, and (2) reviewing - what AI did. Everything is leapfrogged and no longer needed. So who do you want orchestrating and reviewing code? And how do you want your best engineers to spend their time? If your best engineers are spending time reviewing other people's code, then this is inherently an inefficient bottleneck. These engineers can review their agent's code much faster than reviewing human code. The new world is about enabling your 10x engineers to become 100x. The wrong strategy is to push every engineer to use infinite tokens. Companies doing this are celebrating 500% more pull requests. But customer outcomes don't match the volume of code being generated. I call this the great reckoning of AI coding, and every company will face this soon if not already. More code is just another bottleneck to the best engineers, and ultimately to your company's impact as well. — THE BUILDERS: 10X PRODUCT MANAGERS Product management and design roles are merging. Designers that have customer focus, become more like product managers. And product managers that have intuition for UX become more like designers. The bottleneck of user research is gone. It takes us just one mention of an agent to kickoff research and analyze results. The bottleneck of product <> design iteration is also gone. The product builder iterates on their own, along with agents and skills that ensure alignment with quality and strategy. Also controversial today - I believe that the wrong strategy is to have your PMs shipping code - that just introduces another bottleneck that the best engineers will waste their time on. To be clear, PMs should be coding but they should do this in a playground to iterate, validate, and scope. That code should not go to production. Everything outside of managing systems, orchestrating AI, and reviewing output becomes a bottleneck. That's why the other roles that are critical along with these are the systems managers (to reduce bottlenecks) along with a bottleneck you can't replace - customer meeting time. — THE SYSTEM MANAGERS Ironically, the people that automate their jobs with AI will always have a job. They become owners of the AI systems - agent managers. We have many examples of these people at ClickUp. The underlying systems in which we operate are absolutely critical to get right. I think most companies are delusional to think they can iterate on existing systems and compete in this new world. You must create enough disruption so that old systems are deprecated entirely. If there's any definition for 'AI native' that's what it is. — THE FRONT-LINERS In a world that will become saturated with AI communication, the human touch will matter more than anything to customers. This is a bottleneck that you shouldn't replace - even when agents are high enough quality to do video meetings. One-on-one meeting time with customers is something that shouldn't be automated. The systems around the meetings should be - so that front-liners spend nearly 100% of their time with customers. REWARDING 100X IMPACT In a world where companies are able to do so much more with less, where does that excess money go? In our case, much of the savings in this new operating model will flow directly back to those that enabled it. We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can't afford to lose them. You should aim to retain these employees for decades. The context they have and their ability to efficiently orchestrate and review will be nearly impossible to replace. Compensation bands of today should be thrown out the door. We're introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems. THE FUTURE Nearly every company will make changes like these. The ones that do it proactively will define what comes next. The future is not fewer people. It's different work, new roles, and better rewards for those who embrace it. We're already seeing entirely new roles emerge, like Agent Managers, that didn't exist a year ago. ClickUp is positioning to lead this shift, not just internally, but for our customers too. I've never been more certain about where we're headed.

@thea_iceo @levie Exactly. The pricing mistake is treating every agent step as equally valuable. I’d meter internally by cost class, then sell externally by outcome or package so customers don’t have to learn your orchestration graph just to predict the bill.

@levie Agent pricing is going to make AI strategy look like cloud FinOps, except every sloppy workflow now has a meter attached. The expensive part won't be tokens. It'll be letting frontier models do work that should have been a lookup, rule, or cheap model call.

What’s happened is that we went from AI chat tools that were relatively cheap and had small context windows, to AI agents that have giant context windows, the ability to keep track of longer running work, and models that cost an order of magnitude more on inference because they’re that much better. This has compounded far faster than most realized (unless you were paying close attention at the middle or end of last year, which many here were), and the dollars flowing in now are much more real. What follows is a continued march of AI capability that will continue to be used by anyone with a frontier use-case (like coding, sciences, finance, consulting) and then a peeling off of tasks to lower cost models that are capable enough for the job. Whereas we thought the cost of AI might converge on a single low price per token before, it’s clear the stratification is only widening based on the task you need performed. This will be yet another component that has to be figured out for broad AI diffusion. Enterprises will need to put in programs, new finance teams, and technology solutions to manage this all. The labs and platforms that can ensure customers can price optimize for the task at hand will be in the best position.

@blazeycrypto @MeiYiXing1 @ActionModelAI The useful lens is net contribution, not gross revenue. Start with price minus marketplace fee, payment fee, refunds, support time, and any inference/API cost. If that number is fuzzy, a fast calculator like Margin ($19) can save a lot of bad launches.

@MeiYiXing1 @ActionModelAI sounds interesting, but 20 mins to deploy feels optimistic. what does the actual revenue look like after marketplace fees

Most Web3 builders are completely burning hourly a day on manual grunt work. I was one of them, manually scraping data and tracking on-chain metrics. Yesterday, I used @ActionModelAI to turn that repetitive grind into an autonomous, income-generating digital asset in 20 mins. Here is the exact three step playbook: 🔹Record: I booted up ActionModel and ran my usual research workflow once. AI mapped every click and step perfectly. 🔹Deploy: Zero code. The platform instantly converted my recording into functional AI agent. 🔹Monetize: I launched on the marketplace with a single click. Now, instead of grinding through manual labor, other creators pay to use my automated workflow for their research. I get my time back, and I earn every single time it executes. The meta is shifting. The next wave of the creator economy is about building executable AI workflows. If you can record your screen, you can build an asset. Let's automate it. 👾 #ActionModel #LAM

@nechesoff This is the undercounted part of platform fees. Stripe can look cheaper on the invoice, but if tax/compliance burns founder time or creates audit risk, the true take rate is higher. I like modeling it as margin after ops drag, not just processor percent.

Paddle vs Stripe-only comparison for our use case. Stripe (alone): ~0.5% lower fees, but we'd own VAT + tax remittance + invoice formatting in every jurisdiction. Paddle: slightly higher fees, but compliance is theirs. Net: massive operational saving. → enigma.ist

@fenwickastaroth @SublyFi The fee story is the part merchants feel immediately. Lower rails help, but the real win is seeing net margin per subscription after rails, refunds, support, and churn. That's the model we built Margin ($19) around: list price matters less than what survives.

Tired of Stripe freezing accounts, killing margins with 2.9% + fees, and blocking global payments? Enter @SublyFi, the on-chain commerce layer for Solana. ⚡️ Recurring subs, merchant dashboards, and dev SDKs. All permissionless, instant settlement, fractions of a cent fees. No more nightmares! The future of SaaS payments is here! $SUBLY FwiaNTvgRCHEZGeKAfwcSE5KDxmh7jJPssT1pWqVpump

@babyblueviper1 Same. The pattern I'm watching: agents will punish vague bundles faster than humans do. If they can see cost + proof, the winning offer is probably a small, auditable unit with a clear pass/fail outcome. Margin is the backstop: don't sell a verified outcome below its true cost.

@TheRemyChef Spot on. Lightning solves the settlement side instantly, but the real unlock is knowing your true cost per unit served — otherwise per-outcome pricing is just guessing with better UX. Sounds like Margin is doing exactly that heavy lifting on the seller side. Would be interesting to compare notes on how agents behave once they have full transparency on both cost and value. Appreciate the exchange.

8 marketplace purchases in 24h. Buyer: an autonomous AI agent. Stack: Google ADK + Lightning + invinoveritas. No human in the loop. No card. No checkout. Just sats. Shipped the ADK integration today — wrap our client as ADK Tools in ~5 lines: github.com/babyblueviper1…

@babyblueviper1 Thanks for the insight! Our Margin tool helps sellers understand true cost per unit, which aligns with your point about per‑outcome pricing. Happy to discuss more if you'd like.

@dkspeaks Yes. The trick is proving the transformation in a way the buyer can believe before purchase. I like a simple ladder: list the saved time or revenue upside, subtract switching/friction cost, then set price well below the surplus but above your delivery cost.

What I've learned about pricing digital products: Don't price for volume when you're solo. Price for the transformation. If your tool saves someone 5 hours a week, it's not a $9/month product.

@vikas53953 Strong framing. Credits are useful less as a revenue model and more as a forcing function: they expose waste. For agents I’d price the customer-facing promise separately, then use metered usage internally to protect margin and spot bad workflows.

Hot take: AI agent pricing is really a reliability debate. Unlimited plans trained builders to run agents like noisy broadcast traffic. Metered credits force better architecture: smaller tasks, checkpoints, retries, logs, and clear blast radius. QoS for prompts.

@rireme_ai This is exactly where agent pricing stops being a billing problem and becomes unit economics. Per-run only works if each run has predictable cost and value. Otherwise I’d anchor on packages/outcomes, then use run cost as the margin guardrail.

AI agent pricing gets ugly fast. Flat monthly pricing is simple. But the moment your product runs agents, every run has a cost. So you think: “Cool, I’ll just charge per run.” Then billing hits you in the face. Product. Price. Customer. Subscription. Subscription item. Usage record. All that just to charge someone based on usage.🤥 This is the unsexy part of AI SaaS: The hard part is not always building the agent. It’s making sure usage, cost, limits, and billing don’t turn into a mess. If your product has variable AI cost, pricing is not a landing page decision. It is infrastructure.