Understanding AI Pricing and Its Effects on Users

I keep seeing the same pattern destroy SaaS companies: AI makes their customers insanely productive. Those customers need 80% fewer seats. Revenue falls off a cliff. The pricing model is literally eating itself. I've executed pricing transformations across 4 SaaS turnarounds. What worked 18 months ago now destroys value - every SaaS company is racing to embed AI, and it's breaking their revenue models. The automation paradox: AI makes customers wildly productive, so they need fewer seats. You just automated away your own revenue model. 85% of SaaS companies have abandoned pure per-seat pricing. The holdouts are learning why the hard way. Here's what actually works now: Track different data. Old way: Seats, tiers, revenue per account. New way: Token consumption, API calls, automated workflows. Found one enterprise using AI to replace 10 seats while consuming 100x the resources. Seat pricing misses this completely. Price outcomes, not access. Old way: ROI = human hours saved. New way: Automated resolutions, workflows completed. Saw $500/month AI running entire departments. Customer saves $2M annually. Your pricing is broken. Build hybrid models. Old way: Per-seat with usage tiers. New way: Base subscription + AI consumption. Example: $X base platform fee + $Y per 1,000 AI resolutions. Revenue jumps 3x. Churn drops. Value finally makes sense. Model the seat apocalypse. Old way: 20% churn assumptions. New way: Accounts dropping from 50 to 10 seats but 10x-ing AI usage. Price it right = 2x revenue. Miss it = -60%. Prove value first. Old way: Show features, hope they get it. New way: "Our AI resolves 1,000 tickets = 40 human hours." Now $2/resolution pricing clicks. Without proof, you're just taxing AI. CS becomes AI coaches. Script: "You're paying for 50 seats but AI handles 30 of those workflows. Let's optimize." Fewer seats, higher revenue. Trust wins. Real-time transparency. Token usage dashboards. Cost predictions. 80% alerts. Show exactly what AI costs vs human alternative. Black box pricing = dead company. Most SaaS companies still add 50% "AI premiums" to seat licenses. Meanwhile, Salesforce charges per conversation. Zendesk per ticket resolved. The leaders already moved. But the window's closing. Companies with consumption-based AI models report 38% higher growth. Foundation models commoditize by 2030. We have maybe 24 months. After that, it's a race to the bottom. The fundamentals from my 4 turnarounds still apply - but the game has changed. We used to price software that helped humans work. Now we're pricing software that replaces them. Get this transition wrong and you'll watch competitors eat your market share. Get it right and you own the next decade.

AI pricing feels chaotic. One fix follows. 👇 130 of 175 AI or founders selling Agents struggle to set prices, Kyle Poyar survey. Seat plus usage still dominate because buyers understand such structures, Sandhya Hegde analysis. Why the gap persists • Tokens mirror GPU spend, ignore buyer value • Seats align with headcount, agents run 24 × 7 • Usage tracks activity, overlooks results Case from a sales startup • AI agent handles 10 000 contacts each day • Seat plan costs about 50 dollars a month • Token plan costs 500 dollars a month • Outcome plan stalls in credit disputes Better unit: decisions Price every discrete choice the agent makes • Send follow‑up email • Route a lead • Approve refund Example • 10 000 decisions • Reply rate rises from 4 percent to 8 percent • 400 extra replies create 20 000 dollars in pipeline • Fee at 0.20 dollars per decision equals 2 000 dollars • Buyer sees 10 × return Questions for your team • How many decisions did our agents deliver last week • Which metrics moved after those decisions Price decisions, not infrastructure. Let's discuss.

AI pricing is broken. Not cute broken. CFO-nightmare broken. Every vendor’s got a shiny new “outcome-based” gimmick. 1. Intercom: per resolution 2. Decagon: per conversation 3. AirHelp: 35% success fee Sounds smart. Feels modern. Try buying it. ○ Vendor: How many conversations do you have per month? ● Customer: What’s a “conversation”? ○ Vendor: Good question. We define it. Actually... we’re still workshopping that. Tell you what, use the product for 60 days so we can figure out how you use it, and then we’ll tell you how we charge you for it. ● Customer: Sounds...confusing. Can we just go back to price per seat? ○ Vendor: No but we can offer you an annual pack of conversations! ● Customer: What if I go over? ○ Vendor: We charge you more. ● Customer: So how much will I actually pay? ○ Vendor: Depends. ● Customer: Perfect. I’ll have my CFO build our budget in Crayons this year. I read Kyle Poyar post last week asking: “Is outcome-based pricing the future?” The answer is no. Outcome-based sounds good, until you touch it →The definitions are fuzzy →The math is opaque →The costs are unpredictable And your CFO? One pricing convo away from rage-quitting. So let’s kill the theater and bring back common sense. You don’t pay humans per conversation. You hire them. The future is no different. You hire Cloud Employees—autonomous AI agents with defined roles, clear outputs, and a flat monthly cost. Just like a salaried human. Only approx 1/10th the price. →No per-unit billing →No conversation-based pricing →No budget gymnastics You hire for the job. • Need a junior SDR? Done • Need a seasoned rep? Upgrade • Need a CS agent that crushes Tier 1 tickets? Hire a Cloud Employee for that. You match the Cloud Employee to the job like you would a human, based on skill, speed, and scope. Then you train them, coach them, and let them run. That’s what we’re pioneering at Signals. This isn’t “AI pricing.” It’s AI employment. Predictable. Aligned. Scalable. Call it what you want. I call it the end of AI pricing games.

The Cursor and OpenAI bills are the new AWS invoice. Few executive leaders realize that most AI tool and agent subscriptions don’t come with unlimited usage. It’s not part of the initial cost estimate or budget. Pricing isn’t always clear either. AI and agentic tool vendors don’t make it easy to determine average monthly cost or provide simple usage calculators. Some don’t make it clear that subscription tiers aren’t really unlimited. Apps and APIs get throttled to the point where they become useless without buying more credits or requests. At that point, it becomes a hamster wheel. Once startups realize how powerful AI vendor lock-in is, they are quick to raise prices. Businesses that lay off employees become dependent on the AI vendor for mission-critical business operations. Today’s VC subsidized pricing won’t last long as startups focus on profitability vs. growth and gaining market share at all costs. Watch this become a growing problem with adoption in the coming months.

AI knows LOTS about you. And it's about to set the prices YOU, personally, pay... One of the early movers in AI pricing is Delta Airlines. They plan to expand AI-personalized pricing from 3% to 20% of tickets by year's end. Their president told investors: "We will have a price that's available on that flight, on that time, to you, the individual." Customer Reaction: "Wait, WHAT?" Translation: The algorithm has calculated how much you're likely to pay. Profit-wise, it's working. It's producing "amazingly favorable unit revenues." But what about the customers on the other side of these transactions? Seems like a zero-sum game. Delta's AI knows you. Your credit score. Purchase history. Loyalty status. That discount you almost clicked. How many times you checked the price. Whether you're on an iPhone or Android. Lots more. Here's the psychology they're missing: We're hardwired for fairness. Nobel winner Daniel Kahneman showed people will actually reject profitable deals if they feel unfair. They'll even pay extra to punish companies they perceive as predatory. When customers find out they paid more because AI analyzed their "willingness to pay," trust dies. This isn't yield management where everyone understands prices vary by timing and open capacity. This is weaponized information asymmetry that makes used car dealers look transparent. (More on that in my Forbes CMO Network article, linked in comments.) The irony? Short-term revenue gains could trigger long-term loyalty collapse. Customers who feel manipulated don't just leave. They tell everyone why they left. What's your take: Is AI-personalized pricing the future of commerce or a trust-destroying mistake? Is there a right way to do this? #CustomerPsychology #AIpricing #CustomerExperience #PricingStrategy

AI pricing is absolutely bonkers And customers are paying the price The pricing for these addons are as follows: 💸 Slack: 80-133% additional spend 💸 Salesforce: 25-40% additional spend* 💸 Gmail: 111-333% additional spend 💸 O365: 90-111% additional spend 💸 Notion: 50-100% additional spend 💸 GitHub: 48-250% additional spend 💸 Zoom: 0% additional spend 👀 Pricing has a tendency to increase over time, while expense to operate decrease. So if this is the initial benchmark, there's a large profit opportunity ahead. But at who's expense? If you are a company of 100 and you pick just two of these tools to increase productivity will hit you at $50k a year, a company of 1000 and you're looking at $500k. If we're improving productivity as the rational and we typically measure productivity with employee expense, this means we must recoup this cost by reducing head count. Which in the above example we can say 1 to 10 employees. But is that truly realized productivity? Is a little bit of productivity spread over a lot of people better than a little bit of unproductivity spread over a lot of people? We've been sold the story of how AI will bring order to our lives and free up time, but in reality the corporations are using it as a gold rush or outright cash grab. And if these large companies are squeezing their customers for AI budget, what remains for the disruptive companies trying to enter the market? This is an interesting juncture in spending and maybe a bit of a bubble in store. What do you think? Is pricing for AI on the market correct? #salesforce #ai #salesforceadmins #gai #businesssystems #saas #salesforcearchtiects #github #google #o365 #zoom #slack * it's difficult to calculate Salesforce pricing because you can get multiple AI packages and there are a lot of discounts off MSRP that could affect overall pricing increase

AI's Buy & Sell Side Impacts: Crafting Smarter Pricing Strategies with AI Many SaaS companies charge based on the number of user seats. More users meant more revenue, simple. However, as AI and automation become more integrated into SaaS products, we're seeing interesting shifts in how people use these tools. With AI and automation enhancing SaaS products, the number of users may decrease for certain tasks. However, the AI capabilities allow the remaining users to be far more productive and gain significantly more value from the software. By using AI-powered tools, these users can accomplish much more. So, if you find yourself in this situation, how do you price your services to match the greater value that AI brings? The SaaS world is at a turning point. The old per-seat pricing model may not quite fit the nuanced value that some AI-powered solutions now offer. As automation changes how tasks get done and AI deepens user engagement, we need to rethink how we price our products to meet our business goals. The table below shows some options: per-seat, hybrid models, and usage-based pricing. Some considerations in changing pricing: 1) Align with real value - Make sure your pricing matches the actual value users get and perceive. 2) Adapt to new usage - As AI transforms workflows, pricing should adapt to how people use your platform differently. 3) Stay competitive - Flexibility and perceived value are crucial. Don't get left behind. 4) Meet diverse needs - Cater to a wider range of user preferences and requirements. 5) Encourage behaviors - Use pricing to incentivize desirable user engagement. 6) Hit financial goals - Balance user value with revenue growth and stability. AI isn't just changing SaaS products; it's giving us powerful tools to help us make decisions. You can leverage AI for research, data analysis, what-if scenario planning, and collaboration to inform pricing strategy. If you haven't explored this, now's the time. What are your thoughts? Feel free to DM if you want to collaborate on this. #PricingStrategy #SaaS #AIUseCase #AIAnalytics #ChatGPT GrowthPath Partners

SaaS charged you for seats. AI agents will charge you for outcomes. This shift is massive. And it's happening fast. In SaaS: You pay a flat fee per user/month – regardless of usage or value delivered. Even if no one logs in, the meter runs. With AI agents: You pay only when value is created. → Calls handled → Tickets resolved → Refunds processed → Demos booked The economics shift from "access" to "outcome." No more paying for software that sits idle. This also makes ROI brutally clear. If an AI agent processes 5,000 refund requests/month with zero errors and 40% cost savings vs humans – the value is visible instantly. That’s why outcome-based pricing is not just fairer -- it’s stickier, scalable, and aligned with business goals. The best part? You don’t need to train your team to use new tools. The agent just plugs into your systems and gets to work. SaaS was a tool. AI agents are the workforce. And you don’t pay a workforce by the seat. You pay them for what they deliver. The future is outcome-priced. Welcome to Agent-as-a-Service. --- At SigmaMind AI (YC S22), we focus on delivering outcomes for our customers. If you’d like to build/deploy AI agents for customer service, lead gen, admin tasks, etc. check out some of our case studies. Link in comments.

🚀A Race Not Just for the Top, But for Value Recent insights from artificialanalytics.ai have shed light on the dynamic and competitive terrain of LLMs. Here are some of my observations and opinions: 🔍 The Quality vs. Price conundrum: High-quality models like GPT-4 are facing a pricing existential risk. With prohibitive costs per token, even the most powerful model risks becoming sidelined and of academic interest. It's clear that maintaining the lead on quality is not enough—value for money is the game to play. OpenAI's recent and consistent price cuts are indicative of the fact that $ per token is extremely important and quality is not enough. 🆓 OSS models are revolutionizing the pricing game, marching towards near-zero cost per token and not compromising on quality. This trend is a wake-up call for proprietary models: the intersection of high quality and low cost is the sweet spot. Long term cost of intelligence will become ZERO and commoditized. 🌐 OpenAI's Ecosystem Play: Foundational model monetization will become non-viable, OpenAI's already shifting its focus and cultivating a robust ecosystem. Developer tools, enterprise solutions, marketplace, and an unparalleled first-party user experience (ChatGPT). In essence, OpenAI's future hinges on its ability to become a comprehensive AI platform company. ⚡ Quality vs. Throughput: Speed is non-negotiable. The enterprise demands AI that's not just smart and safe, but also swift. Slow responses are out; efficiency is in. GPT-4's turbo variants, despite their prowess, must up their game in throughput to meet the enterprise adoption. 🤖 The Bottom Line: It's a race to the top left corner of the graph—where high quality meets low cost and high throughput. What are your thoughts on this evolving AI market? More interesting visuals can be found on https://lnkd.in/eBq8s6Md #AI #OpenAI #LLMs #AIPlatforms #GenerativeAI #AITrends #Innovation

AI is making it possible for businesses to do more than ever before. But not everything you can do is good for your customers, or your business. Take Delta, for example. They are rolling out AI-driven dynamic pricing. From a consumer perspective, that is unsettling. Every time someone goes to buy a ticket, the thought creeps in: “Am I paying a fair price, or is an AI model unfairly extracting dollars based on what it thinks I am willing to pay?” That perception matters. So, what should Delta do? No one at Delta asked me, but as a long-time Delta flyer, I won't necessarily stop flying them just because they use AI-based pricing. But what I would like in return is transparency. Inside the SkyMiles app, show me what customer pricing group I fall into. Tell me whether I am in the third decile, or the eighth. Give me something to evaluate the tradeoffs between my loyalty and the prices you are asking me to pay. If your business is using AI to create new opportunities, focus on building trust, not eroding it. Use AI to create value with your customers, not extract value from them. Otherwise, churn might be just one AI-powered search away.