How to price your AI product in a post-SaaS world? | This is what raising $1.5M really looked like: 250 meetings & 171 rejections.
What people really want from AI? - Anthropic Survey.
đ Hey, Sahil here - Welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrowâs companies.
How to price your AI product in a post-SaaS world?
What people really want from AI? - Anthropic Survey.
This is what raising $1.5M really looked like: 250 meetings, 171 rejections in 98 days.
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đ DEEP DIVE
How to price your AI product in a post-SaaS world?
Most founders are thinking about AI pricing completely wrong.
They obsess over features, distribution, and even model performance, but treat pricing like a packaging decision. In the AI era, pricing isnât packaging. Itâs a strategy. If you get it wrong, your growth can look impressive while your margins quietly collapse.
This AI pricing playbook from Bessemer Venture Partners lays out something many founders donât want to admit: AI businesses are structurally different from SaaS, and that changes how you monetise.
AI broke SaaS economics
In classic SaaS, marginal cost was almost zero. Once the software was built, adding one more user barely cost anything. Thatâs why founders could aggressively chase ARR growth and worry about margins later.
AI changes that equation. Every query carries real costs:
Inference and computation
GPU usage
Model upgrades
Sometimes human review
Ongoing support overhead
This means gross margins in AI often sit in the 50-60% range (sometimes lower), compared to 80â90% in SaaS. If your pricing model doesnât reflect this from day one, scaling only amplifies the problem.
One practical test: if your unit economics donât work with 10 customers, they wonât magically work with 1,000. Many startups scale into negative margin territory because they never fully modelled their true costs, including founder time and hidden support work.
Thatâs the first mindset shift: pricing must absorb cost reality while capturing value.
The real strategic choice: what are you charging for?
Most AI founders jump to usage pricing because it mirrors their cost structure. But pricing isnât about your costs, itâs about how customers perceive value.
Across Bessemerâs research and portfolio companies, three AI-native business models are emerging. Each one changes how value is delivered, and therefore how pricing must work.
Before we go deeper into charge metrics, hereâs how the landscape is actually shaping up:
Consumption-based (tokens, API calls, usage)
Clean and predictable for infrastructure companies. Works well for technical buyers who understand granular usage. But non-technical customers donât think in tokens; they think in outcomes.
Workflow-based (per task completed)
Charging per document drafted, per ticket resolved, per case processed aligns better with how businesses measure work. It reduces translation friction and makes ROI clearer.
Outcome-based (per successful result)
The boldest version. You get paid only when the job is done. Intercom charging per AI resolution is a strong example - not per message, but per resolved ticket.
As you move from consumption â workflow â outcome pricing, you accept more cost variability but gain stronger alignment with customer value.
The best founders donât choose whatâs easiest to implement; they choose what customers emotionally understand and are willing to pay for.
The soft ROI trap
Hereâs where many AI companies will struggle in 2026.
Copilots that âassistâ but donât close the loop create soft ROI. They help, suggest, draft, but donât finish the job. When renewal time comes, customers ask: âDid this actually move the needle?â
Agentic products that fully complete workflows have stronger pricing power because their ROI is measurable.
You can think of AI products across two dimensions:
Revenue generation vs. cost reduction
Hard ROI vs. soft ROI
Products sitting in hard ROI territory, clear revenue uplift or measurable cost savings, can justify premium pricing. Soft ROI products will face budget scrutiny once the hype fades.
Thatâs a practical insight founders should act on now, not later.
Hybrid pricing: the early-stage advantage
For most startups, especially early on, hybrid models are a smart middle ground:
Base platform fee (predictability for the customer)
Plus usage or outcome-based expansion (upside capture)
This protects you from unpredictable compute spikes while allowing revenue to scale with value delivered. It also gives enterprise buyers budget clarity, which matters more than founders assume.
Hybrid models are not a compromise; theyâre a hedge against uncertainty in both demand and cost structure.
Pricing shapes your entire company
One of the strongest insights: pricing isnât a finance decision. It determines how your company behaves.
If you charge per outcome:
Sales sell results, not seats
Customer success focuses on realised value
Product optimises for completion and accuracy
Engineering prioritises reliability
If you charge per seat:
Teams may optimise adoption over actual impact
Value conversations get diluted
Pricing becomes your operating system.
How to actually find your price
Most founders undercharge. Not because they lack data, but because asking for more feels uncomfortable.
The playbook recommends something counterintuitive: find your pricing through friction.
Start with a price.
If customers immediately say âyes,â youâre likely to be too cheap.
Raise incrementally until you hear âwe need to think about that.â
Stop just before it becomes a blocker.
That tension zone is often where true willingness-to-pay lives.
And donât default to cost-plus pricing. If youâre creating $100K in measurable value and charging $12K because it âcovers compute,â youâre anchoring to cost instead of value.
The bigger shift is underway
Every major software shift changed monetisation:
Client-server monetised licenses
SaaS monetised access
AI is monetising outcomes
AI isnât just another feature layer. In many cases, itâs replacing or autonomously completing work. That means charging for access alone underestimates what your product is actually doing.
The founders who win in this era will be the ones who design pricing systems that reflect:
Real compute economics
Clear customer ROI
Scalable expansion logic
Operational simplicity
AI pricing is not about clever billing mechanics.
Itâs about making sure your product earns what itâs worth and that your margins survive long enough to compound.
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đ QUICK DIVES
What people really want from AI? - Anthropic Survey.
Most conversations about AI get stuck in abstractions. We hear big claims about job loss, AGI, productivity, safety, and existential risk, but much less about a simpler question: what does âAI going wellâ actually look like for real people already using it?
Anthropic recently ran one of the most interesting studies Iâve seen on this. It invited Claude users to describe how they use AI today, what they hope it could unlock in their lives, and what worries them most. More than 80,000 people across 159 countries and 70 languages participated, which makes this less like a normal product survey and more like a global snapshot of how people are beginning to fit AI into everyday life.
What makes the findings valuable is that they move beyond generic optimism or fear. They show where AI is already delivering value, where it still feels shaky, and what kinds of products or companies are likely to matter next.
The biggest thing people want from AI is not magic. Itâs a relief.
When Anthropic asked people what they most wanted from AI, the answers were surprisingly grounded. The top categories were:
That tells you something important. Most people are not asking AI to become some futuristic super-being. They want it to help them do better work, reduce mental overload, manage life logistics, learn faster, and reclaim time.
Thatâs a useful correction for founders. The most immediate demand is not for âAI for everything.â Itâs for products that remove friction from real life.
A lot of the strongest responses had the same emotional structure underneath them:
AI helped me do what I already needed to do, but with less stress and more breathing room. Someone used it to reduce documentation burden at work. Someone used it to learn coding despite a learning disorder. Someone used it to ask questions they felt embarrassed asking other people.
So, the best AI products may not be the ones that feel the most futuristic. They may be the ones that quietly remove cognitive load.
Work is still the main wedge, but quality of life is the real promise
The largest category people mentioned was âprofessional excellence.â That makes sense. Work remains the easiest entry point for AI because the ROI is easier to spot. If AI saves time, improves output, or helps someone get through repetitive tasks faster, the benefit is obvious.
But whatâs more interesting is what sat underneath that answer. Many people started by talking about productivity, then revealed that what they really cared about was what productivity enabled outside of work: more time with family, more emotional energy, less admin, less mental clutter.
That distinction matters.
A founder might think theyâre building a productivity product, when in reality the real value proposition is:
more time freedom
less anxiety
less friction in daily life
more space for better work and better living
That is a much richer product insight than âhelp users move faster.â
AI is already delivering in a few very specific ways
When Anthropic asked whether AI had already taken a step toward usersâ goals, 81% said yes. Thatâs a meaningful number. But the shape of delivery matters more than the headline.
The main buckets where people felt AI had already helped were:
This mix is fascinating because it shows that AI is not just being used as a faster search engine. It is already functioning as a kind of thought partner, teacher, translator, research assistant, and in some cases, even an emotional support layer.
Some of the strongest responses came from people who felt AI gave them access to things they previously couldnât reach. Not because the knowledge didnât exist, but because the format was inaccessible, judgment-heavy, too expensive, or too difficult to navigate. Thatâs a powerful clue about where AI creates the most real value: not just speed, but access.
For builders, that opens up a big product opportunity. AI seems especially strong when it does one of three things:
reduces intimidation
removes judgment from learning
translates complexity into something people can act on
Thatâs a better lens than simply asking whether a task can be automated.
The most common fear is not âAI takes over the world.â Itâs that AI becomes unreliable at exactly the wrong moment.
Public AI debates often drift to dramatic long-term fears. But the top concern in this study was much more immediate: unreliability.
Users worry that AI will sound convincing while being subtly wrong. In some categories, thatâs a mild annoyance. In others, itâs dangerous. Law, finance, healthcare, and government workers especially raised this concern because the cost of a confident error is high.
This is a critical insight for founders. Reliability is not a ânice to haveâ layer you add later. In many AI products, it is the product.
Other major concerns included:
Whatâs striking is how practical most of these fears are. People are not mainly debating whether AI is philosophically good or bad. They are asking whether it will make them less employable, less independent, less thoughtful, or too dependent on a system that is always available.
The real story is not optimism vs pessimism. Itâs tension.
One of the best concepts in the report is what Anthropic calls the âlight and shadeâ of AI.
The same capabilities people love are often the ones they fear. For example:
AI helps people learn faster, but they worry it may weaken their own thinking.
AI saves time, but they worry it increases expectations and speeds up the treadmill.
AI offers emotional support, but they worry it could become a substitute for human connection.
AI creates economic opportunity, but it also raises fear about displacement.
That is a much more honest frame than dividing people into âAI believersâ and âAI sceptics.â Most users are both at once. They see upside and risk together.
Thatâs an important product lesson. Great AI products will not win simply by maximising usefulness. Theyâll win by managing the tension around usefulness. The winners wonât just provide capability. Theyâll create trust, boundaries, and clarity around how the product should fit into a personâs life.
The regional split is worth paying attention to
The report also found a noticeable pattern across geographies.
Users in lower- and middle-income countries were generally more optimistic about AI than users in wealthier regions. In many emerging markets, AI is seen less as a threat and more as a ladder, a way to start businesses, access education, or overcome infrastructure gaps.
That matters if youâre building global products.
In richer markets, AI is often framed around life management, overload reduction, and economic anxiety. In many developing regions, it is framed more around entrepreneurship, learning, and access. Same technology, very different emotional job-to-be-done.
Founders who understand that distinction will position themselves much better across geographies.
For founders -
The strongest message from this report is that people do not just want faster outputs. They want better lives.
That sounds obvious, but it is surprisingly easy to forget when building in AI. Many teams optimise around what the model can do instead of what the user is trying to become. Anthropicâs findings suggest that the most enduring AI products will be the ones that sit at the intersection of usefulness and human aspiration.
The near-term winning categories likely look less like vague general-purpose intelligence and more like products that help people:
do meaningful work with less administrative burden
learn without fear or embarrassment
manage overloaded lives
turn curiosity into action
gain economic leverage
access systems that previously felt closed off
At the same time, founders need to respect the downside. If your product saves time but increases dependency, gives emotional support but weakens real relationships, or accelerates output while making judgment worse, users will eventually feel that tension.
Thatâs why this study is so useful. It reminds us that AI is not entering a vacuum. It is entering messy human lives. And the companies that win wonât just be the ones with better models. Theyâll be the ones who understand what people are actually trying to protect, improve, and reclaim.
This is what raising $1.5M really looked like: 250 meetings, 171 rejections in 98 days.
Most fundraising stories you see are clean.
âRaised $2M in 2 weeks.â
âOversubscribed round.â
âStrong investor demand.â
What you donât see is the messy middle.
Matija Sonic shared what actually happened behind the scenes: 250+ meetings, 171 rejections, 24 ghosted, and just 17 investors closing the round over 98 days. Even after YC Demo Day generated 100+ investor leads, not a single one invested.
Thatâs the real starting point most founders donât talk about.
Fundraising is not one big moment. Itâs a system.
The biggest mistake early founders make is treating fundraising like a few high-stakes conversations.
In reality, it behaves much more like sales.
212 investors contacted
250+ meetings run
constant follow-ups, tracking, refining
If you donât treat it like a pipeline, you lose control of it. The founders who struggle the most are usually the ones approaching it casually, reacting to meetings instead of building a system around them.
Your first 50 meetings are basically practice
They walked into early meetings expecting quick closes.
Instead, most investors didnât understand the product at all. It was technical, pre-revenue, and they had no strong network advantage.
But something important happened around meeting #50.
The pitch started improving, not because they rewrote it overnight, but because repetition forced clarity. By meeting #100, both founders knew the pitch deeply, could anticipate objections, and communicated with confidence.
That shift is underrated.
Confidence in fundraising doesnât come from preparation alone. It comes from exposure. You earn it by sitting through uncomfortable conversations until your thinking sharpens.
The mindset shift that changes everything: chase Noâs
Around 50 meetings in, rejections started piling up, and morale dropped.
Then came a simple but powerful reframe: stop chasing yeses. Start chasing noâs.
Set a target, like 100 rejections.
Why this works:
Noâs are predictable and within your control
They remove emotional pressure from each meeting
They give you a clear sense of progress
If you havenât hit your rejection target, it likely means you havenât spoken to enough investors.
This turns fundraising from an emotional rollercoaster into a process you can actually manage.
The âvalley of deathâ is real
For nearly 2 months, they were stuck around ~$300K. No momentum. No visible progress. Others were closing rounds.
This is where most founders quit, not because of rejection, but because of silence.
What they didnât see: their lead investor was doing deep diligence behind the scenes. Fundraising often feels dead⌠right before it breaks open.
Then everything compounds at once
After weeks of slow progress:
Investors started closing faster
urgency kicked in
The round filled quickly
It became oversubscribed
This is how most rounds actually work: slow â stuck â sudden acceleration
What founders should actually take from this
Donât try to convince non-believers - find investors who already understand your space
Warm intros > random inbound (Demo Day interest is often a low signal)
Treat fundraising like a pipeline, not conversations
Track pitch quality, not just money raised
Those 250 meetings werenât just about raising money. They made them better founders.
Because in the end, fundraising isnât just about getting a yes. Itâs about becoming someone who can consistently earn it.
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250 meetings and 171 rejections for $1.5M is the fundraising reality nobody posts about. great issue.