# Marcos Rivera

Marcos Rivera advises B2B software companies on pricing and monetization, including how to price and package AI products.

- Profile: https://b2binfluencer.marketing/creators/marcos-rivera/
- LinkedIn: https://www.linkedin.com/in/marcoslrivera
- Median of last 8 posts: 23 reactions, 10 comments (checked 2026-11-02)
- Topics: artificial-intelligence
- Based on 8 dated posts, latest 2026-09-28

## Summary

Marcos Rivera is most useful for explaining and testing AI pricing and monetization choices for B2B software buyers.

- Best for: A B2B software company moving an AI feature to usage-based pricing and buyer communication.
- Not for: Consumer brands, broad B2B brands with no pricing angle, or high-volume paid influencer placement.
- Check first: Ask for recent audience survey results or documented job roles and seniority.

### Takeaways

- Usage-based billing is not automatically fair or predictable, Rivera says.
- Before shipping usage-based pricing, ask if buyers can estimate bills and handle usage spikes.
- Credit models need fewer, clearer billable actions so buyers can anticipate cost.

## Main ideas

- **Seats are not dead; lazy seat pricing is**: Rivera argues that AI has not eliminated seat-based pricing, and that claims that seats are dead are often provocations for engagement. What fails is charging for seats without examining what the product does. If the product works fine with no human involved, consumption or usage pricing fits. If a person uses, accesses, or collaborates with it, user-based pricing or a hybrid of seats plus usage fits. Rivera warns against swinging the pendulum from everyone charges for seats to everyone abandons seats.
- **Measure usage before choosing a pricing metric**: Rivera's first question to CEOs who say they need to charge for AI features is who is actually using them. The answer is usually silence. Rivera cites a Revenera survey of 415 software leaders: 96% had shipped AI, almost half said customers barely use it, and only 7% reported losing seats to AI. A Benchmarkit survey of 296 buyers found 89% went over their AI budget, mostly because AI features drove more usage than planned. Rivera's conclusion is that vendors cannot see usage and buyers cannot forecast it, so no pricing metric fixes the blind spot. Measurement comes first; otherwise a team cannot tell a feature nobody values from one it packaged badly.
- **Predictable total cost beats a low entry price**: Rivera repeatedly cites an AI pricing study built with Benchmarkit: 68% of AI buyers rank predictable total cost in their top three priorities, while only 19% care about a lower entry price. Rivera uses Atlassian's expansion of usage-based pricing around AI to argue that leading with visibility and spend controls is the right instinct, but visibility is not predictability. Rivera says any team shipping a usage-based model should answer three questions: can a buyer estimate their bill before signing; what happens when usage spikes, whether a hard cutoff, soft cap with alerts, or silence until invoice; and does the metric track customer value or just infrastructure cost?
- **Credit models need fewer units and more outcome shapes**: Rivera calls many AI credit models electric bills with a nicer name because they become pure pay-per-use billing. Rivera quotes Emily Sanz, who argues a well-run credit model is a flavor of outcome-based pricing that tracks multiple outcomes instead of one. Rivera also quotes Steve Inman, who compares it to a hot shower: the outcome is the completed shower, while usage is the kilowatts to get there. Sanz extends the analogy to a faucet, asking what it means to charge every time someone turns it on when nobody knows how many times a day they do. Rivera cites buyer research in which 55% said credit and token pricing is harder to evaluate for AI than for traditional SaaS. The fix Rivera endorses is a credit model with clear definitions and fewer definitions that look more like outcomes, since fewer actions make consumption more predictable.
- **Three reasons AI breaks old seat pricing**: Rivera lists three structural reasons old pricing models fail in the AI era. First, adoption volatility: AI delivers swings between high value and no value, far less predictable than SaaS. Second, real cost structure: LLM token costs are significant and constantly shifting, unlike the near-zero marginal cost of adding a SaaS user. Third, blurred attribution: the line between human and system contribution is unclear, complicating value capture. Rivera says these break at the same time, but repeats that seats can still be part of the answer for many companies; they just cannot carry the whole model once AI does real work inside the product.
- **Different buyer sizes fight different pricing battles**: Using a survey of 292 B2B software buyers for an AI pricing report, Rivera segments buyer priorities by company size. At companies under $50M in revenue, 77% rank predictable total cost as a top-three priority when evaluating an AI vendor; that drops to 63% above $1B in revenue. Meanwhile, ease of justifying spend to finance and the CFO climbs from 44% to 55% across the same range. Rivera reads this as smaller teams trying to guess the bill while larger teams already know they will spend and need help defending that spend internally. Rivera connects this to more than 480 pricing engagements and says the budget conversation never disappears; it moves from what will this cost us to can we justify what we are already spending.
- **Research data is the engine of the commentary**: A large share of Rivera's public posts rest on published research rather than opinion alone. Rivera co-produces an AI Pricing Report with Benchmarkit and cites its findings across posts on predictable cost, credit models, buyer-size differences, and usage measurement. Rivera also references a Revenera survey of 415 software leaders and a base of more than 480 pricing engagements. Several posts end with an invitation to download the full report. This makes research a recurring priority: the argument is expected to come with buyer data, not just a framework.
- **Handwritten notes are how conviction gets built**: Rivera describes keeping pages of handwritten pricing notes, each starting as a question he could not answer yet. Rivera uses AI tools but draws a distinction: a prompt can produce an answer in seconds, while conviction only shows up after wrestling with the problem. Paper slows Rivera down enough to work out what Rivera actually believes, and the crossed-out parts are where Rivera is wrong on paper so as not to be wrong in front of a client. Rivera closes by calling pricing the ultimate expression of business strategy and says you cannot copycat or prompt your way into a strategy.

## Work to read

- [Three questions to answer before shipping usage-based pricing](https://www.linkedin.com/posts/marcoslrivera_68-of-ai-buyers-rank-predictable-total-cost-activity-7508200989942976512-t6BS)
- [Measure first: the AI features you can't see are the ones you can't price](https://www.linkedin.com/posts/marcoslrivera_we-need-to-start-charging-for-our-ai-features-activity-7521639533763944448-EIks)
- [Credit models: fewer units, closer to outcomes](https://www.linkedin.com/posts/marcoslrivera_too-many-ai-credit-models-are-electric-bills-activity-7518710262447738880-c-AV)
- [Three reasons seats can't carry the whole model anymore](https://www.linkedin.com/posts/marcoslrivera_old-pricing-models-are-failing-in-the-ai-activity-7521964212672344065-7gzL)
- [Seats aren't dead: where the seat-versus-usage pendulum lands](https://www.linkedin.com/posts/marcoslrivera_the-narrative-is-running-past-reality-thats-activity-7517970374723051520-g6hR)
- [Small buyers want a predictable bill; large buyers want a defensible one](https://www.linkedin.com/posts/marcoslrivera_your-smallest-ai-vendor-conversations-and-activity-7508555969547694082-TBhz)
- [Handwritten pricing notes: how the recommendations get made](https://www.linkedin.com/posts/marcoslrivera_some-people-collect-sneakers-i-collect-activity-7523058987714478080-FUPB)

## Brand relationships

- **Pricing I/O** (disclosed_partnership)
- **Currv** (disclosed_partnership)
- **Benchmarkit** (disclosed_partnership)
- **The AI Report** (disclosed_partnership)
- **Camino Search** (disclosed_partnership)
- **EisnerAmper** (disclosed_partnership)
- **Pavilion** (mention)

## Partnerships

The material reviewed shows Marcos Rivera working with research partners and event hosts, but it does not document a sponsorship or paid partnership offer. His posts name Benchmarkit, The AI Report, and Revenera as survey and research collaborators on AI pricing studies, and describe executive dinners he hosted, co-hosted, or led a table at, including one co-hosted with Camino Search and EisnerAmper and one hosted by Sherri Sklar during the Pavilion GTM 2026 conference. These are reported as professional conversations and joint research, not as paid placements, and no rate card, media kit, audience demographics for buyers, sponsorship policy, or disclosure language appears in what was available. A brand planning around this person should therefore not assume a purchasable ad slot or sponsored post. The clearest supportable route is co-producing research or an executive event alongside his pricing work, since that is the collaboration pattern his own posts describe. Any terms, exclusivity, deliverables, or measurement would have to be agreed directly, because nothing in the reviewed material establishes them. Treat the absence of a documented sponsorship format as an evidence gap to close in conversation, not as a statement that no such arrangement is possible.

## Alternatives

- Dhaval Moogimane: Moogimane is the stronger choice when the campaign has to land an AI product inside large enterprise accounts and the buyer task is really about the go-to-market motion and stakeholder sign-off, not the pricing metric. His reviewed posts frame AI pricing as a cross-functional problem: providers have to weigh outcome-based pricing against margin risk, build guardrails on AI-related costs, and treat pricing as a product, sales, finance, and legal issue, while buyers are managing tool sprawl, chasing transparency, and trying to keep flexibility. A second post promotes a session with ZoomInfo and Google on why go-to-market AI pilots stall, why data quality and context matter, and how teams move from experimentation to business outcomes. For a B2B software company launching usage-based AI pricing into enterprise accounts, that material speaks to the conditions that decide whether the product is adopted at all: who signs off, whether the data underneath is ready, and how buyers govern spend across overlapping tools.
- Rebecca Martins: Martins is the stronger choice when the buyer's first question is what comparable companies actually pay, or when the campaign needs to reach procurement and finance teams who negotiate software contracts. Her reviewed posts describe SpendHound benchmarks drawn from a 10,000+ vendor dataset based on what more than 1,500 companies pay, now available inside Zip's procurement workflow, and a ProcureCon East push inviting procurement teams to compare their SaaS, AI, and cloud pricing against those benchmarks. For a B2B software company rolling out usage-based AI pricing, that is the purchasing-side evidence a prospect asks for when they want to know whether the number on the contract is normal. It answers a different part of the same campaign decision: not how to design the meter, but what the market pays and how procurement will test the price.
- Charles Howe: Howe is the stronger choice when the buyer task moves from pricing the AI feature to operating AI inside finance workflows, and the prospect's blocker is approval and control rather than the price metric. His reviewed posts argue there is a wide gap between how AI in finance is discussed and how it is used: named agents ship in every major ledger and most CFOs report some agentic use, yet around two-thirds of AP teams still key invoices by hand, and most finance teams have caught AI fabricating data. The pattern Howe describes is deterministic systems carrying repeatable volume, AI handling judgment calls, and a human approving before money moves. A second post covers MakersHub's rebuilt approval workflow, where rules combine conditions such as vendor, project, entity, and characters inside an invoice number. For a software company selling AI into finance, that is the operational argument that decides whether finance will let the system touch the ledger.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/