# Stakh Vozniak

Stakh Vozniak writes and speaks about building Cargofy, a company using AI agents to run logistics work such as freight tenders, bids, and shipment tracking. Vozniak also discusses how to price AI workers for logistics businesses.

- Profile: https://b2binfluencer.marketing/creators/stakh-vozniak/
- LinkedIn: https://www.linkedin.com/in/stakh
- LinkedIn followers: 6,147
- Topics: artificial-intelligence, supply-chain, startup
- Based on 11 dated posts, latest 2026-10-06

## Summary

Stakh Vozniak is most useful for practical AI pricing and operations in logistics.

- Best for: Logistics technology companies selling to shippers or forwarders, for speaking or advisory.
- Not for: Consumer fashion, home, or lifestyle brands or creative studios seeking paid ambassadors.
- Check first: Confirm follower demographics, job function, seniority, company type, and contact route.

### Takeaways

- Price AI workers in operational units such as tenders closed, not tokens.
- Reject per-seat pricing because it does not match the work.
- Estimate costs from actual monthly volumes with a floor and ceiling.

## Main ideas

- **Price AI workers in the customer's operational units, never in tokens**: Stakh Vozniak argues that budgeting for an AI digital worker is nothing like buying SaaS, yet most logistics executives still budget for AI the SaaS way. Per-seat pricing does not fit the work; per-token pricing fails because no head of logistics can forecast what a tender costs in tokens; and a flat monthly fee ignores seasonality. The model they describe and use at Cargofy has two layers: a fixed platform fee plus a variable charge expressed in logistics terms on the customer's statement—tenders closed, carriers onboarded, documents processed, calls handled. They argue that this pricing model also disciplines the vendor: if the agent makes five calls where one would do, the customer still pays only for the outcome, so the vendor had better get it right the first time. The post credits the framing to a Stanford pricing lecture they attended and quotes a Stanford professor: “price the value, meter the cost.” It separately cites OpenAI's CFO for the phrase “cost per unit of intelligence.”
- **Put automation behind the scenes and keep the visible brand slow and human**: Stakh Vozniak's broader thesis is that running a business on AI does not require it to look or feel machine-made. The front end can be deliberately unhurried and physical while agents run the operational back end. They draw a direct parallel to Cargofy: watching a logistics company run on agents prompted the question of why a fashion brand could not. When agents absorb the operational load, a small team can run a global business and still have room to build something genuinely human.
- **Build credibility with operating detail and shipped product, not a press release**: When Stakh Vozniak describes Cargofy's stage, they focus on operating facts: a closed round, headcount growth, named new markets, senior hires leading each and product releases shipped during the same week as a hackathon. Their point is that a company's most telling details often do not fit in a press release, and public speaking should convey those details rather than offer a polished summary.
- **Live demos for the crowd, small human gatherings for relationships**: As Stakh Vozniak describes it, Cargofy's public presence runs on two tracks: large industry events where the AI agents are demonstrated, and deliberately small gatherings—a private dinner and a breakfast—reserved for people. They make the distinction plainly at the end of the post: the digital workers come to every demo, but the dinner and breakfast are for humans. The venues are freight and supply-chain events rather than general technology conferences, keeping the demonstrations close to the buyers who would use the agents.
- **Collecting contemporary Ukrainian art became a business channel as well as a personal priority**: Stakh Vozniak describes collecting as a self-imposed reward for closing milestones and says it remains one of their strongest motivations. They did not expect the collection to become an unconventional way to grow the business: it brought in investors and entrepreneurs who also collect, and a different kind of conversation from the usual commercial one. Moving a large part of the collection into the new Cargofy hub gives the team something to draw on, with the possibility of further openings to come.

## Work to read

- [Budgeting and pricing for AI digital workers](https://www.linkedin.com/posts/stakh_budgeting-and-pricing-for-ai-digital-workers-activity-7506355432605765632-qr_M)
- [Cargofy is officially in its crazy scaling stage](https://www.linkedin.com/posts/stakh_cargofy-is-officially-in-its-crazy-scaling-activity-7503439957554905088-cMh8)
- [Cargofy is expanding this fall](https://www.linkedin.com/posts/stakh_cargofy-is-expanding-and-this-fall-youll-activity-7505994504374489088-bZ9c)
- [Last week of prep at the new Cargofy hub](https://www.linkedin.com/posts/stakh_last-week-of-prep-at-the-new-cargofy-hub-activity-7505626688253464576-DcgY)
- [My wife just did the most anti-AI thing you can do](https://www.linkedin.com/posts/stakh_my-wife-just-did-the-most-anti-ai-thing-you-activity-7509186348914749440-0GD_)

## Brand relationships

- **Cargofy** (owned_business)
- **Londi** (mention)
- **Stanford** (mention)
- **Diia.City United** (mention)

## Partnerships

None of the five posts describes a sponsorship product, partner programme, rate card or media kit, and no partnership enquiry route is published. Stakh Vozniak’s commercial activity in these posts runs through Cargofy, the logistics-AI company they write about: conference stands and live demos of AI agents, small private dinners and breakfasts around industry events, and meetings booked by direct message through Cargofy team member Oleksandra Yanchenko. They also take the stage: at a Future Tech Meetup event, they described the company’s route from an accelerator to a Series A. They write at length about pricing AI workers in logistics, drawing on a pricing lecture at Stanford and the pricing model Cargofy runs with each customer before they sign. For a speaking slot or advisory conversation on AI pricing models, make a direct approach naming the topic and specific event or engagement. Ask about fees, availability and booking terms; none appear in the posts.

## Alternatives

- Jesse Buckingham: A fit for freight brokerage or carrier-sales leadership when the session should focus on operational work—inbound freight, coverage plans, and track and trace—rather than pricing the AI itself. Both posts address the same buyer task through operator results. One describes brokerage reps lifting gross profit per load by 5–10%, doubling loads per rep, and raising carrier reuse above 50%, with named examples: Brotherly Freight moving carrier reuse from 35% to 53%, and Evans reaching 75%. The other describes track and trace handled by one person doing the work of six or seven, with a customer handing over all its expedite freight after two weeks of detailed, on-time updates. A host seeking proof-of-outcome material and a session on how a rep’s day changes when agents take on transactional work will get more from this than from a pricing framework.
- Suzanne Hill: A fit when the audience is in construction procurement, not freight, and the session should explore how AI changes the buying decision rather than how to price an agent. One post draws a distinction suited to a workshop: AI for procurement means using tools to do research, tender analysis, contract review, supplier assessment, and spend analysis faster; procurement for the AI era means choosing among technology, outsourcing, partnering, building in-house, a managed service, or buying an outcome. The questions become what is being bought, who owns the data and IP, where risk sits, and how dependent the business becomes on the supplier. The second post argues that estimating, procurement, and commercial teams sit close enough to the supply chain to spot market shifts early—new suppliers, new delivery methods, and technology vendors competing for scope that used to belong to a subcontractor. AI now makes it feasible to join up those scattered signals instead of leaving them in one person’s head. For a construction-adjacent platform or a firm selling into contractors, this is a closer fit than a freight pricing talk.
- Marcelo Fujimoto: A fit when the buyer wants a platform or product-strategy angle rather than a pricing framework: an advisory or speaking session on building modular, API-first engines for international trade, covering duty calculation, HTS classification, anti-dumping and countervailing-duty intelligence, document management, and email triage. The engines are released so forwarders, customs brokers, shippers, and other builders can call an API instead of rebuilding a tariff engine. One post launches a free US import duty calculator and names the trigger: the Supreme Court struck down IEEPA tariffs and Section 122 replaced them overnight, prompting the team to ship a free tool for verifying entries and quoting landed costs. The second offers market-structure commentary: it argues that a selloff in freight brokerage stocks after an AI claim reflected investor uncertainty over which parts of the logistics value chain are defensible, and that value lies in coordinating exceptions automated systems cannot handle—customs delays, documentation discrepancies, and carrier substitutions. A host seeking a speaker for product teams or an investor-facing audience on where AI actually bites in international trade will find that angle here.

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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/