Velocity AI vs. Tenex: Enterprise Delivery Capacity vs. Output-Based Engineering

Velocity AI vs. Tenex is a comparison that comes up because both firms use similar language — embedded teams, fast delivery, outcome focus — but the structural realities are quite different. Tenex has a compelling story and a genuinely differentiated pricing model. Understanding what that model is built to deliver, and where its limits are, is the most important part of this evaluation.

This post is written by Velocity AI. We have an obvious interest in how it reads. We have written it honestly — including a direct account of what makes Tenex's output-based model genuinely interesting, and where it fits best.

At a Glance

The Capacity Constraint

Tenex's most visible characteristic — and the most material fact for enterprise buyers — is its size. Seven employees, all former founders, executing AI transformation and engineering engagements simultaneously. This is a deliberate choice: Tenex has said publicly that every employee being a former founder is a core differentiator, and it is a real one for the right client.

The constraint is bandwidth. A seven-person firm cannot simultaneously staff multiple large, multi-workstream enterprise AI programs without meaningful quality or timeline risk. If you are a Fortune 500 company with a complex AI initiative that requires concurrent work across data infrastructure, model development, integration, governance, and change management — you will outgrow Tenex's capacity before the engagement is over.

This does not make Tenex the wrong choice for every buyer. It makes them the wrong choice for buyers whose programs require capacity that seven people cannot deliver.

Velocity AI operates with 100+ AI practitioners available via CourtAvenue. Large programs get properly staffed teams. We do not create a capacity constraint at scale.

The Output-Based Model: What It Is and When It Works

Tenex's AI Engineering service line is genuinely differentiated in the boutique AI market. Rather than billing by the hour or by the month, Tenex prices on output — features delivered. Engineers are compensated for what ships, not for time spent. Some Tenex engineers reportedly earn $1M+ per year under this model.

This creates strong incentive alignment for a specific type of engagement: software product development where the deliverable is clearly defined, discrete features. If you need a team to ship AI-powered product features faster than your internal engineers can — and you can define what "done" looks like for each feature — Tenex's output model is compelling.

The model is less suited to enterprise AI transformation programs where deliverables are harder to discretize into "features." Complex data infrastructure work, multi-system AI integration, governance frameworks, and production MLOps cannot always be scoped in feature-unit terms. Enterprise procurement and legal teams may also push back on output-based contracts, which are structurally unfamiliar to most enterprise legal frameworks.

Velocity AI uses fixed-fee and milestone-based pricing — a structure that enterprise procurement teams understand, that creates clear budget predictability, and that applies equally well to complex infrastructure programs as to discrete product builds.

The Brand Advantage Tenex Has Built

Any honest comparison of Tenex requires acknowledging what Alex Lieberman's media platform does for the firm. Morning Brew reached 4 million subscribers before its sale. Lieberman has maintained a significant content presence across LinkedIn, newsletter, and podcast. When Tenex does interesting work or publishes a playbook, it reaches an audience that most boutique AI firms cannot access.

This is a real marketing moat. It means Tenex has inbound awareness and credibility that Velocity AI — or any firm without a founder of Lieberman's profile — cannot replicate through conventional marketing. For buyers who discovered Tenex through this channel, the discovery itself is a legitimate data point about their market presence.

What brand awareness does not resolve is the capacity question. The work still gets executed by approximately seven people. For the right engagement, that team is excellent. For a large enterprise program, the math does not work regardless of who founded the firm.

Where Tenex Genuinely Excels

We believe in honest advice. There are contexts where Tenex is the better fit:

VC-backed startups and growth-stage software companies. Tenex's output-based engineering model is ideally suited to software product companies that need to ship AI-powered features faster. The model aligns engineer compensation with product velocity in a way that standard consulting engagements do not.

Transformation audits for organizations exploring AI. Tenex's 30/60/90-day AI Transformation service — auditing existing operations, surfacing AI use cases, and producing a prioritized roadmap — is a well-structured entry point for organizations still defining their AI strategy.

Buyers who respond to founder credibility. For companies where the CEO or founder relationship matters more than institutional delivery infrastructure, Tenex's all-founders team and Lieberman's profile creates a compelling peer dynamic.

Digital-native and media-adjacent businesses. Tenex's roots in digital media and startup ecosystems give them strong pattern recognition for these types of organizations.

How to Decide

The question with Tenex is not whether they are a good firm — they are — but whether your program fits their structure.

If your initiative is a software product that needs AI-accelerated feature delivery, and you want output-based pricing, and your team size doesn't outgrow Tenex's capacity, Tenex is worth a serious conversation.

If your initiative is an enterprise AI transformation — complex data infrastructure, multi-system integration, production ML deployment, regulated industry compliance — Velocity AI is built for that. We have the team size, the enterprise delivery structure, and the fixed-fee accountability that large programs require.

We are willing to give you an honest assessment of whether your initiative matches our strengths. If it doesn't, we will say so.