Larry Walsh, CEO of Channelnomics, is pressing IT vendors to stop treating the ballooning cost of AI-powered partner tools as an unpredictable operational expense and instead turn token usage into a managed performance benefit. In a July 13 analysis, Walsh argues that channel programs can learn from how streaming services adjusted pricing and packaging—and apply the same discipline to AI allocations tied to partner sales, renewals, and revenue generation.

The Hidden AI Bill in Partner Operations

Every time a partner or channel account manager uses an AI agent embedded in a sales portal, a customer-support workflow, or a proposal-generation tool, the application burns tokens. Those tokens—the fundamental unit of consumption for large language models—carry real infrastructure costs. Because utilization fluctuates wildly across a partner base, the total bill can be hard to predict. Vendors are already absorbing these expenses as they race to integrate AI into partner enablement, but many have not yet accounted for the scale of token consumption.

Walsh points to the streaming industry as an instructive parallel. Netflix, Disney, and others discovered that fixed subscription pricing couldn’t sustain rising content costs. They introduced tiered plans, advertising-supported tiers, and usage limits. In the channel, the equivalent is the creeping cost of AI assistants, co-pilots, and automated workflows that make the go-to-market engine faster and more efficient. If unmanaged, those costs could spiral just as unchecked cloud spend did in the early days of infrastructure-as-a-service.

Tokens as the New Performance Metric

Rather than simply paying the AI bill, Walsh proposes that vendors allocate additional token capacity based on a partner’s measurable output: revenue growth, new customer wins, renewals, qualified pipeline, or account expansion. In this model, a high-performing partner would receive a larger AI resource pool—not just a bigger discount—that directly aids their ability to sell, quote, market, and support customers more efficiently.

This flips the conversation. Instead of viewing tokens as a cost center, vendors can position them as a productivity accelerator. A token has value because of what it enables: eliminating manual steps, reducing sales friction, improving customer response times, and ultimately generating higher overall profits through speed and volume. For partners, access to such capabilities can be a stronger draw than a marginal improvement in margin, especially when ease of doing business consistently ranks among the top criteria for partner loyalty.

Why MDF Budgets Are the Natural Fit

Walsh’s most concrete suggestion is to fund token incentives from existing market development funds (MDF) or to create dedicated AI enablement pools. Traditional MDF pays for campaigns, events, and lead-generation activities that often yield ambiguous returns. By contrast, AI token consumption can be tracked at the partner, user, application, and workflow levels. Vendors can see which partners use their allocations, for which tasks, and whether that usage correlates with faster sales cycles, higher renewal rates, or increased pipeline.

This makes token-based incentives a more measurable form of MDF. Instead of funding a generic digital campaign, a vendor might provide AI resources that help a partner identify upsell opportunities, personalize outreach, or accelerate service delivery. The objective—generate more business—remains the same. But the mechanism is more accountable and potentially more effective.

The model can also impose financial discipline. Because tokens are consumed incrementally, vendors can set governance rules around limits, allocation tiers, and periodic recalibration. Costs can be directly tied to partner production, possibly structured as contra-revenue programs where the expense is offset by revenue from the partners using the AI tools. That creates a clear link between partner contribution and AI investment.

What This Means for Microsoft Channel Partners

For Windows-focused vendors, managed service providers, and Microsoft partners, the concept hits close to home. Microsoft Copilot, Azure OpenAI Service, and an expanding set of AI-infused Dynamics 365 tools are already weaving token consumption into everyday partner workflows. When a partner uses a quote-generation assistant built on a large language model, or when an MSP relies on AI-driven ticket triage, the meter is running.

If Microsoft—or any major vendor—adopted token-based incentives, a partner’s earning potential could change. A reseller that consistently drives Azure consumption, closes large enterprise agreements, or maintains high renewal rates might gain additional Copilot capacity for its own internal operations. That capacity could give the partner a competitive edge: faster proposal turnaround, more accurate opportunity scoring, or automated customer success outreach.

However, the economics are not static. As Walsh notes, ongoing infrastructure investments by model providers could push token costs higher, while hardware improvements and competition drive them down. Vendors will need to keep one eye on the underlying model pricing and the other on partner usage patterns, recalibrating allocations at least quarterly.

Four Steps Vendors Can Take Right Now

1. Audit your current AI spend. Identify every partner-facing tool that consumes tokens—including internal sales assistants, portal chatbots, and service-desk automation. Estimate per-interaction costs and monthly consumption across the partner base. Many organizations lack this visibility today.

2. Pilot a performance-based allocation with top-tier partners. Select a small group of high-performing partners and offer them a token stipend tied to a specific outcome, such as new customer wins or pipeline growth. Track usage against that outcome over a 90-day period and compare results to a control group receiving only traditional incentives.

3. Design governance guardrails. Set clear rules: consumption limits per partner, approval workflows for large allocations, and a cadence for reviewing and adjusting tiers. Decide whether the program will be funded via MDF reallocation, a dedicated budget, or contra-revenue from partner sales.

4. Measure what matters. Use telemetry to answer key questions: Which partners actually consume their token allocations? For which tasks? Does token usage shorten the quote-to-close cycle, lift renewal rates, or expand wallet share? This data is essential to justify continued investment and to refine the program over time.

For partners, the immediate action is simpler: start tracking how much AI-assisted work your teams are doing and the business impact. If a vendor does launch a token incentive, being able to articulate your usage and its ROI will put you in a stronger negotiating position.

The Road Ahead

The call to treat AI tokens as channel currency arrives at a moment when almost every major vendor is embedding generative AI into partner operations. Walsh’s advice—budget deliberately, tie cost to output, and measure relentlessly—applies whether a vendor ever adopts a formal token-incentive program or not. As a minimum, channel leaders need to understand what their AI consumption costs are and whether that spend is producing a return.

The bigger shift is cultural. Moving from open-ended expense to managed incentive requires new muscle: data-driven partner segmentation, agile budgeting, and a willingness to experiment. But if done right, the payoff is a partner ecosystem that not only sells more but operates more efficiently—funded in part by the very tools that create the leverage.