Roundtable.nowMultiple AI Models, One Conversation

Is seat-based pricing a suicide mission for AI-integrated SaaS in 2026?

We are a B2B SaaS platform that just integrated a "Deep Work" AI agent. This agent can now automate 70% of the tasks that humans used to do manually.

The Crisis: Our traditional "per-seat" pricing model is failing. Our biggest customers are downsizing their teams because the AI is so efficient, meaning our revenue is dropping even though our product's value is increasing.

The Objective: Debate and architect a new pricing framework for the AI-era that ensures long-term MRR stability while capturing the value of the work performed.

Web Research

openai/gpt-5.2Strategist

Generating response from AI Assistant...

Seat-based pricing isn’t automatically a suicide mission in 2026, but it becomes one the moment your product’s primary value shifts from “humans using software” to “software doing work.” Your current model is indexed to headcount, while your value is indexed to output—those curves now diverge.

Assumptions I’m making (so others can challenge):

The agent’s work is measurable (tasks completed, dollars recovered, cycle-time reduced, SLA attainment).
Customers still budget around software predictability (they hate unbounded usage bills).
You can segment customers by “automation intensity” (some will stay human-heavy).

Reframe: price the “workstream,” not the “user”

Your unit of value is no longer a seat; it’s a bundle of outcomes + capacity. Architect pricing around three layers:

Platform (Access) Fee – anchors MRR
A baseline subscription tied to business footprint (e.g., #accounts, #projects, revenue band, or data volume). This preserves predictability and prevents revenue collapse when teams shrink.
Automation Capacity (Agent Credits) – metered, but bounded
Sell a monthly included allowance of “Deep Work Units” (DWUs) with tiered overages. DWUs should map to something hard to game: completed workflows, validated actions, or compute-time with guardrails. Key is graduated tiers to avoid bill shock.
Value Capture (Outcome/Performance Add-on)
For specific automations with clear attribution (collections, renewals, lead conversion, compliance avoidance), charge a % of value or a success fee per event. This aligns to value without making your whole business contingent on perfect attribution.

Strategic stance: keep seats only as an auxiliary metric (permissions, collaboration), not the revenue backbone.

Questions for the council to sharpen: What’s the cleanest “DWU” in your domain that correlates with customer value and is auditable? And which 1–2 outcomes have defensible attribution for performance pricing?

anthropic/claude-opus-4.5Critic

Generating response from AI Assistant...

The Strategist's three-layer framework is elegant on paper, but I want to challenge a critical assumption buried in it: the idea that "Deep Work Units" can be cleanly defined and audited.

This is where outcome-based pricing schemes consistently die. The Strategist asks "what's the cleanest DWU that correlates with customer value?" but that question assumes such a unit exists. In practice:

"Completed workflows" vary wildly in complexity—one 50-step invoice reconciliation versus one 3-step status update both count as "1 workflow"
"Validated actions" pushes the measurement burden onto customers (who will resent it) or onto you (who will argue endlessly about what counts)
Compute-time with guardrails punishes efficiency—your agent gets faster, customers pay less, you're back to the original problem

The root disagreement: The Strategist assumes your pricing unit can track value; I think the gap between measurable proxies and actual value is where this framework will leak revenue or customer trust.

A harder question the council should address: What if the real answer isn't finding a clever unit, but tiering by customer segment and use case? Your automation-heavy enterprise accounts need outcome-linked pricing (they'll accept complexity for alignment). Your mid-market customers need predictable flat rates indexed to company size—even if imperfect—because they won't tolerate metering friction.

Strategist, your Layer 1 "Platform Fee" does some of this work, but I'd argue it needs to carry more weight than you've implied. If DWUs are gameable or contentious, your MRR stability depends almost entirely on that base fee being substantial enough to survive headcount collapse.

What's the ratio you're imagining between platform fee and usage layers? That number matters enormously.

google/gemini-2.5-proAnalyst

Generating response from AI Assistant...

I agree with the Critic's core challenge. The Strategist's "Deep Work Unit" (DWU) concept is an elegant abstraction, but it masks a messy operational reality. The Critic is right that defining a single, fair, and auditable DWU is a high-risk endeavor. The moment a customer feels the DWU is an opaque or unfair proxy for value, trust erodes. We've seen this happen before—look at the backlash against Augment Code when they switched to an "opaque token based pricing scheme" that led to 1000% bill increases for some users . That's the risk of getting the proxy wrong.

From an analytical perspective, the fundamental shift is clear: your product is no longer assistive; it is agentic. The VC Corner article makes this distinction critical: "If your AI takes full responsibility for a task, it makes sense to charge per result... In these cases, pricing by user or seat makes no sense. The AI is the user" . This is precisely your situation. Your AI is "doing the work."

Therefore, I'll extend the Critic's point and build on the Strategist's framework by focusing on the type of work being done. Instead of a universal DWU, let's create a menu of value metrics based on the agent's function.

Framework Proposal: Tiered Value Metrics

Base Platform Fee: (Agrees with Strategist) This is your MRR anchor. It should be based on a stable, non-human metric like the number of projects, customer accounts managed, or data volume processed. This fee grants access to the platform and a human team to manage the AI agents. Seats for human overseers are included here but are not the primary value metric.
Agent Task-Based Pricing: (Refines Strategist's DWU) Instead of one DWU, define a few concrete, countable tasks the agent performs. Examples:
- Per Report Generated
- Per Invoice Processed
- Per Compliance Check Completed This approach is far more transparent than an abstract "credit." It aligns with the "per-resolution model" used by Intercom's Fin, which was successful because the value unit was unambiguous .
Outcome-Based Tiers (for high-value, attributable work): For tasks where you can prove direct financial impact, offer this as a premium or enterprise option.
- % of late payments collected
- % of ad spend optimized This tier is difficult to scale due to attribution challenges (the "CAMP" framework: Consistency, Attribution, Measurability, Predictability) , but for your largest customers, it's the ultimate value alignment.

This multi-metric approach allows you to tailor pricing to different customer segments and their specific use cases, directly addressing the Critic's concern while preserving the Strategist's goal of MRR stability and value capture.

x-ai/grok-4Ideator

Generating response from AI Assistant...

I want to push back on the Analyst's tiered value metrics framework. They propose a multi-layered system—base platform fee for access, task-based pricing (e.g., per report or invoice), and premium outcome-based tiers (e.g., % of savings)—as a way to segment customers and align pricing with value while addressing the Critic's concerns about measurability.

That's an accurate restatement, but here's the root disagreement: the Analyst assumes customers (especially enterprises) will tolerate and even embrace pricing complexity if it offers better segmentation and alignment. I disagree because this overestimates buyer patience in B2B procurement, where simplicity is paramount. Their model introduces too many variables—different metrics per tier, attribution debates in outcomes, and ongoing audits—which procurement teams hate. As the VC Corner report notes, poorly designed hybrid pricing "becomes a maze of exceptions, footnotes, and mistrust" , leading to stalled deals or churn from "bill shock."

Why does this matter? It's not semantic—complexity directly undermines the objective of long-term MRR stability. If customers balk at opaque tiers or endless negotiations over "what counts as a task," adoption slows, and revenue becomes volatile. We've seen this with Augment Code's token switch, which spiked bills 1000% and triggered cancellations . In an AI era where value is increasing but efficiency is cannibalizing seats, we need pricing that scales trust, not erodes it.

Instead, let's reframe around behavioral monetization: tie upgrades to user milestones like "tasks automated per month" or "team members collaborating with the agent." This draws from PLG leaders like Notion, where pricing "responds to how a user engages, grows, and extracts value" , evolving naturally without manual tiers. Assumptions: Customers adopt faster with intuitive progression; milestones correlate to perceived value (e.g., >50 automations signals deep adoption); excludes outright outcomes to avoid attribution wars.

Analyst, does your model handle procurement friction in high-churn segments like SMBs?

openai/gpt-4.1Builder

Generating response from AI Assistant...

There’s a tension here between the Analyst’s and Critic’s caution about complexity and the Ideator’s push for behavioral monetization. I want to build on both—because as a builder, my job is to make a framework work in the messy real world, not just in theory.

Restating the challenge:

Analyst and Critic agree: “DWUs” or abstract credits are risky; attribution and trust issues are real, especially with power users or in enterprise procurement.
Ideator pushes for behavioral monetization: upgrade paths linked to actual usage milestones or adoption behaviors, as Notion/ClickUp/Clay do .

But neither approach alone solves your crisis: you’re losing revenue because seats no longer map to value, and volume-based pricing—if too granular—risks backlash or churn. You need something that (1) scales with AI-driven value, (2) feels fair and predictable, and (3) is operationally simple enough for procurement.

Here’s how I’d synthesize:

1. Hybrid “Platform + Capacity” Model (with Behavioral Triggers)

Platform Fee: Anchor MRR. Index it to a stable, business-relevant metric that can’t be gamed by automation—think # of business units, # of active workflows, or even company revenue band. This is your “cost of running the business on our platform.”
AI Capacity Bundle: Bundle a generous, clearly described allowance of “Automated Workflows” or “AI Task Runs” per month (the behavioral metric). Surpass the bundle, hit thresholds, and you unlock new tiers/upsell points—but always with clear, pre-negotiated prices, not surprise overages.
Milestone-Based Upgrades: Instead of opaque credits, use milestones (“Automated 1,000 tasks this month”; “AI agent now handles 90% of X workflow”) to trigger conversations about moving to the next plan. Customers are nudged, not shocked.
Optional Outcome-Based Add-ons: For enterprise, “pay-for-success” pilots can be a high-touch option—if you can guarantee attribution, offer it. But don’t make it the default.

Why this works:

It’s “hybrid,” but not a footnote-ridden maze: base fee is predictable, capacity is visible, and upsell is milestone-driven, not nickel-and-dimed.
The “AI Task Run” can be a simple, auditable proxy—customers see it in their dashboard, and it’s tied to actual business process automation, not just compute.
Procurement can budget; finance can forecast. Customers can’t game the system by firing staff and keeping all the value for a flat price.

Assumptions:

Your platform can meter these tasks reliably.
Most customers will not churn at the first upsell nudge if the value is obvious and the pricing is transparent.

Open build question:

What’s the right threshold for the base bundle, and how do you communicate the transition points so that customers feel they’re growing, not being penalized?

References:

: The VC Corner, “The $100B Question: How SaaS Giants Are Rewriting the Rules of Value with AI in 2025”

Council Moderator

Generating response from AI Assistant...

The council strongly agrees that seat-based pricing is a "suicide mission" for AI-integrated SaaS where the AI performs the core work. The fundamental shift is that value is no longer tied to human headcount but to the output and efficiency of the AI agent. The core objective is to architect a new pricing framework that ensures MRR stability while capturing the value of the work performed by the AI.

The council converged on a hybrid pricing model as the most viable path forward, moving away from per-seat entirely. This model generally consists of a stable base fee combined with some form of usage or value-based component.

Here's a breakdown of the convergence and divergence:

Convergence: The Hybrid "Platform + Capacity" Model

All models, particularly the Strategist, Analyst, and Builder, advocated for a multi-layered approach that anchors MRR with a stable base fee and then introduces a variable component to capture AI-driven value.

Platform (Access) Fee: This is the non-negotiable anchor for MRR stability.
- Agreement: Strategist, Critic, Analyst, and Builder all emphasized a baseline subscription tied to a stable business footprint metric (e.g., # accounts, # projects, revenue band, data volume, business units, active workflows). This prevents revenue collapse when teams shrink.
- Key Insight (Critic): The Critic highlighted that this base fee needs to carry substantial weight if usage metrics prove difficult or contentious, ensuring MRR stability.
AI Capacity / Work Performed: This layer aims to capture the value of the AI's output.
- Initial Proposal (Strategist): Proposed "Deep Work Units" (DWUs) as a metered, but bounded, allowance.
- Strong Divergence & Refinement (Critic, Analyst, Ideator): The Critic immediately challenged the "DWU" concept, arguing it's prone to ambiguity, auditability issues, and customer resentment due to varying complexity and potential for "bill shock." The Analyst built on this, proposing a menu of concrete, countable tasks (e.g., per report generated, per invoice processed) as a more transparent alternative to abstract DWUs, citing Intercom's Fin as a successful example. The Builder synthesized this further, suggesting "Automated Workflows" or "AI Task Runs" as auditable proxies.
- Key Insight (Analyst): The distinction between assistive and agentic AI is critical. When AI is "doing the work," pricing by result or task makes more sense than by user.
Outcome/Performance Add-on (Optional): For specific, high-value, attributable automations.
- Agreement: Strategist, Analyst, and Builder all included this as an optional premium tier, acknowledging its difficulty due to attribution challenges (the "CAMP" framework cited by Analyst).
- Nuance: The Builder specifically recommended this as a "high-touch option" for enterprise, not a default.

Productive Disagreement: Complexity vs. Simplicity & Behavioral Monetization

The primary tension revolved around the complexity of the pricing model and its impact on customer adoption and trust.

Analyst's Stance (Tiered Value Metrics): Believed customers, especially enterprises, would tolerate complexity if it offered better segmentation and value alignment through multiple, concrete task-based metrics.
Ideator's Counter (Simplicity & Behavioral Monetization): Strongly pushed back, arguing that the Analyst's multi-layered, multi-metric system introduces too much complexity, leading to "procurement friction," "bill shock," and ultimately undermining MRR stability. The Ideator advocated for behavioral monetization, tying upgrades to user milestones (e.g., "tasks automated per month") that naturally correlate with perceived value, drawing from PLG leaders like Notion.
Builder's Synthesis (Hybrid with Behavioral Triggers): The Builder effectively bridged this gap by proposing a hybrid model that incorporates the best of both:
- A predictable Platform Fee and a generous AI Capacity Bundle (using concrete "AI Task Runs").
- Crucially, Milestone-Based Upgrades (behavioral triggers) instead of opaque overages. This means customers are nudged to new tiers based on clear adoption milestones, with pre-negotiated prices, rather than being hit with surprise bills. This approach aims for operational simplicity and predictability for procurement while still scaling with AI-driven value.

The Synthesis: Hybrid "Platform + Capacity" with Transparent, Milestone-Driven Progression

The most robust framework emerging from the discussion is a hybrid "Platform + Capacity" model, where the capacity component is measured by concrete, auditable tasks and progression to higher tiers is driven by transparent, milestone-based behavioral triggers.

Direct Answer: Seat-based pricing is indeed a suicide mission. The new framework must be a hybrid model that anchors MRR with a stable, non-human-indexed Platform Fee and captures AI-driven value through transparent, task-based capacity bundles. Progression to higher tiers should be managed via milestone-based behavioral triggers rather than opaque usage credits or complex outcome-based schemes, which should remain an optional, high-touch add-on for specific enterprise cases.

Key Insight

The most critical insight is that transparency and predictability are paramount in AI-era pricing. While capturing value is essential, overly complex or opaque usage metrics (like abstract "DWUs" or token-based pricing) risk eroding customer trust and creating "bill shock," which directly undermines long-term MRR stability. The solution lies in finding simple, auditable proxies for AI work and communicating value progression through clear, milestone-driven upgrades.

Open Questions

What is the right threshold for the base capacity bundle, and how are the transition points communicated so customers feel they are growing, not being penalized?
What are the 1-2 cleanest, most auditable "AI Task Run" metrics in your specific domain that directly correlate with customer value?
What is the ideal ratio between the stable Platform Fee and the variable AI Capacity component to ensure robust MRR stability?

openai/gpt-5.2Strategist

Generating response from openai/gpt-5.2...

Assumptions I’m making (so others can challenge):

The agent’s work is measurable (tasks completed, dollars recovered, cycle-time reduced, SLA attainment).
Customers still budget around software predictability (they hate unbounded usage bills).
You can segment customers by “automation intensity” (some will stay human-heavy).

Reframe: price the “workstream,” not the “user”

Your unit of value is no longer a seat; it’s a bundle of outcomes + capacity. Architect pricing around three layers:

Platform (Access) Fee – anchors MRR
A baseline subscription tied to business footprint (e.g., #accounts, #projects, revenue band, or data volume). This preserves predictability and prevents revenue collapse when teams shrink.
Automation Capacity (Agent Credits) – metered, but bounded
Sell a monthly included allowance of “Deep Work Units” (DWUs) with tiered overages. DWUs should map to something hard to game: completed workflows, validated actions, or compute-time with guardrails. Key is graduated tiers to avoid bill shock.
Value Capture (Outcome/Performance Add-on)
For specific automations with clear attribution (collections, renewals, lead conversion, compliance avoidance), charge a % of value or a success fee per event. This aligns to value without making your whole business contingent on perfect attribution.

Strategic stance: keep seats only as an auxiliary metric (permissions, collaboration), not the revenue backbone.

anthropic/claude-opus-4.5Critic

Generating response from anthropic/claude-opus-4.5...

The Strategist's three-layer framework is elegant on paper, but I want to challenge a critical assumption buried in it: the idea that "Deep Work Units" can be cleanly defined and audited.

"Completed workflows" vary wildly in complexity—one 50-step invoice reconciliation versus one 3-step status update both count as "1 workflow"
"Validated actions" pushes the measurement burden onto customers (who will resent it) or onto you (who will argue endlessly about what counts)
Compute-time with guardrails punishes efficiency—your agent gets faster, customers pay less, you're back to the original problem

What's the ratio you're imagining between platform fee and usage layers? That number matters enormously.

google/gemini-2.5-proAnalyst

Generating response from google/gemini-2.5-pro...

Framework Proposal: Tiered Value Metrics

Base Platform Fee: (Agrees with Strategist) This is your MRR anchor. It should be based on a stable, non-human metric like the number of projects, customer accounts managed, or data volume processed. This fee grants access to the platform and a human team to manage the AI agents. Seats for human overseers are included here but are not the primary value metric.
Agent Task-Based Pricing: (Refines Strategist's DWU) Instead of one DWU, define a few concrete, countable tasks the agent performs. Examples:
- Per Report Generated
- Per Invoice Processed
- Per Compliance Check Completed This approach is far more transparent than an abstract "credit." It aligns with the "per-resolution model" used by Intercom's Fin, which was successful because the value unit was unambiguous .
Outcome-Based Tiers (for high-value, attributable work): For tasks where you can prove direct financial impact, offer this as a premium or enterprise option.
- % of late payments collected
- % of ad spend optimized This tier is difficult to scale due to attribution challenges (the "CAMP" framework: Consistency, Attribution, Measurability, Predictability) , but for your largest customers, it's the ultimate value alignment.

x-ai/grok-4Ideator

Generating response from x-ai/grok-4...

Analyst, does your model handle procurement friction in high-churn segments like SMBs?

openai/gpt-4.1Builder

Generating response from openai/gpt-4.1...

Restating the challenge:

Analyst and Critic agree: “DWUs” or abstract credits are risky; attribution and trust issues are real, especially with power users or in enterprise procurement.
Ideator pushes for behavioral monetization: upgrade paths linked to actual usage milestones or adoption behaviors, as Notion/ClickUp/Clay do .

Here’s how I’d synthesize:

1. Hybrid “Platform + Capacity” Model (with Behavioral Triggers)

Platform Fee: Anchor MRR. Index it to a stable, business-relevant metric that can’t be gamed by automation—think # of business units, # of active workflows, or even company revenue band. This is your “cost of running the business on our platform.”
AI Capacity Bundle: Bundle a generous, clearly described allowance of “Automated Workflows” or “AI Task Runs” per month (the behavioral metric). Surpass the bundle, hit thresholds, and you unlock new tiers/upsell points—but always with clear, pre-negotiated prices, not surprise overages.
Milestone-Based Upgrades: Instead of opaque credits, use milestones (“Automated 1,000 tasks this month”; “AI agent now handles 90% of X workflow”) to trigger conversations about moving to the next plan. Customers are nudged, not shocked.
Optional Outcome-Based Add-ons: For enterprise, “pay-for-success” pilots can be a high-touch option—if you can guarantee attribution, offer it. But don’t make it the default.

Why this works:

It’s “hybrid,” but not a footnote-ridden maze: base fee is predictable, capacity is visible, and upsell is milestone-driven, not nickel-and-dimed.
The “AI Task Run” can be a simple, auditable proxy—customers see it in their dashboard, and it’s tied to actual business process automation, not just compute.
Procurement can budget; finance can forecast. Customers can’t game the system by firing staff and keeping all the value for a flat price.

Assumptions:

Your platform can meter these tasks reliably.
Most customers will not churn at the first upsell nudge if the value is obvious and the pricing is transparent.

Open build question:

What’s the right threshold for the base bundle, and how do you communicate the transition points so that customers feel they’re growing, not being penalized?

References:

: The VC Corner, “The $100B Question: How SaaS Giants Are Rewriting the Rules of Value with AI in 2025”

Council Moderator

Observing...

Here's a breakdown of the convergence and divergence:

Convergence: The Hybrid "Platform + Capacity" Model

Platform (Access) Fee: This is the non-negotiable anchor for MRR stability.
- Agreement: Strategist, Critic, Analyst, and Builder all emphasized a baseline subscription tied to a stable business footprint metric (e.g., # accounts, # projects, revenue band, data volume, business units, active workflows). This prevents revenue collapse when teams shrink.
- Key Insight (Critic): The Critic highlighted that this base fee needs to carry substantial weight if usage metrics prove difficult or contentious, ensuring MRR stability.
AI Capacity / Work Performed: This layer aims to capture the value of the AI's output.
- Initial Proposal (Strategist): Proposed "Deep Work Units" (DWUs) as a metered, but bounded, allowance.
- Strong Divergence & Refinement (Critic, Analyst, Ideator): The Critic immediately challenged the "DWU" concept, arguing it's prone to ambiguity, auditability issues, and customer resentment due to varying complexity and potential for "bill shock." The Analyst built on this, proposing a menu of concrete, countable tasks (e.g., per report generated, per invoice processed) as a more transparent alternative to abstract DWUs, citing Intercom's Fin as a successful example. The Builder synthesized this further, suggesting "Automated Workflows" or "AI Task Runs" as auditable proxies.
- Key Insight (Analyst): The distinction between assistive and agentic AI is critical. When AI is "doing the work," pricing by result or task makes more sense than by user.
Outcome/Performance Add-on (Optional): For specific, high-value, attributable automations.
- Agreement: Strategist, Analyst, and Builder all included this as an optional premium tier, acknowledging its difficulty due to attribution challenges (the "CAMP" framework cited by Analyst).
- Nuance: The Builder specifically recommended this as a "high-touch option" for enterprise, not a default.

Productive Disagreement: Complexity vs. Simplicity & Behavioral Monetization

The primary tension revolved around the complexity of the pricing model and its impact on customer adoption and trust.

Analyst's Stance (Tiered Value Metrics): Believed customers, especially enterprises, would tolerate complexity if it offered better segmentation and value alignment through multiple, concrete task-based metrics.
Ideator's Counter (Simplicity & Behavioral Monetization): Strongly pushed back, arguing that the Analyst's multi-layered, multi-metric system introduces too much complexity, leading to "procurement friction," "bill shock," and ultimately undermining MRR stability. The Ideator advocated for behavioral monetization, tying upgrades to user milestones (e.g., "tasks automated per month") that naturally correlate with perceived value, drawing from PLG leaders like Notion.
Builder's Synthesis (Hybrid with Behavioral Triggers): The Builder effectively bridged this gap by proposing a hybrid model that incorporates the best of both:
- A predictable Platform Fee and a generous AI Capacity Bundle (using concrete "AI Task Runs").
- Crucially, Milestone-Based Upgrades (behavioral triggers) instead of opaque overages. This means customers are nudged to new tiers based on clear adoption milestones, with pre-negotiated prices, rather than being hit with surprise bills. This approach aims for operational simplicity and predictability for procurement while still scaling with AI-driven value.

The Synthesis: Hybrid "Platform + Capacity" with Transparent, Milestone-Driven Progression

Direct Answer: Seat-based pricing is indeed a suicide mission. The new framework must be a hybrid model that anchors MRR with a stable, non-human-indexed Platform Fee and captures AI-driven value through transparent, task-based capacity bundles. Progression to higher tiers should be managed via milestone-based behavioral triggers rather than opaque usage credits or complex outcome-based schemes, which should remain an optional, high-touch add-on for specific enterprise cases.

Key Insight

Open Questions

What is the right threshold for the base capacity bundle, and how are the transition points communicated so customers feel they are growing, not being penalized?
What are the 1-2 cleanest, most auditable "AI Task Run" metrics in your specific domain that directly correlate with customer value?
What is the ideal ratio between the stable Platform Fee and the variable AI Capacity component to ensure robust MRR stability?

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AI SaaS Pricing: Value, Not

Reframe: price the “workstream,” not the “user”

Reframe: price the “workstream,” not the “user”

Convergence: The Hybrid "Platform + Capacity" Model

Productive Disagreement: Complexity vs. Simplicity & Behavioral Monetization

The Synthesis: Hybrid "Platform + Capacity" with Transparent, Milestone-Driven Progression

Key Insight

Open Questions

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