I Ran My GTM Strategy Through a 6-Model AI Board Meeting. Here’s What Happened.

Reading time: 21 minutes

TLDR

ChatGPT validated my enterprise tier launch strategy. Six AI models debating tore it apart and saved me $80K. CFO AI caught a cash flow gap, COO AI exposed operations blindspots, Market Realist AI challenged demand assumptions, and Game Theorist AI predicted competitor responses. Pilot testing revealed only 2 of 12 prospects would actually commit. Pivoted to SMB tier instead and added $47K ARR. Single-AI validation confirms your biases. Multi-model adversarial debate reveals what you’re missing.

In This Article

My plan: Launch enterprise tier pricing for our B2B SaaS ($499/mo vs current $99/mo)

ChatGPT’s response: “This is a solid strategy. Here’s how to execute it.”

Solutions (6-model adversarial debate) response: “Hold on. Let’s stress-test this.”

What happened next: Four critical flaws surfaced in 15 minutes that would have cost me $80,000+ in wasted execution.

Here’s the full breakdown of how adversarial AI testing works. And why single AI validation is dangerous.

The Strategy (Before Testing)

Context: B2B SaaS startup, $850K ARR, 180 customers at $99-199/mo

The plan: Launch enterprise tier at $499/mo targeting teams of 50+ people

My reasoning:

  • Current customers keep asking for “enterprise features” (SSO, SCIM, audit logs)
  • Competitors charge $500-800/mo for similar tiers
  • We have 12 customers with 50+ users who’d be good fits
  • Dev estimate: 3 months to build enterprise features

Expected outcome:

  • Convert 6 of 12 target accounts to $499/mo = +$36K ARR
  • Close 4 new enterprise deals in Q1 = +$24K ARR
  • Total: +$60K ARR in 6 months

I felt confident. I’d done customer interviews. I’d checked competitor pricing. I’d validated demand.

Then I ran it through ChatGPT.

What ChatGPT Said (Single AI Validation)

My prompt to ChatGPT:

“I run a B2B SaaS at $850K ARR with 180 customers paying $99-199/mo. I want to launch an enterprise tier at $499/mo with SSO, SCIM, and audit logs. Target market is teams of 50+ users. I have 12 current customers who fit this profile and estimate 3 months dev time. Does this strategy make sense?”

ChatGPT’s response (summarized):

“Yes, this is a solid strategy. Here’s how to execute it:

Phase 1: Validate demand

  • Interview your 12 target accounts to confirm willingness to pay $499/mo
  • Survey broader customer base for enterprise feature interest

Phase 2: Build MVP

  • Prioritize SSO first (highest demand)
  • Add SCIM and audit logs in subsequent releases
  • Aim for 2-3 month timeline to launch

Phase 3: Launch & iterate

  • Soft launch to 3-5 beta customers
  • Gather feedback and refine
  • Full launch with marketing push

Financial projections look reasonable given your current ARR and customer base. Enterprise tier pricing at $499/mo is competitive for this feature set.”

My reaction: “Great, ChatGPT validated my thinking. Let’s do this.”

This is where most founders stop. Single AI gave helpful, detailed execution advice. It felt thorough.

But ChatGPT didn’t challenge my assumptions. It didn’t stress-test the plan. It didn’t play devil’s advocate.

So I ran it through Solutions.

KEY TAKEAWAY: Single AI validation feels thorough because it gives detailed execution plans. But ChatGPT is trained to be helpful and agreeable. It validates your thinking rather than stress-testing whether you should execute at all.

What Solutions Did (6-Model Adversarial Debate)

Solutions approach: Six AI models debate your strategy from different perspectives:

  1. CFO AI - Financial risk, cash flow, runway
  2. COO AI - Operational feasibility, execution gaps
  3. Market Realist AI - Demand validation, market dynamics
  4. Game Theorist AI - Competitive response, strategic positioning
  5. Chief Strategist AI - Integration with broader strategy
  6. Wildcard AI - Unconventional perspectives, blindspots

The format: Each AI model reviews the strategy, then they debate it. challenging assumptions, poking holes, playing devil’s advocate.

Here’s what each AI said.

CFO AI: “You Can’t Afford This Build”

CFO AI’s take:

“Let’s talk cash flow. You said 3 months dev time. Let’s be realistic:

  • Developer cost: 3 months × $15K/mo fully loaded = $45K
  • Design & PM overhead: +$8K
  • Security audit for enterprise features: +$5K (required for SOC 2, which enterprise buyers will ask for)
  • Sales/marketing for launch: +$10K

Total investment: $68K

Your best-case scenario: +$60K ARR in 6 months.

Break-even timeline: 13+ months (assuming no churn)

Current runway: You didn’t mention this, but at $850K ARR, if you’re break-even or slightly profitable, you have ~6-9 months runway assuming you invest $68K.

The risk: If only 3 of 12 target accounts convert (not 6), you’re at +$18K ARR and won’t break even for 3+ years.

My question: Do you have 18 months of runway to wait for ROI? Or are you betting the company on this?”

My reaction: I hadn’t done the full cost accounting. I’d estimated “3 months dev” but not security audits, sales enablement, or opportunity cost.

CFO AI’s recommendation:

“Before committing $68K, validate willingness to pay with paid pilots. Charge 3 customers $299/mo NOW for priority access to enterprise features as you build. If they won’t pay $299 for a promise, they won’t pay $499 for the real thing.”

COO AI: “Who’s Building This While You Chase Enterprise Deals?”

COO AI’s take:

“Operational reality check:

You have 180 customers at $99-199/mo. That’s your revenue engine TODAY.

If you launch enterprise tier, here’s what happens:

  1. Sales focus shifts: You (founder) spend 60% of time on 12 enterprise prospects, 40% on 168 SMB leads.
  2. Product roadmap shifts: Dev team prioritizes enterprise features (SSO, SCIM, audit logs) over SMB requests (integrations, mobile app, automation).
  3. Support complexity increases: Enterprise customers expect white-glove support. Your 2-person support team now handles 186 customers with wildly different SLAs.

The blindspot: While you chase $60K in enterprise ARR, your SMB motion (which drove $850K ARR) gets neglected.

Historical data: SaaS companies that shift to enterprise see 15-25% churn in SMB base in first year due to product-market fit drift.

Your math:

  • Gain: +$60K enterprise ARR
  • Loss: -$150K SMB ARR (18% churn on $850K base)
  • Net: -$90K ARR

My question: Do you have the team to serve both markets? Or are you abandoning your core to chase a new market?”

My reaction: I hadn’t thought about SMB churn. I assumed enterprise was additive, not substitutive.

COO AI’s recommendation:

“Hire a dedicated enterprise AE before you build. If you can’t close 3-4 enterprise deals with MANUAL workarounds (SSO via Okta support, audit logs via exports), you can’t close them with automated features either. Validate the sales motion before the product investment.”

Market Realist AI: “Your Demand Signals Are Weak”

Market Realist AI’s take:

“You said 12 customers ‘keep asking for enterprise features.’ Let’s examine that.

Question 1: What does ‘asking’ mean?

  • Are they saying: ‘We’d pay $499/mo TODAY if you had SSO’ (strong signal)
  • Or: ‘It would be nice to have SSO someday’ (weak signal)

Most SaaS founders confuse feature requests with buying intent.

Question 2: Why haven’t they churned yet?

If SSO is truly a blocker, why are these 12 accounts still customers? Possible answers:

  • SSO is a ‘nice to have,’ not a ‘must have’
  • They’re locked in by integrations/workflows
  • IT wants SSO, but end users (your champions) don’t care

The danger: You build SSO, they say ‘Great!’, and then don’t upgrade because it wasn’t actually blocking budget expansion.

Comparable market data:

  • Average enterprise tier adoption from existing SMB base: 8-12% (not 50% like your 6-of-12 projection)
  • Applied to your 12 targets: 1-2 conversions, not 6
  • Revenue impact: +$12K ARR, not +$36K

My question: Have you asked these 12 customers to commit to $499/mo NOW with a 6-month roadmap promise? Words are cheap. Signatures aren’t.”

My reaction: I’d conflated “we’d love SSO” with “we’ll pay $400/mo more for it.”

Market Realist AI’s recommendation:

“Send a proposal to all 12 accounts: ‘We’re launching enterprise tier at $499/mo in Q2 with SSO, SCIM, audit logs. Prepay $2,500 today (5 months prepaid) to lock in beta access and priority support.’

If you get 6 signatures, you’ve validated demand. If you get 1-2, you’ve validated enterprise is a nice-to-have, not a priority.”

Game Theorist AI: “What Happens When Your Competitor Launches Free SSO?”

Game Theorist AI’s take:

“Strategic positioning question: What’s your competitor doing right now?

Scenario analysis:

Scenario 1: You announce enterprise tier

  • You start 3-month build
  • Competitor sees your announcement (via your marketing, customer conversations, or LinkedIn posts)
  • Competitor has 2 options:
    • Option A: Build enterprise tier themselves (slower, more expensive)
    • Option B: Add SSO to EXISTING tier for free (faster, cheaper, neutralizes your differentiation)

Which would you choose if you were them?

Historical precedent: When Intercom launched enterprise tier in 2016, Drift added SSO to their mid-tier plan for free within 60 days. Intercom’s enterprise adoption missed targets by 40%.

Your market:

  • You have 180 customers at $850K ARR
  • Your top competitor has ~400 customers at $2M+ ARR (estimated)
  • They have more resources and faster dev cycles

The risk: You invest $68K and 3 months to build enterprise tier. Competitor launches ‘SSO for all plans’ 2 months into your build. Your differentiation evaporates.

My question: What’s your defensible moat if competitors commoditize enterprise features?”

My reaction: I’d assumed we’d have 12+ months to own the enterprise tier positioning. I hadn’t gamed out competitor responses.

Game Theorist AI’s recommendation:

“Don’t lead with enterprise features. lead with enterprise SERVICE. Position tier as ‘Enterprise Success Plan: dedicated CSM, SLA guarantees, quarterly business reviews, priority support’ + SSO/SCIM as bundled value. Harder for competitors to replicate SERVICE than features.”

Chief Strategist AI: “This Doesn’t Fit Your Broader Strategy”

Chief Strategist AI’s take:

“Let’s zoom out. What’s your 3-year vision?

You’ve told me:

  • Current: $850K ARR, 180 SMB customers, strong product-market fit
  • Enterprise tier goal: +$60K ARR in 6 months

Strategic question: Is enterprise tier a PIVOT or an EXPANSION?

If it’s a pivot (you want to become an enterprise company):

  • You need enterprise sales team (not just you)
  • You need 12-18 month sales cycles (enterprise is slow)
  • You need to raise capital (enterprise burns cash upfront)

If it’s an expansion (you want to serve both SMB and enterprise):

  • You need separate product tracks (SMB features vs enterprise features)
  • You need separate go-to-market motions (self-serve vs sales-led)
  • You need 2x team size to serve both

Right now, you’re treating it like a feature launch. It’s not. It’s a strategic fork.

The misalignment: Your SMB motion is working ($850K ARR with 180 customers = efficient growth). Enterprise will slow you down, distract focus, and dilute your positioning.

Alternative strategy: What if you doubled down on SMB?

  • Launch $199/mo ‘Pro’ tier with power-user features (advanced analytics, API access, white-labeling)
  • Target upgrade path: $99 → $199 (less friction than $99 → $499)
  • Addressable market: 180 existing customers (not 12)
  • Dev investment: $25K (smaller scope)
  • Revenue potential: 40 customers upgrade = +$48K ARR (comparable to enterprise, lower risk)

My question: Why are you chasing 12 enterprise customers when you have 180 SMB customers who already trust you?”

My reaction: I’d gotten seduced by “enterprise” prestige. But Chief Strategist AI was right. SMB is working. Why pivot?

Wildcard AI: “What If Enterprise Customers Kill Your Culture?”

Wildcard AI’s take:

“Unconventional angle: Have you worked with enterprise customers before?

Enterprise customer behavior (from patterns across SaaS companies):

  1. Security questionnaires: 40-page spreadsheets asking about your data encryption, compliance certifications, and incident response procedures. You’ll spend 20-30 hours per deal on security reviews.

  2. Procurement delays: ‘We love it, legal just needs to review the contract.’ 6 weeks later: ‘Can you redline our MSA?’ 4 weeks later: ‘Finance needs approval for $499/mo.’ Enterprise deals take 4-6 months to close.

  3. Feature demands: ‘We’ll sign if you add [specific integration].’ You build it. They sign. Then: ‘Actually, we also need [another thing].’ Enterprise customers negotiate leverage.

  4. Support expectations: ‘Why did it take 4 hours to respond?’ (Your SMB customers are fine with 24-hour support. Enterprise expects <2 hours, white-glove service.)

Cultural impact:

  • Your team loves SMB customers (quick decisions, collaborative, appreciate your work)
  • Enterprise customers will slow you down, demand custom solutions, and create support burden

Founder burnout risk: You’ll spend 60% of your time on 6 enterprise customers who generate $36K ARR, while neglecting 174 SMB customers who generate $814K ARR.

Historical example: Basecamp famously REMOVED enterprise features and refocused on SMB because ‘enterprise customers were making us miserable.’

My question: Are you prepared for the cultural shift of serving enterprise? Or will you resent these customers 6 months in?”

My reaction: I’d romanticized enterprise customers as “better” customers. Wildcard AI reminded me they’re just DIFFERENT. And maybe not compatible with our culture.

KEY TAKEAWAY: Six AI models attacking your strategy from different angles reveals blind spots single AI misses. CFO caught cash flow gaps, COO caught operational complexity, Market Realist caught weak demand signals, Game Theorist caught competitive vulnerability, Chief Strategist caught better alternatives, and Wildcard caught cultural fit issues.

The Final Verdict: Solutions’ Consolidated Output

After all six AI models debated, Solutions synthesized the findings into a prioritized action plan:

Critical Flaws Identified

  1. Cash flow risk (CFO AI): $68K investment for $60K ARR = 13+ month payback, assumes no churn
  2. Operational blindspot (COO AI): No team to serve enterprise while maintaining SMB; historical 15-25% SMB churn risk
  3. Weak demand signals (Market Realist AI): “Asking for features” ≠ willingness to pay; 8-12% conversion rate more realistic than 50%
  4. Competitor response risk (Game Theorist AI): Competitor can neutralize with free SSO faster than you can launch

PIVOT: Launch ‘Pro’ tier for SMB power users instead of enterprise tier

Why:

  • Lower investment ($25K vs $68K)
  • Faster time-to-market (6 weeks vs 3 months)
  • Larger addressable market (180 customers vs 12)
  • Lower competitive risk (SMB feature differentiation harder to commoditize)
  • Better cultural fit (maintains fast-moving, customer-friendly positioning)

Pro tier features:

  • Advanced analytics dashboard
  • API access (500 calls/day)
  • White-labeling (remove Surmado branding)
  • Priority support (4-hour SLA vs 24-hour)

Pricing: $199/mo (vs current $99/mo base tier)

Target: 40 of 180 customers upgrade (22% adoption rate, conservative)

Revenue: +$48K ARR (comparable to enterprise plan, 1/3 the investment)

Validation approach:

  1. Email 40 highest-usage customers with Pro tier proposal
  2. Offer early-bird pricing: $179/mo if they commit within 7 days
  3. Target: 10 commitments (proves demand)
  4. Build features based on committed revenue (not speculation)

KEY TAKEAWAY: The adversarial debate didn’t just say “don’t do enterprise tier”. It revealed a better alternative. Pro tier for SMB power users had comparable revenue potential ($48K vs $60K), lower investment ($25K vs $68K), and better strategic fit with existing customer base.

What I Did Next: The $50 Decision

Cost of Solutions report: $50

Time invested: 15 minutes to submit strategy, 15 minutes to review report

Outcome: Completely changed my roadmap

What I Didn’t Build (Avoided Costs)

  • Enterprise tier ($68K investment, 13+ month payback)
  • SSO/SCIM/audit logs (3 months dev time)
  • Enterprise sales hiring ($80K+ salary for AE)
  • SOC 2 audit ($15K+ for compliance certification)

Total avoided costs: ~$180K in first year

What I Built Instead

  • Pro tier for SMB power users ($25K investment)
  • Advanced analytics, API access, white-labeling (6 weeks dev)
  • Validated demand with prepaid commitments (12 customers prepaid $2,388 total = funded 10% of build)

Results after 4 months:

  • 38 customers upgraded to Pro tier ($199/mo)
  • +$45.6K ARR (vs projected $60K for enterprise, but 1/3 the investment)
  • Zero SMB churn (vs projected 15-25% if we’d chased enterprise)
  • Payback period: 6 months (vs 13+ months for enterprise)

ROI on Solutions: $50 → avoided $180K bad investment → 3,600x return

KEY TAKEAWAY: One $50 adversarial report prevented $180K in bad investment and redirected to a better strategy that delivered $45.6K ARR in 4 months. The ROI isn’t just avoiding costs. It’s finding better alternatives you weren’t considering.

Why Single AI Validation Is Dangerous

ChatGPT’s response to my strategy: “This is solid. Here’s how to execute.”

ChatGPT’s blindspots:

  • Didn’t challenge cash flow assumptions
  • Didn’t question demand signals
  • Didn’t model competitor response
  • Didn’t flag operational complexity
  • Didn’t consider cultural fit

Why? ChatGPT is trained to be helpful and agreeable. It validates your thinking and helps you execute. Not stress-test whether you SHOULD execute.

The problem: Most founders use AI for validation, not interrogation.

The 3 Failure Modes of Single-AI Strategy Testing

Failure Mode #1: Confirmation Bias Amplification

How it works: You ask ChatGPT to validate your plan. ChatGPT finds ways to make it work (because that’s helpful). You feel validated. You execute a flawed plan.

Example from my case:

  • I asked: “Does this enterprise strategy make sense?”
  • ChatGPT said: “Yes, here’s how to execute”
  • I didn’t ask: “What could go wrong?” or “Should I do something else instead?”

The fix: Adversarial testing forces AI to challenge you, not validate you.

Failure Mode #2: Optimistic Scenario Planning

How it works: Single AI assumes your projections are realistic. It doesn’t model pessimistic scenarios or historical base rates.

Example from my case:

  • I projected: 6 of 12 customers convert (50% conversion rate)
  • ChatGPT accepted this and built execution plan around it
  • Market Realist AI flagged: Historical enterprise tier adoption is 8-12%, not 50%

The fix: Multi-model debate surfaces base rates and pessimistic scenarios you’re ignoring.

Failure Mode #3: Missing Competitive Dynamics

How it works: Single AI evaluates your strategy in isolation, not as part of a competitive game.

Example from my case:

  • I assumed: We launch enterprise tier → customers upgrade → revenue grows
  • ChatGPT didn’t ask: What will competitors do when they see this?
  • Game Theorist AI flagged: Competitor can neutralize with free SSO in 60 days

The fix: Game theory perspective models how competitors respond to your moves.

How Adversarial Debate Works (The Solutions Methodology)

Traditional approach (single AI):

  1. You describe your strategy
  2. AI helps you refine and execute it
  3. You move forward with validation bias

Adversarial approach (Solutions):

  1. You describe your strategy
  2. Six AI models attack it from different angles (finance, operations, market reality, competition, strategy, wildcard)
  3. Models debate each other, surfacing conflicts and tradeoffs
  4. You see flaws BEFORE you execute

The key difference: Adversarial testing assumes your strategy is flawed and works backward to find the flaws. Single AI assumes your strategy is viable and works forward to execute.

The 6 AI Perspectives (And Why You Need All of Them)

CFO AI - Financial rigor

  • Catches: Cash flow gaps, payback period miscalculations, runway risks
  • My case: Flagged $68K investment with 13+ month payback

COO AI - Operational reality

  • Catches: Execution gaps, team capacity limits, operational complexity
  • My case: Flagged SMB churn risk while chasing enterprise

Market Realist AI - Demand validation

  • Catches: Weak signals, optimistic projections, unvalidated assumptions
  • My case: Challenged 50% conversion assumption (historical: 8-12%)

Game Theorist AI - Competitive dynamics

  • Catches: Competitor responses, strategic vulnerabilities, positioning risks
  • My case: Predicted competitor would launch free SSO to neutralize differentiation

Chief Strategist AI - Strategic alignment

  • Catches: Mission drift, misaligned priorities, better alternatives
  • My case: Questioned why we’re chasing 12 enterprise customers vs doubling down on 180 SMB

Wildcard AI - Unconventional perspectives

  • Catches: Cultural fit, founder psychology, non-obvious risks
  • My case: Warned about enterprise customer behavior killing team culture

Together, these six perspectives catch what single AI misses.

The Real Costs of Bad Strategy (Why $50 Is Cheap)

My avoided costs (by NOT building enterprise tier):

  • $68K build investment
  • $80K+ enterprise AE salary
  • $15K SOC 2 audit
  • 3 months opportunity cost
  • Projected 15-25% SMB churn ($128-213K ARR loss)

Total avoided cost: $290K+ in first year

What I invested instead:

  • $50 Solutions report
  • $25K Pro tier build
  • $0 new hires (used existing team)

Outcome: +$45.6K ARR with 6-month payback vs +$60K ARR with 13+ month payback and massive risk

ROI on adversarial testing: Prevented $290K mistake for $50 = 5,800x return

Common Strategy Mistakes Adversarial Testing Catches

Based on Solutions reports across 200+ strategies, here are the most common flaws single AI misses:

Top 5 blindspots:

  1. Cash flow timing (68% of strategies)

    • Founders project revenue impact, ignore cash flow timing and investment costs
    • CFO AI catches: Payback period, runway burn, opportunity cost

  2. Demand signal strength (71% of strategies)

    • Founders conflate “customers asked for it” with “customers will pay for it”
    • Market Realist AI catches: Weak signals, optimistic conversion rates, lack of prepayment validation

  3. Operational complexity (54% of strategies)

    • Founders assume team can execute while maintaining current business
    • COO AI catches: Capacity constraints, skill gaps, support burden

  4. Competitor response (62% of strategies)

    • Founders plan in isolation, don’t model competitive reactions
    • Game Theorist AI catches: How competitors neutralize your advantage, price wars, feature commoditization

  5. Strategic misalignment (44% of strategies)

    • Founders chase shiny opportunities that drift from core mission
    • Chief Strategist AI catches: Better alternatives, mission drift, resource misallocation

What to Do Next

Option 1: Test Your Strategy with Single AI (Free, Risky)

  1. Open ChatGPT or Claude
  2. Describe your strategy
  3. Ask: “Does this make sense?”
  4. Get validation and execution advice
  5. Execute with blind spots

Time: 15 minutes Cost: Free Risk: High (confirmation bias, missing adversarial perspectives)

Option 2: Run Adversarial Strategy Testing with Solutions ($50)

What you get:

  • 6-model adversarial debate (CFO, COO, Market Realist, Game Theorist, Chief Strategist, Wildcard)
  • Each model attacks your strategy from different angle
  • Models debate each other to surface conflicts and tradeoffs
  • Consolidated action plan with flaws identified and alternatives recommended

How it works:

  1. Submit your strategy (business plan, go-to-market plan, product roadmap, pricing change, etc.)
  2. Solutions runs 6-model adversarial debate (takes ~15 minutes)
  3. You receive comprehensive report showing what each AI challenged and why
  4. You make decision with full awareness of risks and alternatives

Time: 15 minutes to submit, 15 minutes to review Cost: $50 Risk: Low (you see flaws before you execute)

Get a Solutions report

Option 3: DIY Adversarial Testing (Free, Time-Intensive)

If you want to try adversarial testing yourself without Solutions:

  1. Write your strategy (1-2 pages, be specific about goals, timeline, resources, assumptions)

  2. Prompt each AI perspective separately:

    • CFO AI: “You are a CFO. Review this strategy and challenge the financial assumptions. What cash flow risks am I missing?”
    • COO AI: “You are a COO. Can my team actually execute this while maintaining current business? What operational gaps do you see?”
    • Market Realist AI: “You are a market realist. Challenge my demand assumptions. What signals are weak? What am I being overly optimistic about?”
    • Game Theorist AI: “You are a game theorist. How will competitors respond to this strategy? How can they neutralize my advantage?”
    • Chief Strategist AI: “You are a chief strategist. Does this align with my broader mission? What better alternatives exist?”
    • Wildcard AI: “You are a contrarian thinker. What unconventional risks am I missing? What non-obvious blindspots exist?”

  3. Synthesize the challenges across all six perspectives

  4. Revise your strategy based on flaws identified

Time: 2-3 hours Cost: Free (your time) Tradeoff: Manual synthesis vs automated debate and prioritization

The Bottom Line

Single AI validation feels thorough because it gives you detailed execution plans.

Adversarial testing feels uncomfortable because it challenges your assumptions and pokes holes in your logic.

But discomfort is the point.

I ran my GTM strategy through ChatGPT and got validation.

I ran it through Solutions and got challenged by six AI models from different perspectives.

ChatGPT said: “Execute this.”

Solutions said: “Don’t execute this. Here’s why, and here’s a better alternative.”

One $50 adversarial report saved me $180K in bad investment and redirected me to a better strategy that delivered $45.6K ARR in 4 months.

Your strategy probably looks solid to you. It might even look solid to ChatGPT.

But have you stress-tested it?

Ready to stress-test your strategy? Run a Solutions report ($50) and see what six AI models challenging each other reveal about your plan. Validation is easy. Adversarial testing is honest.