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The Global AI Marketplace Opportunity: A Comprehensive Feasibility Analysis

The market gap is real and massive. As of October 2025, no unified platform-agnostic AI marketplace exists despite explosive demand, severe fragmentation pain, and $100+ billion in venture funding validating the opportunity. This analysis reveals a rare window to build critical infrastructure for the AI ecosystem—but success requires navigating significant technical, regulatory, and competitive challenges.

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Market Demand: Strong and Growing Fast

The AI developer tools market is experiencing explosive 27% annual growth, expanding from $4.86 billion in 2023 to a projected$26+ billion by 2030. More telling than raw numbers is the demonstrated pain: 76% of developers now use AI tools, but organizations juggle 231-342 disconnected applications on average, with 45% of workers experiencing productivity losses from constant context switching. Two-thirds of businesses remain stuck in pilot phases, unable to scale AI precisely because of this fragmentation. The demand signals are unambiguous. ChatGPT alone reached 800 million weekly active users by October 2025, while 78% of enterprises have integrated AI into at least one business function. Most significantly, industry analysis consistently identifies fragmentation as “AI’s silent killer”—a $252 billion corporate AI investment is being undermined by tool sprawl, with developers explicitly seeking unified solutions. The race is already underway: AWS launched its AI Agent Marketplace with 900+ tools in July 2025, Microsoft unified its marketplace in September, and Anthropic just released Claude Code plugins in October 2025. Venture capital validates this opportunity decisively. AI companies captured 33% of all global VC funding in 2024 ($100+ billion), with January 2025 alone seeing$5.7 billion invested. The shift from infrastructure to application layer and unified platforms signals where smart money sees the next wave of value creation.

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The Competitive Landscape: Fragmented with No Clear Winner

The most critical finding is that no platform-agnostic unified AI marketplace exists today. The current landscape segments into three disconnected categories, none offering true cross-platform functionality: AI-native marketplaces like OpenAI’s GPT Store (159,000 public GPTs, 700M+ weekly users) and Hugging Face Hub (350,000+ models) dominate their respective niches but lock users into specific ecosystems. OpenAI’s market share actually declined from 76% to 59.5% between January 2024 and 2025, suggesting no single player will monopolize the space. Anthropic’s October 2025 plugin launch and Microsoft’s September marketplace unification show major players still searching for the right model. Prompt and template marketplaces like PromptBase (220,000+ prompts) address narrow use cases but lack the breadth for comprehensive AI workflows. Meanwhile, general plugin platforms like VS Code Marketplace (60,000+ extensions, 3.3 billion installs) and Zapier (7,000+ app integrations) demonstrate proven marketplace mechanics but weren’t built AI-native. The whitespace is striking. Hugging Face comes closest to platform-agnostic with true multi-framework support, but focuses on models and datasets rather than agents and applications. Every major player—OpenAI, Anthropic, Microsoft, AWS, Google—is building walled gardens to capture ecosystem value. This creates a massive opportunity for a neutral platform that works everywhere, analogous to how npm became essential infrastructure despite competing interests.

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Technical Feasibility: Complex but Achievable

Building a unified AI marketplace is technically feasible using proven patterns from npm, Docker Hub, Chrome Web Store, and VS Code Marketplace, enhanced with emerging AI standards like Model Context Protocol (MCP). The architecture requires sophisticated multi-layered infrastructure but follows established blueprints. Core infrastructure needs include distributed databases separating metadata (fast access) from binary storage (cost-efficient), global CDN for sub-100ms latency worldwide, and RESTful APIs complemented by JSON-RPC 2.0 endpoints for MCP integration. The technical insight from existing registries is clear: npm’s evolution from single CouchDB to microservices architecture demonstrates that separation of concerns—metadata versus binaries, discovery versus delivery—is critical for scale. Platform-agnostic integration presents the primary technical challenge. Different AI platforms use heterogeneous APIs (OpenAI’s Chat Completions, Anthropic’s Messages API, AWS Bedrock runtime), incompatible authentication schemes, and varying capabilities (context windows from 8K to 128K tokens). The solution pattern combines a unified API abstraction layer using OpenAI-compatible interfaces as baseline, adapter patterns for platform-specific requirements, and version compatibility matrices. MCP is emerging as the de facto integration standard, with 1,000+ open-source connectors by February 2025 and support from all major players including Anthropic, Microsoft, OpenAI, Google, Cloudflare, and AWS. Security requirements are non-negotiable and AI-specific. The platform needs multi-engine malware scanning, sandboxed runtime execution for behavioral analysis, and dependency vulnerability tracking across the supply chain. AI introduces novel threats—prompt injection attacks, data exfiltration through model outputs, malicious tool calls—requiring defense-in-depth with input validation, output filtering, tool execution consent (MCP mandates explicit user approval), and audit logging. VS Code’s experience is instructive: only 1,800 of 45,000 publishers are verified (4%), and malicious extensions have been found, underscoring that security cannot be retrofitted. The recommended implementation follows a three-phase approach: Phase 1 MVP (6-9 months) focuses on core registry with MCP as primary integration, basic security, and templates/GitHub repos only. Phase 2 (6-12 months) adds multi-platform adapters for OpenAI, Anthropic, HuggingFace, plus private registries and enhanced security. Phase 3 (12+ months) achieves complete platform coverage with GPU compute marketplace and self-hosted options. Total timeline to production MVP: 12-18 months with a team of 13-20 people across backend, frontend, DevOps, security, and AI/ML engineering, requiring $700K-$2.8M annual infrastructure costs at scale.

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Regulatory Landscape: Navigate Carefully or Face Existential Risk

The regulatory environment for a global AI marketplace is unprecedented in complexity, with compliance costs estimated at $425K-$1.6M annually and existential risks from non-compliance. The EU AI Act, effective August 2026 for high-risk systems, creates the most severe penalties: up to €35 million or 7% of global turnover for prohibited AI practices. The EU AI Act introduces a risk-based framework requiring classification of all AI systems. High-risk categories (employment, education, essential services, law enforcement) must register in an EU database, implement risk management systems, maintain technical documentation, ensure human oversight, and conduct post-market monitoring. General-purpose AI providers like OpenAI and Anthropic face additional obligations including transparency reports, training data summaries, and copyright compliance. The Act creates criminal liability for violations, not just civil penalties. Platform liability extends beyond AI-specific regulations. The Digital Services Act (effective February 2024) imposes marketplace obligations including Know Your Business Customer (KYBC) due diligence, seller traceability, and content moderation with 6-month transparency reporting. Platforms with 45M+ monthly EU users qualify as Very Large Online Platforms (VLOPs), requiring annual systemic risk assessments and independent audits. Penalties reach 6% of global annual turnover. Copyright infringement represents the highest immediate legal risk. Multiple billion-dollar lawsuits are pending (Authors Guild, Getty, New York Times vs. OpenAI/Microsoft/Stability AI) over training data and AI outputs. The New York Times case, with a court allowing contributory infringement claims to proceed in 2025, establishes that platforms facilitating copyright infringement face liability. Section 1202 violations for removing copyright management information carry statutory damages of $2,500-$25,000 per violation (GitHub lawsuit involves $9M+ potential liability). Payment processing requires PCI-DSS compliance for all entities handling cardholder data, with Level 1 merchants (6M+ transactions annually) requiring Qualified Security Assessor audits. The strategic recommendation is to use Stripe Connect ($0.25$0.25 per payout) to outsource compliance burden. Marketplace facilitator tax obligations add complexity: platforms must collect sales tax when reaching economic nexus in U.S. states and act as “deemed suppliers” for EU VAT. AI export controls introduced by the U.S. in January 2025 create criminal liability. Advanced computing items require global licensing with a three-tier framework: unrestricted for 18 allies, annual quotas for other countries (26.9M TPP), and prohibition for China and embargoed nations. AI model weights above 10^26 operations face first-ever controls under the Foreign Direct Product Rule, with compliance deadline May 15, 2025. Violations result in criminal prosecution and export privilege revocation. Data protection varies dramatically by jurisdiction but centers on GDPR’s €20 million or 4% global turnover penalties. Meta’s €1.2 billion fine in 2023 for inadequate data transfer safeguards demonstrates enforcement severity. Cross-border data transfers require adequacy decisions, Standard Contractual Clauses, or Binding Corporate Rules. China, Russia, India, Vietnam, Indonesia, and Nigeria impose data localization requirements. The compliance roadmap demands immediate establishment of legal entity structure (Delaware C-Corp with EU representative recommended), PCI-compliant payment processing, and GDPR/CCPA privacy policies. Medium-term priorities include EU AI Act classification, copyright protection mechanisms, and export control screening. Ongoing obligations include quarterly regulatory monitoring, annual risk assessments, and 30-day data subject request handling.

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Business Model Optimization: Hybrid Approaches Win

The optimal monetization strategy combines subscription tiers with transaction fees and consumption-based pricing, evolving as the platform scales. Analysis of 150+ vendors and current market data reveals that successful AI marketplaces avoid single-revenue models in favor of sophisticated hybrids that balance predictable income with growth alignment. Commission structures in comparable marketplaces range from 10% (Gumroad) to 30% (Apple App Store, though regulatory pressure is forcing reductions). The EU Digital Markets Act forced Apple from 30% to 17%, while U.S. courts ruled against anti-steering in April 2025, allowing external payment links. For AI marketplaces, Andreessen Horowitz analysis shows specialized high-value agents command 20-30% commissions, while commodity agents settle at 10-15%. The trend is clear: 10-20% with demonstrable value delivery is increasingly the defensible range. Developer revenue share should follow a tiered structure starting at 70/30 (developer/platform) and escalating to 85/15 for top performers generating $100K+ monthly GMV. This aligns with industry standards—App Store and YouTube use 70/30 splits, Gumroad uses 90/10—while adding performance incentives. Quality bonuses (+2% for 4.5+ star ratings, +2% for under 5% refund rates) and exclusivity bonuses (+5% for 12-month platform commitment) encourage ecosystem health. Envato demonstrates this model’s effectiveness with exclusive author rates escalating from 12.5% to 37.5% based on volume. Consumption-based pricing is becoming essential for AI tools. McKinsey research shows consumption models overtaking pure subscriptions as AI performs work rather than just supporting it. The hybrid “bucket model” works best: base subscription with included credits, pay for overages. HubSpot offers 500-5,000 AI credits per tier with additional credits purchasable in 1,000-credit increments. ServiceNow separates “Now Assist” credits from base subscriptions. This approach provides predictable baseline revenue while capturing upside from power users. The recommended three-phase model starts conservatively: Year 1 focuses on freemium with optional premium and 15% transaction fees (no platform fee) to build critical mass toward 10K+ monthly active users. Year 2 introduces hybrid subscriptions at $29-$99/month with tiered commissions (12-15%) and included API call allotments, targeting 10-15% paid conversion. Year 3 scales to full enterprise offerings with custom pricing, consumption credits fungible across the product family, and outcome-based pricing for measurable use cases. Additional revenue streams include featured marketplace placement ($500-$5,000/month), verified developer badges ($99/year), API infrastructure fees beyond included usage, training/certification programs, and enterprise support (15-20% of contract value annually). The key is modularity—customers pay only for what they need—while maintaining simplicity in core offerings. Critical metrics for marketplace success differ from traditional SaaS. Track dual-sided customer acquisition costs (both developers and users), lifetime value to CAC ratios (target 3:1 minimum, 4:1 healthy), and marketplace-specific indicators: gross merchandise value (GMV), take rate percentage, active developers versus total developers, and buyer-to-seller ratios. Two-sided marketplaces face unique challenges as seller and buyer lifespans differ significantly, and idle sellers still count toward CAC. LTV calculations should account for transactions over lifetime multiplied by GMV per transaction and take rate. McKinsey’s finding that 65% of buyers prefer fungibility—the ability to exchange usage commitments across products—suggests unified credit systems work well. Only 16% of SaaS incumbents have commercialized AI as standalone products, but those achieving it report 2-3x higher customer traction and revenue, indicating proper AI monetization as competitive advantage rather than feature bloat.

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Strategic Considerations and Success Factors

Several critical factors separate potential success from failure in this opportunity. Network effects and cold start present the classic chicken-egg problem for two-sided marketplaces. Developers won’t build for platforms without users; users won’t join without quality tools. The solution combines aggressive early developer incentives (low or zero commissions initially), anchor tenant strategy (securing marquee developers pre-launch), and focusing on a specific vertical niche before expanding horizontally. Data shows platforms delaying monetization until 10K+ MAU achieve 30% higher long-term growth than those monetizing prematurely. Quality control mechanisms become paramount given low verification rates (VS Code: 4% of publishers verified) and security incidents in existing marketplaces. Implement multi-stage review: automated malware scanning and dependency vulnerability checks pre-publication, human review for featured/promoted listings, community ratings and reviews with verified purchase requirements, and reputation scoring with verified developer badges. The platform’s brand depends on users trusting that tools won’t compromise security or data. Discovery and search challenges multiply with platform scale. VS Code Marketplace with 60,000+ extensions demonstrates the problem—finding the right tool becomes harder as selection grows. Solutions include AI-powered recommendation engines trained on usage patterns, curated collections for common workflows (starter packs, industry-specific bundles), semantic search understanding intent beyond keywords, and personalization based on user’s tech stack and usage history. Platform lock-in concerns among developers may resist another walled garden. The differentiation must be clear: this is Switzerland, not another fiefdom. Provide portable standards (MCP), allow self-hosting options for enterprises, offer transparent data export, and avoid proprietary APIs that create switching costs. The value proposition should be network access and infrastructure, not captivity. Go-to-market strategy should follow enterprise software patterns for the B2B segment while maintaining self-serve for SMB and individual developers. Start with a beachhead market—perhaps AI coding tools or API integrations—where pain is most acute and value most measurable. Expand to adjacent categories only after achieving liquidity (sufficient buyers and sellers for consistent transactions) in the initial category. Partnership opportunities exist with major platforms (OpenAI, Anthropic, AWS) as neutral distribution channel rather than competitor. Developer incentives extend beyond revenue share. Provide analytics dashboards showing usage patterns and customer feedback, comprehensive documentation and SDKs reducing integration friction, marketing exposure through featured placements and case studies, and community building through forums, hackathons, and certification programs. Referme IQ research shows referral-acquired users demonstrate 37% higher retention—build viral mechanics into the developer experience. Ecosystem fragmentation in AI is both problem and opportunity. The Model Context Protocol’s adoption by major players (Anthropic, Microsoft, OpenAI, Google, AWS, Cloudflare) in 2024-2025 suggests industry awareness of interoperability needs. Position as the platform that makes MCP accessible to developers who don’t want to implement each integration from scratch. The technical complexity of maintaining adapters for 10+ AI platforms is the moat—it’s substantial work that few will replicate well. Competitive positioning requires differentiation from tech giants’ marketplaces. AWS, Microsoft, and Google will own their ecosystems but face credibility challenges as neutral platforms. They’re simultaneously marketplace operators and dominant sellers—an inherent conflict. The independent platform can credibly claim neutrality, focusing exclusively on making developers successful rather than promoting proprietary services. Hugging Face proves this model viable with $4.5 billion valuation despite$40 million revenue, based entirely on community goodwill and open-source positioning.

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Adoption Barriers and Risk Mitigation

Several significant barriers could derail execution. Technical integration complexity with 10+ heterogeneous AI platforms creates ongoing maintenance burden. APIs change weekly at some providers, breaking integrations and frustrating users. Mitigation requires dedicated platform engineering team, automated compatibility testing in CI/CD pipelines, version pinning options for stability, and clear migration guides for breaking changes. Budget 2-3 engineers full-time just for adapter maintenance. Price compression risk emerges from LLM inference costs dropping 80% annually. This puts pressure on pricing power—what commands premium today may be commodity tomorrow. Mitigation focuses on outcome-based value propositions rather than compute costs, investing in complex multi-step workflows that maintain pricing power, and regularly reviewing pricing quarterly rather than annually to stay ahead of cost curves. Developer churn risk increases if commission rates don’t reflect value delivered. If the platform is merely distribution with minimal support, developers route around to direct sales. Mitigation requires continuously adding value: superior analytics, customer acquisition that developers couldn’t achieve alone, infrastructure that reduces their operational burden, and community that provides feedback and validation. Envato’s model keeping exclusive authors with escalating revenue shares shows loyalty can be bought—but must be earned. Usage unpredictability in consumption-based models makes forecasting difficult for both customers and the platform. Customers fear surprise bills; the platform struggles with revenue predictability investors expect. Mitigation includes pre-commit options with volume discounts, usage alerts and spending caps, “true forward” pricing adjustments, and transparent cost calculators. ServiceNow and HubSpot demonstrate this approach working at scale. Regulatory compliance burden grows as the platform scales globally. The $425K-$1.6M annual compliance cost assumes mature scale; early-stage companies face proportionally higher burdens. Mitigation starts with conservative approach—operate in fewer jurisdictions initially, focus on U.S. and EU first, expanding only as compliance capabilities mature. Use Stripe Atlas for entity formation and Stripe Connect for payment processing to outsource complexity. Retain specialist counsel in EU AI Act and data protection—these aren’t areas for generalists. Customer concentration risk affects two-sided marketplaces uniquely. Value depends on liquidity for both buyers and sellers. If top 10 developers generate 50%+ of GMV, their departure tanks the marketplace. Mitigation involves actively cultivating the long tail, revenue sharing that reduces incentive to defect, and multi-year deals with key developers including equity participation for true alignment. Margin pressure from AI compute costs creates structural challenges absent in traditional SaaS. Pure software has gross margins of 80-90%; AI platforms face 60-70% due to infrastructure. Mitigation includes model selection optimization (routing to cheapest capable model), aggressive caching strategies, negotiating volume discounts with infrastructure providers (AWS, Azure, GCP), and customer-funded compute for high-volume use cases.

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Viability Assessment: Strong but Timing-Dependent

The comprehensive analysis supports a positive viability assessment with important caveats about execution and timing. Market timing is favorable for 2025-2026 launch. The pain of fragmentation is acute and well-documented, with 63% of developers saying leaders don’t understand their challenges and organizational inefficiencies consuming productivity gains. Meanwhile, no dominant unified platform has emerged—OpenAI’s declining market share (76% to 59.5%) and the October 2025 timing of Anthropic’s plugin launch show the market remains fluid. The window exists but won’t stay open indefinitely; by 2027, consolidation will likely reduce opportunities for new entrants. Technical feasibility is proven through existing marketplace patterns (npm, Docker Hub, VS Code Marketplace) and emerging standards (MCP). The 12-18 month MVP timeline is aggressive but achievable with proper resourcing. The critical success factor is MCP-first strategy—betting on the protocol that has backing from every major player (Anthropic, Microsoft, OpenAI, Google, AWS, Cloudflare) rather than building proprietary integration layers. Economic viability depends on achieving marketplace liquidity. The financial model works with realistic assumptions: 10K+ monthly active users by Year 1, 10-15% paid conversion by Year 2, and blended 15% take rate producing $1.5M annual GMV at 10K MAU scales to$150M at 1M MAU. With $700K-$2.8M infrastructure costs and $1.6M compliance/operations costs, the platform reaches profitability at approximately 100K-200K monthly active users. The LTV:CAC ratio of 3:1 at maturity is achievable given marketplace network effects and low marginal cost of serving additional users once infrastructure exists. Regulatory compliance is manageable but requires upfront investment and ongoing vigilance. The $400K-$1.6M annual compliance budget is substantial for early-stage companies but necessary for global operation. The highest-risk areas—EU AI Act, copyright infringement, export controls—all have clear mitigation strategies: conservative AI system classification, licensed training data and output filters, automated export screening. The alternative is existential risk from €35 million fines or billion-dollar litigation. Competitive dynamics favor neutrality over vertical integration. While tech giants have distribution advantages, they face credibility gaps as self-interested platforms. AWS, Microsoft, and Google push their own AI services alongside third-party tools, creating inherent conflicts. The independent platform can credibly claim Switzerland status, though maintaining this requires discipline—resisting the urge to compete with marketplace participants, as Amazon’s private label problems demonstrate.

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Execution Requirements: What Success Demands

Phase 1 priorities (Months 0-9) focus on technical foundation and initial liquidity:

• Assemble core team: 4-6 backend engineers, 2-3 frontend, 2-3 DevOps/SRE, 2-3 security engineers, 2-3 AI/ML engineers, 1-2 product managers (13-20 people total)
• Build MVP with MCP-first integration, basic security (malware scanning, signature verification), and S3/CDN distribution
• Recruit 50-100 anchor developers pre-launch with preferential terms
• Target beachhead market (recommended: AI coding tools or API integrations)
• Establish Delaware C-Corp with EU representative, PCI-compliant payment processing via Stripe Connect
• Initial funding requirement: $2-4M for team, infrastructure, legal

Phase 2 priorities (Months 10-18) expand platform capabilities and market reach:

• Add multi-platform adapters (OpenAI, Anthropic, HuggingFace, AWS Bedrock)
• Implement advanced security (sandboxing, behavioral analysis, dependency tracking)
• Launch private registries for enterprise customers
• Build developer analytics dashboards and usage insights
• Expand to 1,000+ developers and 10K+ monthly active users
• Begin monetization with hybrid model (freemium + 15% transaction fees)
• Series A funding: $10-20M for scaling team and infrastructure

Phase 3 priorities (Months 19-36) achieve full platform vision:

• Complete platform coverage (Google, Azure, additional providers)
• Launch GPU compute marketplace for heavy workloads
• Implement self-hosted option for air-gapped enterprise environments
• Add advanced features (A/B testing, canary deployments, analytics integrations)
• Scale to 100K+ monthly active users across multiple verticals
• Optimize monetization with tiered subscriptions and consumption pricing
• Series B funding: $30-50M for international expansion and category expansion

Critical success metrics to monitor:

• Developer metrics: Active developers, tools published per developer, developer retention rate
• User metrics: Monthly active users, paid conversion rate, usage frequency
• Transaction metrics: Gross merchandise value, average transaction size, take rate achieved
• Quality metrics: Average tool rating, refund rate, support ticket volume
• Financial metrics: GMV growth, revenue growth, gross margin, LTV:CAC ratio, burn multiple

Team composition beyond core engineering:

• Developer relations: 2-3 FTEs by Year 2 for community building and support
• Compliance: 2-4 FTEs for regulatory, security, and legal operations
• Sales (enterprise): 2-4 FTEs by Year 2 for $100K+ contracts
• Customer success: 2-3 FTEs for onboarding and retention
• Marketing: 2-3 FTEs for developer acquisition and brand building

Total estimated investment through profitability:

• Years 1-2: $5-15M (seed through Series A) for MVP and initial scale
• Years 3-4: $20-40M (Series A/B) for platform completion and market expansion
• Total: $25-55M to reach profitability at 100K-200K MAU
• Timeline: 36-48 months from founding to profitability

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Recommendation: Proceed with Strategic Execution

The research conclusively supports proceeding with the global AI marketplace concept, with the critical qualifier that success requires sophisticated execution across technical, regulatory, and business model dimensions simultaneously. The opportunity is real. A $26+ billion market growing 27$100+ billion in VC funding validating the space create favorable conditions. Most importantly, no platform-agnostic unified player exists—every major marketplace locks users into specific ecosystems. This whitespace won’t persist indefinitely, making 2025-2026 the optimal launch window. The challenges are substantial but surmountable. Technical complexity of maintaining 10+ platform integrations, regulatory burden of $400K-$1.6M annual compliance costs, and competitive pressure from tech giants all present real obstacles. However, proven architectural patterns exist (npm, Docker Hub, VS Code Marketplace), emerging standards reduce integration burden (MCP), and regulatory compliance creates moat that competitors must also navigate. The neutrality positioning—Switzerland rather than walled garden—addresses competitive concerns if maintained with discipline. The financial model works with realistic assumptions. Transaction fees of 10-15%, developer revenue shares of 70-85%, and hybrid consumption pricing align incentives while providing sustainable economics. The path to profitability at 100K-200K MAU is achievable within 36-48 months with $25-55M investment—aggressive but comparable to successful marketplace businesses. Three critical success factors determine outcomes: First, MCP-first technical strategy betting on the protocol backed by every major player rather than proprietary approaches. This reduces integration burden and positions the platform as neutral infrastructure. Second, developer-centric business model sharing 70-85% of revenue and providing genuinely useful tools, analytics, and support that justify the take rate. The platform succeeds only if developers succeed—alignment must be authentic. Third, conservative regulatory approach with upfront compliance investment and specialist counsel. The EU AI Act, copyright litigation, and export controls create existential risks that cannot be addressed reactively. The verdict: viable with disciplined execution. The market timing, technical feasibility, and business model all support proceeding. The window is open now but closing as the market matures. Success requires resisting shortcuts—inadequate security, aggressive monetization before proving value, regulatory corners cut—that would undermine long-term viability for short-term gains. For teams with AI technical expertise, marketplace experience, and access to $5-15M seed/Series A funding, this represents one of the most significant infrastructure opportunities in the current AI wave. The unified AI marketplace will exist in 2027—the only question is who builds it successfully.