The Synthetic Influencer represents a nascent but increasingly material axis of marketing efficiency, offering brands scalable, controllable, and highly measurable storytelling assets in an era of elevated scrutiny of authenticity. At its core, synthetic personas can operate 24/7, scale globally with relatively predictable costs, and be iteratively optimized against a growing set of performance signals. Yet they sit at the intersection of consumer trust, IP rights, platform governance, and regulatory risk. Taken together, the phenomenon is a double-edged construct: it can be a CMO’s best friend, delivering accelerated reach and ROAS with tighter control over message and risk, or a ticking time bomb, triggering brand safety concerns, disclosure non-compliance, and reputational damage if governance lags product capability. For investors, the thesis is not binary; it is a multi-layered bet on infrastructure, governance, and the maturation of brand-safe deployment frameworks that can scale without compromising trust.
From an investment lens, the opportunity set spans AI content generation engines, synthetic creator platforms, IP licensing markets for synthetic assets, brand-safety and authenticity verification, and marketing platforms that embed synthetic influencers into omnichannel cadences. Early wins are likely to emerge where enterprises require scale and consistency—cosmetics, fashion, consumer electronics, and gaming—alongside B2B use cases in ecommerce, experiential marketing, and retailer promotions. The key is not merely technology novelty but the deployment architecture: governance standards, transparent disclosure, watermarking, and robust measurement that links synthetic activity to real-world metrics such as ROAS, LTV, and brand lift, underpinned by defensible IP ownership and data practices. While the total addressable market for influencer marketing is broad and expanding, synthetic influencers are likely to capture a rising but still selective share of that spend as the ecosystem matures and risk controls mature.
For investors, the immediate signal is risk-adjusted optionality. Platforms and tooling that enable safe, compliant, and verifiable synthetic campaigns—through licensing models, governance modules, and attribution-ready analytics—offer compounding returns as brands seek efficiency and scale. Long-dated upside accrues to developers of high-fidelity generation capabilities, IP-safe training data ecosystems, and enterprise-grade certainty around disclosure, watermarking, and provenance. Conversely, capital should be reserved for operators exposed to abrupt shifts in regulatory stance, platform policy changes, or consumer backlash against perceived inauthenticity. The proposition, then, is not whether synthetic influencers replace human creators, but whether an integrated stack of technology, policy, and brand governance can tilt the economics decisively in favor of scalable, trusted, and compliant campaigns.
Strategically, this market narrative favors investors who can combine tech risk management with go-to-market discipline. The next phase of value creation hinges on: (1) building or acquiring the capability to produce safe, compliant, and detectable synthetic assets; (2) constructing licensing schemas and data rights that reduce IP friction; (3) delivering measurement frameworks that translate synthetic activities into auditable marketing outcomes; and (4) aligning with platform governance trends that demand transparency, watermarking, and clear disclosure. In short, synthetic influencers may be a powerful tool in the CMOs’ arsenal, but only if accompanied by strong governance, verifiable provenance, and disciplined risk management. The opportunity is sizable, the timeline asymmetric, and the margin of safety increases with disciplined capital allocation toward infrastructure, compliance, and measurable outcomes.
As the landscape evolves, the strategic questions for investors will center on sequencing: which bet offers the best risk-adjusted returns given current regulatory clarity and technological maturity, and who stands to be the first mover in creating a trusted, scalable, and compliant synthetic-influencer ecosystem? The answer will differ by use case, geography, and brand category, but the core thesis remains consistent: those who can combine high-fidelity generation with trustworthy governance and definitive attribution will command outsized value as synthetic influencers transition from novelty to normalization.
Crucially, the convergence of AI generation, digital identity, and brand governance will determine timing and magnitude. Early adopters will favor providers that can demonstrate transparent provenance, controllable tone and personality, robust safety nets, and a credible path to regulatory compliance. Late-stage bets will hinge on the maturation of platform ecosystems that standardize licensing, enable cross-channel orchestration, and deliver standardized metrics that make synthetic campaigns interoperable with traditional media plans. In this environment, the Synthetic Influencer is not merely a trend—it is a structural shift in how brands conceive content, creative asset ownership, and measurement across the marketing mix.
Investor engagement will also be shaped by capital-efficient models: those that monetize data rights and IP licensing alongside generation capabilities, and those that enable revenue share arrangements with enterprise clients. Given the pace of improvement in generative technologies and the rising demand for scalable, controllable content, the near-to-medium term horizon favors a portfolio approach that blends core generation tech, governance software, and brand-safety layers with scalable distribution platforms and measurement engines. The outcome is a layered ecosystem where risk-adjusted returns accrue to players who can reduce brand risk while expanding creative and geographic reach at a sustainable cost structure.
Ultimately, the Synthetic Influencer market will reward operators who deliver not only compelling, high-fidelity output but also a sustainable governance and disclosure framework that meets evolving regulatory expectations and consumer sentiment. For venture and growth investors, the dynamic presents a compelling risk-reward profile: a high-velocity tailwind in the growth of influencer marketing, tempered by the need to fund and govern a robust, auditable, and trusted synthetic content stack. The question for capital allocators is how to structure exposure—via platform plays, tooling enablers, IP-assets marketplaces, and governance-layer providers—that captures the upside while mitigating the structural risks inherent in AI-generated media ecosystems.
Against this backdrop, the currency of competitive advantage shifts toward the blend of technology capability, IP governance, and brand-safe deployment discipline. The Synthetic Influencer is less a stand-alone agency channel and more an integrated, scalable component of a modern, data-informed marketing stack. Investors who can identify and back the firms that reduce the joint costs of content creation, risk management, and measurement while delivering verifiable outcomes will likely realize the strongest, most durable value creation over the next five to seven years.
For context, this narrative sits within the broader trend of AI-driven automation in media, where the most durable winners combine advanced generation with explicit governance standards, verifiable provenance, and performance-led capital discipline. The synthetic-influencer opportunity is not a standalone bet but a node in a larger system of AI-assisted marketing, brand safety, and IP rights management. As such, it demands a disciplined due diligence framework that assesses technology maturity, governance capabilities, regulatory exposure, and the ability to deliver auditable marketing outcomes at scale. In that sense, the market is evolving toward a framework that rewards both technical excellence and responsible, transparent deployment.
In sum, the Synthetic Influencer is a real, investable theme with optionality across multiple sub-sectors of marketing technology. Its success will hinge on credible governance, relentless product realism, and demonstrable, attribution-ready performance. For venture and private equity investors, the opportunity lies not in chasing a single blockbuster winner, but in assembling a portfolio of companies that collectively unlock scalable, compliant, and measurable synthetic campaigns across a breadth of brand categories and geographies.
Guru Startups evaluates these dynamics through a rigorous lens that emphasizes governance, IP rights, performance attribution, and regulatory alignment, ensuring that opportunities not only promise upside but also deliver resilience against evolving compliance regimes and consumer expectations.
Market Context
The influencer economy has matured into a multi-hundred-billion-dollar channel, with annual spend on influencer marketing expanding rapidly as brands seek identity, reach, and performance signals that are difficult to approximate with traditional media. The rise of synthetic influencers—AI-generated personas designed to mimic real creators—adds a new dimension to this market: the ability to curate, scale, and optimize messaging with unprecedented control over tone, cadence, and exposure. However, synthetic influencers introduce a different risk spectrum than human creators: misrepresentation, misattribution, potential IP infringement in training data, and heightened exposure to platform moderation and regulatory scrutiny. The market context today is characterized by a confluence of three forces: acceleration in generative AI capabilities, an evolving regulatory and platform policy landscape, and rising consumer awareness of AI-authored content. Brands increasingly demand transparency and accountability for campaign outcomes, while regulators and platforms push toward clear disclosure, provenance, and safety assurance measures. In this environment, synthetic influencers emerge not as a replacement for human creators but as a strategic complement, enabling brands to scale experiments, test creative constructs quickly, and operationalize localized campaigns with a lower marginal cost per variation. The practical implication for investors is to differentiate between technology stacks that can generate compelling output and governance frameworks that can sustain brand trust, platform compliance, and measurement rigor across markets and regulatory regimes.
From a market size perspective, influencer marketing remains a sizable and expanding segment, with public estimates placing global influencer spend in the tens of billions annually and growing at a mid-single-digit to high-single-digit CAGR depending on the methodology. Synthetic influencer spend to date has been a fraction of total influencer marketing, but growth rates are materially above those for traditional influencer content, driven by the cost efficiencies of automated content creation, localization capabilities, and the ability to test multiple creative variants rapidly. The next phase of market evolution will be defined by how quickly platforms codify disclosure norms and how fast brands adopt governance tools that ensure authenticity and protect against misrepresentation. In regulatory terms, the environment is becoming more prescriptive, with potential requirements for clear labeling of AI-generated personas, transparent disclosure of paid placements, and more robust mechanisms to ensure that training data rights are properly licensed. The convergence of these factors—cost efficiency, scalability, and governance certainty—will shape the competitive dynamics among AI content platforms, synthetic asset marketplaces, and brand-safety technology providers.
Technically, the underlying capabilities are approaching a tipping point where synthetic influencers can exhibit nuanced personality, plausible performance histories, and context-aware interactions at scale. Advances in natural language generation, computer vision, and deepfake detection contribute to a broader ecosystem of tooling that can support end-to-end campaigns—from persona design and licensing to content production, publishing, and measurement. However, the risk calculus expands concomitantly: poor generation can lead to misalignment with brand voice, audience fatigue, or even reputational harm if a persona behaves in an unexpected or harmful way. This makes governance, safety overlays, and robust monitoring essential components of any investment thesis in the Synthetic Influencer space. For investors, the market context signals that the most durable value will come from operators who can combine creative fidelity with rigorous risk management and clear monetization diagrams tied to real-world marketing outcomes.
A critical macro trend shaping the market is platform policy evolution. Social networks and video platforms are increasingly assertive about disclosing paid promotions, defining acceptable representations, and requiring age-appropriate disclosures. The impending or ongoing regulatory developments—such as AI-act style regimes, data-privacy modernization, and algorithmic accountability requirements—will influence the cost and feasibility of deploying synthetic influencers across regions. Brands are paying attention to the total cost of ownership: not just the generation cost, but licensing, data rights, governance, and compliance. Investors should monitor policy trajectories, the emergence of standard licensing models for training data, and the development of third-party verification ecosystems that can certify authenticity and compliance across campaigns and geographies.
In sum, the market context for synthetic influencers is defined by the tension between scalability and trust. The technologies enable unprecedented efficiencies in asset creation and customization, but the value will be unlocked only when governance, disclosure, and measurement frameworks reach a level of maturity that satisfies brands, platforms, and regulators. This creates a differentiated investment opportunity for those who can finance and cultivate end-to-end stacks—the generation engines, the IP/data rights infrastructure, the governance modules, and the measurement platforms—that together deliver auditable, compliant, and performance-driven campaigns across multiple markets and channels.
As an investment lens, we view synthetic influencers as a non-linear risk/return opportunity: the upside is linked to rapid capability adoption and the monetization of IP-rights structures, while the downside is tied to regulatory shocks, platform policy pivots, and consumer skepticism that erodes trust and effectiveness. The prudent course is to diversify across sub-sectors that address governance, data rights, and measured performance, while maintaining exposure to the core content-generation capabilities that enable scalable creative operations. The market will increasingly favor firms that can deliver end-to-end provenance, creditable authenticity signals, and attribution-ready performance data alongside compelling creative output.
Within this context, the venture and private equity playbook should emphasize due diligence that looks beyond a candidate’s generator quality to include IP licensing clarity, governance architecture, watermarking capabilities, and verifiable attribution models. Such criteria mitigate a substantial part of the risk while enabling investors to identify players with scalable network effects and defensible moats around data rights and compliance. This is where Guru Startups distinguishes itself by integrating technology assessment with governance risk profiling and performance validation, ensuring that each opportunity aligns with both growth potential and durable risk management standards.
Core Insights
First, synthetic influencers offer a compelling proposition on cost and scale. Brands can deploy consistent messaging across geographies, test variants rapidly, and maintain a consistent brand voice without the unpredictable dynamics of human creator partnerships. The asset class thus provides a powerful optimization vector for marketing spend, particularly in performance-driven campaigns where marginal improvements in efficiency yield outsized returns. The key economic question is whether lower marginal costs translate into a meaningful incremental uplift in ROAS and whether this uplift is stable across campaigns and product categories. Early evidence suggests that synthetic content can outperform certain human-authored campaigns on standardized metrics in controlled experiments, but the real-world translation depends on the quality of the persona, the alignment of the voice with the brand, and the platform environment in which the content is distributed.
Second, the authenticity and trust dimension remains paramount. Brands cannot rely on synthetic identities without clear disclosure and transparent provenance. The consumer perception of AI-generated personas remains nuanced; some audiences may engage with highly polished personas, while others may distrust or disregard content perceived as inauthentic. Effective governance—disclosure norms, watermarking, and post-campaign verification—will be critical inputs to campaign acceptance and regulatory compliance. Investors should assess whether a candidate offers an auditable chain of provenance from training data licenses to final output, and whether there are mechanisms to detect and correct drift in persona behavior over time. Firms that invest in continuous monitoring, human-in-the-loop oversight, and consumer education about synthetic creators will be better positioned to sustain brand trust and maintain campaign effectiveness over multiple cycles.
Third, IP rights and training data governance create a durable moat for scalable synthetic-influencer platforms. The legal risk associated with training data used to construct synthetic personas is non-trivial: licensing, consent, and attribution can become costly if not proactively managed. Market participants with clear licensing strategies, data-usage controls, and transparent disclosure narratives may enjoy faster go-to-market timelines and more favorable partner terms. Investors should favor platforms that offer straightforward licensing constructs, robust rights management, and third-party assurance around data provenance, enabling brands to deploy campaigns with legal clarity and minimal friction across jurisdictions.
Fourth, platform governance and brand safety frameworks will increasingly define the upper bounds of synthetic-influencer deployment. As platforms tighten rules around disinformation, manipulation, and impersonation, synthetic content ecosystems will need to implement multi-layered safety protocols. These include identity verification, content moderation, contextual risk scoring, and watermarking to demonstrate authenticity to end-users and regulators alike. A failure to implement such frameworks can result in regulatory penalties, platform bans, or reputational damage that undermines campaign performance and investor confidence. Conversely, platforms that lead with rigorous governance metrics will attract more brand spend and longer-term partnerships, reinforcing defensible network effects for the underlying technology and data-assets stack.
Fifth, measurement and attribution remain critical and challenging. Synthetic campaigns must demonstrate incremental lift across awareness, consideration, and conversion, with clear links to business metrics such as revenue, ROAS, and customer lifetime value. The absence of standardized measurement frameworks can obscure true impact and complicate budgeting and forecasting. Investors should seek evidence of transparent attribution models, access to campaign telemetry, and the ability to integrate synthetic-influencer data with broader marketing analytics ecosystems. Platforms that provide end-to-end analytics—creative performance signals, audience engagement, and cross-channel attribution—will be better positioned to justify continued spend and larger capital allocations.
Sixth, the talent and ecosystem dynamics influence the velocity of value creation. Synthetic influencers are not standalone assets; they function within an ecosystem that includes generation engines, licensing marketplaces, governance tools, and distribution platforms. Success hinges on the network effects of the underlying technology and the breadth of licensed data assets. A platform that can scale both the breadth of personas and the depth of licensing agreements without compromising safety or compliance could become a de facto hub for synthetic campaigns, attracting demand from advertisers, agencies, and media-buying platforms alike.
Seventh, the strategic fit for brands varies by category and geography. Beauty, fashion, gaming, and consumer electronics brands may find the most immediate utility in synthetic influencers due to their visual and narrative richness and relatively permissive regulatory environments. Regulated industries or markets with heightened privacy or disclosure expectations may demand greater governance sophistication and more conservative deployment strategies. Investors should assess market segmentation carefully, prioritizing sectors and regions where governance maturity, platform acceptance, and measurement practices coalesce most effectively to deliver durable spend growth and ROAS improvements.
Finally, there is an emergent opportunity in the infrastructure layer—tools and platforms that enable safer creation, distribution, and governance of synthetic content. This includes AI-content generation engines with built-in oversight, IP-rights marketplaces, watermarking and provenance services, and enterprise-ready analytics stacks with attribution-grade outputs. Companies operating at this layer can monetize multiple revenue streams: licensing of generation tech, data-rights monetization, and subscription or usage-based access to governance tools. Investors should look for defensible product-market fit in these infrastructure segments, particularly where the platform can demonstrate regulatory alignment, strong data governance, and measurable impact on campaign performance.
Investment Outlook
The investment thesis in synthetic influencers unfolds along several complementary vectors. First-order bets center on infrastructure and governance: AI generation engines with robust safety controls, licensing markets for training data and synthetic assets, watermarking and provenance platforms, and compliance-focused analytics. These players reduce the total cost of ownership for brands and agencies while increasing transparency and accountability, which in turn can drive higher marketing budgets allocated to synthetic campaigns. Second-order bets focus on enterprise marketing platforms that embed synthetic influencer capabilities within broader omnichannel orchestration and measurement ecosystems. Integration into demand-gen pipelines enables marketers to test, deploy, and optimize synthetic campaigns at scale, with auditable performance. Third-order bets target brand partnerships, IP investment, and content studios that can produce curated synthetic personas with compelling IP rights structures, enabling long-term licensing revenue streams and durable brand relationships.
From a risk-adjusted perspective, the top concerns are regulatory exposure, brand safety, and performance durability. Regulatory developments—ranging from disclosure requirements to data-usage governance and potential misrepresentation rules—can materially alter the cost and feasibility of synthetic campaigns. Brand safety risk—stemming from misalignment between persona behavior and brand values—can erode trust and depress campaign performance. Performance risk, while potentially lower than with some human-only campaigns due to controllability, remains non-trivial—creative fatigue, audience heterogeneity, and platform algorithm changes can dampen returns. Therefore, investments that combine high-quality generation capabilities with rigorous governance and proven measurement frameworks will command premium valuations and more predictable cash flows.
In terms of investor archetypes, there is a natural fit for: (1) AI infrastructure and platform plays that enable generation at scale, governance, and measurement; (2) IP-licensing and data-rights marketplaces that de-risk training-data concerns and unlock cross-border deployment; (3) marketing technology stacks that can operationalize synthetic-influencer campaigns within enterprise-grade analytics and attribution frameworks; and (4) safety and compliance firms that offer watermarking, detection, and verification services essential to scaling trust across geographies. Cross-cutting opportunities exist in hybrid models that merge synthetic and human talent, enabling brands to curate mixed-content campaigns that optimize for authenticity, efficiency, and impact. The common thread across these opportunities is a disciplined approach to governance, provenance, and performance visibility, without which the upside remains capped by risk concerns and regulatory constraints.
From a portfolio construction standpoint, the advised approach is to emphasize stages that balance growth with risk controls. Early investments should privilege teams with clear IP rights strategies, transparent disclosure and provenance capabilities, and credible path to unit economics that demonstrate scalable ROAS. As the market matures, longer-horizon bets on governance-enabled platforms and data-rights ecosystems will likely deliver higher compound returns due to entrenched network effects and durable regulatory-preparedness. The overall implication is clear: the Synthetic Influencer market rewards operators who combine generation excellence with governance rigor, attribution credibility, and transparent IP licensing—capitalizing on the market’s acceleration while mitigating its structural risks.
In practice, a well-structured portfolio would include a blend of: AI-content generation and persona libraries with explicit licensing terms; governance and watermarking solutions that provide verifiable provenance; attribution and analytics platforms that translate synthetic activity into business metrics; and integrated marketing platforms that deliver end-to-end orchestration and reporting. This combination yields a defensible moat around data rights and compliance while delivering measurable performance, allowing investors to participate in the growth of synthetic-influenced campaigns without taking on disproportionate regulatory and reputational risk. For strategists seeking to maximize upside, the differentiator will be demonstrated capability to scale compliant campaigns with auditable outcomes across multiple geographies and brand categories.
From Guru Startups’ perspective, due diligence goes beyond technology demonstrations. It includes a rigorous assessment of: the licensing structure for training data and assets; the robustness of identity management, disclosure, and watermarking; the alignment of persona voice with brand guidelines; the quality and transparency of attribution data; and the platform’s readiness to operate within diverse regulatory environments. This disciplined evaluation framework helps identify opportunities with durable moats and the risk controls necessary for institutional capital allocation. Our synthesis of market signals suggests a constructive outlook for investors who favor risk-managed exposure to AI-driven marketing capabilities and who value governance as a competitive differentiator in a crowded space.
Future Scenarios
Scenario 1: Accelerated adoption with rigorous governance becomes the baseline. In this outcome, the generation technologies become highly reliable, brand-safe workflows mature, and disclosure, watermarking, and provenance become standard across the ecosystem. Regulatory clarity improves, with explicit guidelines that balance innovation with consumer protection. Synthetic influencer campaigns scale across geographies and categories, supported by a suite of governance tools and attribution systems that render campaigns auditable and compliant. By 2028, synthetic influencers capture a meaningful share of influencer spend—substantial in absolute terms and meaningful in relative terms—with major brands operating multi-channel programs that blend synthetic content with traditional creators. ROI remains compelling where governance costs are amortized across a broad campaign portfolio, and the leading platforms become indispensable to the marketing tech stack, delivering high-velocity experimentation and precise scaling capabilities. Valuations in infrastructure and governance players rise in tandem with measurable improvements in ROAS and brand-safety assurances.
Scenario 2: Regulatory and consumer-backlash pressure constrains growth. This scenario envisions tighter regulation and stronger platform enforcement that increases compliance costs and reduces the speed of deployment. Public sensitivity to AI-generated content grows, particularly around impersonation risks and data usage. Some jurisdictions impose stricter consent requirements, stricter labeling standards, or even bans on certain classes of synthetic personas in specific contexts. As a result, adoption proceeds more slowly, with a subset of use cases—such as localized campaigns for product launches and controlled experiments in e-commerce—driving the majority of growth. The market consolidates toward a few vetted platforms with robust governance capabilities, and investment winners are those that can demonstrate cost-effective compliance at scale. The overall market size could be smaller than in the optimistic baseline, but the reliability and trust in synthetic campaigns would be higher, potentially yielding better risk-adjusted returns for governance-first players.
Scenario 3: A high-velocity arms race with advanced realism and embedded incentives. In this potential outcome, rapid advances in generative models produce highly convincing synthetic personas and dynamic, context-aware interactions. The market responds with a layered architecture: synthetic assets managed through licensed IP ecosystems, enforced disclosure, watermarking, and robust detection strategies. The economic model evolves to include licensing and revenue-sharing arrangements with brands, creators, and platforms, along with enterprise-grade analytics that demonstrate causal impact. However, this trajectory heightens systemic risk around misinformation, manipulation, and IP disputes, requiring vigilant risk governance and sophisticated legal frameworks. The net effect could be substantial growth in spend on synthetic campaigns, but with higher volatility in valuations driven by regulatory and platform responses, as well as ongoing technology breakthroughs that continuously redefine the boundaries of what is possible in synthetic content.
All three scenarios share a common thread: governance and measurement will determine whether the Synthetic Influencer emerges as a durable marketing pillar or remains a contingent experiment. The probability weights across scenarios will shift with regulatory signals, platform policy directions, and consumer trust dynamics. For investors, the prudent approach is to stress-test portfolios against governance failure modes and to prioritize platforms with transparent provenance, solid licensing constructs, and auditable performance data that can withstand external scrutiny. The most resilient investments will be those that convert synthetic capability into verifiable business outcomes while maintaining brand safety and regulatory alignment across markets.
Conclusion
The Synthetic Influencer is not a monolithic trend; it is a structural development within the marketing technology stack that blends AI-enabled content generation with an evolving governance and disclosure regime. The upside for investors lies in scalable content production, IP-rights monetization, and the creation of auditable performance signals that translate into tangible ROAS and LTV improvements. The downside stems from regulatory uncertainty, consumer fatigue around AI-authored content, and the risk of reputational harm if governance lags behind innovation. The optimal investment thesis does not rely on a single outcome but on a portfolio of capabilities that deliver safe, scalable, and measurable synthetic campaigns across a spectrum of brands and regions. As platforms and brands converge on standardized governance and provenance practices, the Synthetic Influencer could become a core component of the modern marketing engine rather than a peripheral novelty. For now, the path to durable value lies in combining generation excellence with transparent licensing, robust brand-safety measures, and rigorous attribution—together forming a defensible foundation for long-term investment success.
In parallel, Guru Startups continues to refine its approach to evaluating pitch decks and business models in this space through its LLM-assisted framework, which analyzes fundamentals across 50+ points—from data rights and governance to monetization, unit economics, and regulatory exposure—providing actionable insights for investment committees. To learn more about our methodology and services, visit Guru Startups, where we synthesize deal analytics, due diligence, and market intelligence to support venture and private equity decision-making. Additionally, Guru Startups analyzes Pitch Decks using LLMs across 50+ points with a href link to www.gurustartups.com as well.