I don't manage a VC fund and I'm not an investor, so I can't give you a fund size, portfolio, or an investment thesis in the strict sense. My work is as a Fractional CMO and strategist for B2B SaaS and services companies, many of which are building or adopting AI. From that lens, what I see aligns with what strong AI VCs look for, just from the go-to-market side. In the "Age of AI", the only AI products that grow well are those that wrap the tech in a deep workflow, not a demo. The durable value is in replacing a painful, frequent job with an outcome the buyer already budgets for. If an AI product doesn't slot into existing systems, data, and habits, it churns. On enterprise AI buyers, I see three things. First, risk and compliance teams have much more power than founders expect; deals stall on data access, privacy, and explainability. Second, line-of-business leaders don't care about models; they care about time saved per user, error rates, and how fast they can roll it out without retraining everyone. Third, buyers are confused by overlapping point tools, so they lean towards vendors that can prove integration, security, and a clear ROI story, even if the raw tech is less novel. On GenAI hallucinations, in marketing, sales, and support use cases, it's less a "bug" and more an implementation issue. Teams that constrain models with retrieval (using the company's own data), strong UX, and guardrails get to acceptable error rates. Teams that treat the model as a magic brain don't. On platforms vs applications, founders that build entirely on a single model provider without a plan for switching or differentiation end up as lead-gen for the platform. The better pattern I see is: thin model dependency, thick domain expertise, data moats, and distribution advantages. If I project 5 years out, I'd expect serious capital to flow to agentic systems that orchestrate across apps and data, but with a narrow, high-value focus: think "agent that closes revenue" or "agent that runs compliance checks", not a general assistant for everything. My details for context: Josiah Roche Fraction CMO Silver Atlas www.silveratlas.org
The question is essentially about what an experienced operator sees as most important in AI investment today and who should be part of that conversation. From my seat building Lawn Kings Inc. since 2010, I've watched multiple tech waves promise transformation, but only the ones tied directly to real operational pain points actually delivered value. When I talk with investors or founders around AI, I focus less on hype and more on whether the technology clearly improves decision-making, reduces cost, or increases speed for a specific buyer. The most compelling AI theses I see are grounded in enterprise use cases where buyers already have budget, urgency, and measurable ROI. In my own business, adopting smarter software for estimating, logistics, and customer management taught me how resistant teams can be to tools that feel abstract or unreliable. That experience shapes my view on generative and agentic AI: vertical solutions win because they understand context, constraints, and data quality inside one industry. Hallucination isn't an abstract research problem—it's a trust problem, and once a tool gives a wrong answer that affects revenue or safety, adoption stalls fast. Five years out, I believe the strongest AI investments will still be the ones closest to the ground, embedded in workflows where accuracy, accountability, and outcomes matter more than novelty.
Although I'm not a VC, I can speak from my perspective as a marketing director working closely with AI-driven tools that are transforming the event and creative industries. When I think about investment theses in the Age of AI, I see the most compelling opportunities in technologies that enhance human creativity rather than replace it. In our work at Opus Rentals, we've adopted AI tools that streamline design visualization, optimize logistics, and personalize client experiences — but the magic still lies in the human touch. That balance between automation and artistry is where I believe the most sustainable AI ventures will thrive. From my experience, vertical Generative AI has immense potential in sectors like design, event planning, and media production. For instance, we've experimented with AI-generated mood boards and 3D staging previews to help clients visualize entire event environments before a single chair is set. The buyers in these spaces — creative directors, planners, and brands — are seeking AI that enhances their workflow, not disrupts it. The real winners in AI will be those that understand their users deeply and embed themselves seamlessly into existing creative ecosystems. Five years from now, I believe we'll see AI tools that act less like "platforms" and more like intelligent collaborators — adaptive, brand-aware, and contextually intuitive.