Partner
Craft VenturesCheck size: No Mike-specific check range is public. Craft's official portfolio attributes angel/seed, venture and growth investments to him, but financing totals and round labels are not his check size. Confirm vehicle, ownership target, reserves and lead appetite directly.
Mike invests in enterprise software and AI products that deliver fast, legible value to an end user and can expand from bottom-up adoption into durable enterprise systems. His work links product architecture to buying behavior: a focused AI stack, proprietary retrieval and data access, excellent productization, a clear ideal customer profile and a repeatable go-to-market motion are more important than a broad market map or generic AI label.
Products that create valuable output immediately, bottom-up user love, strong self-serve or developer adoption, a precise buyer and job-to-be-done, model plus product advantages, access to necessary enterprise data, credible enterprise expansion, fast execution and founders who want hands-on GTM help.
His writing warns against undifferentiated AI products, expansive market maps that ignore buyer demand, unclear ICPs, sales tactics that rely on noise, and tools with onboarding friction or no durable data/retrieval advantage. He has not published an exhaustive personal pass list; treat these as source-grounded signals.
Show the product and time-to-value first. Define user, buyer, job, current workaround, ICP and why now; provide self-serve activation, usage, retention, expansion and sales-cycle data; explain model, data, retrieval, API and product moats; outline bottom-up entry and enterprise conversion; then state round, use of funds and the GTM problem where Mike can help.
Map the model, vector or classification layer and permissioned retrieval mechanism, then identify which layer owns the differentiated customer value.
Measure immediate individual value, self-serve activation and organic adoption, then prove security, administration and expansion into an enterprise contract.
Identify the founder's non-obvious insight from direct operating experience and connect it to a large problem outsiders misunderstand.
Specify ICP, buyer, user, trigger, sales motion, cycle, conversion, payback and expansion rather than relying on a broad market narrative.
For voice or other AI modalities, test whether small quality differences materially improve trust, task completion and customer outcomes.
Voice AI can become a major business interface when a company combines high-quality underlying models, rapid productization, bottom-up traction and products for creators and enterprises.
Enterprise AI adoption centers on a minimum viable stack of models, vector search and permissioned data retrieval, evaluated through real buyer appetite rather than equal-weight market maps.
Crowded SaaS markets and difficult macro conditions require teams to adapt sales execution, sharpen differentiation and replace outdated tactics with a more efficient GTM motion.
Founders with an earned secret about a massive, poorly understood problem can build defensible API products, as SentiLink did around synthetic identity fraud.
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SF-based partner focused on generative AI, APIs, and enterprise SaaS. Previously held go-to-market leadership roles at Dropbox, Slack, and Clearbit. Writes about GTM on his Substack, EarlyGTM. Started his career in real estate private equity at Prudential Global Investment Management.