Andreessen Horowitz has led a $55 million Series A round in Town, an AI-powered personal assistant that integrates with tools like email, calendar, Slack and docs to learn your workflow and proactively suggest or execute tasks. Founded by ex-Plaid/Dropbox CTO Jean-Denis Greze and ex-Google/Dropbox product lead Tony, Town aims to turn raw AI intelligence into practical leverage by holding deep, ongoing context and automating follow-ups, scheduling and other messy operational work.

• Andreessen Horowitz led a $55M Series A in Town, an AI assistant embedded in Gmail, Slack, calendar, docs and WhatsApp that learns your workflow and proactively drafts, schedules and follows up
• Founded by ex-Plaid/Dropbox CTO Jean-Denis Greze and ex-Google/Dropbox product lead Tony, who built early versions organically before investors noticed it spreading through group chats and referrals
• Its moat is accumulated personal context (writing style, recurring meetings, dropped follow-ups) that competitors can't quickly replicate
• Investors are betting the next consumer AI wave is contextual, action-taking assistants rather than smarter chatbots

a16z led a $35 million Series A for Lassie, which builds AI agents to handle billing, insurance claims, payroll and other back‐office work for dental practices. The founders spent months in dental offices mapping workflows and have already onboarded 700 practices, cutting errors and saving 250,000 labor hours a year. Lassie plans to expand beyond dental into broader small-business automation.

• a16z led a $35M Series A for Lassie, an AI agent startup automating dental practice back-office work (billing, insurance claims, payroll)
• Already live in 700+ practices across 49 states, generating $10M annualized revenue and saving 250,000 labor hours/year
• Founders spent months doing hands-on work in a dental office and interviewing healthcare staff before writing code, building deep workflow-specific knowledge that's hard to replicate
• Lassie plans to expand its AI-agent labor model from dental into broader small-business automation

Matthew Gallagher built MEDVi, a telehealth service for GLP-1 weight-loss drugs, using only AI tools and one sibling in under two months. He outsourced medical and logistics functions, automated marketing end-to-end with AI, and drove $400 million in revenue his first year while targeting $1.8 billion next.

• One person (plus one sibling) ran MEDVi to $400M revenue and 250,000 users in a single year by outsourcing prescriptions/logistics and using AI tools for everything else
• MEDVi's 16.2% net margin is roughly double competitors like Hims & Hers
• Gallagher's prior startup, Watch Gang, hit $11M revenue but collapsed under 60 employees, teaching him that headcount kills profitability—directly shaping MEDVi's lean, AI-driven model
• He's targeting $1.8 billion in revenue for 2026 despite criticism over "grey-area" marketing tactics

10x Science built a platform that uses chemistry-based algorithms and AI agents to interpret complex mass spectrometry data, speeding up protein characterization for drug development. Backed by a $4.8 million seed round, it helps biotechs and pharma quickly validate AI-generated treatment candidates. The startup plans to refine its models and expand offerings by integrating broader cellular data.

• 10x Science raised a $4.8M seed round led by Initialized Capital to build AI that interprets mass spectrometry data for drug candidate validation, addressing the bottleneck created by AI protein-folding tools flooding pipelines with candidates.
• Its platform is fully traceable rather than a black box, which matters for regulatory compliance—unlike prior tools that over-promised or failed on complex molecules.
• Early users like Rilas Technologies report it shaves weeks off workflows, with the AI accurately identifying proteins by name and pulling sequences from public databases.
• Long-term plan is to layer cellular data on top of protein structure to create "molecular intelligence," positioning it as a potential go-to analytics engine for AI-driven drug development.

Despite the popularity of startup accelerators, 99% fail to meet expectations due to a lack of effective mentorship and experience among their founders. Successful accelerators like Y Combinator thrive because their leaders possess firsthand knowledge of building billion-dollar companies, which is often missing in most programs. The distinction between true accelerators and startup schools is crucial for understanding their impact on startups.

• 99% of startup accelerators fail to meet expectations, unlike standout Y Combinator
• The key differentiator is mentor quality—most mentors lack firsthand experience building billion-dollar companies
• Many programs calling themselves "accelerators" are actually just startup schools teaching basic entrepreneurship rather than truly accelerating growth
• Y Combinator succeeded partly because Paul Graham and co-founders bet on young, undergraduate founders, defying the belief that only experienced MBAs could build startups