Invest1 distinct publisher3 min readUpdated
The vibe-coding startup runs about 150 agents internally at 10 to 15 per human. That ratio, not the avatars, is the number buyers will end up defending in board decks.
The Investor · Invest desk

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Anything AI has released Skydive, a product that lets customers create AI agents to fill internal roles either by describing the job or by picking from a catalog of four templates, according to reporting by Fast Company Design published August 20 [1]. The agents connect to Slack, email and the web, each gets its own phone number so it can be reached on iMessage, and they keep running when human staff are offline [2] - which means the buyer is not purchasing a tool so much as a line item that behaves like a colleague. The vendor is unusually exposed here, because it runs the product on itself. Roughly 150 agents operate inside the company, about 10 of which co-founder Dhruv Amin describes as essential to operations, and each human employee directs somewhere between 10 and 15 [6]. Work backwards and that implies a human team of roughly 10 to 15 people [7]; the essential subset is under 7 percent of the fleet [8]. Anything was founded last year by Amin and Marcus Lowe, both former Google product leads, as a platform for nontechnical people to build apps with AI [3], and it raised an $11 million round at a $100 million valuation from Footwork, Uncork Capital, Bessemer Venture Partners and M13 [4]. The company says more than a million people have used Anything to build millions of apps, including project trackers and CRM platforms [5]. The internal roster is specific enough to audit. Emma investigates reported bugs across logs, databases and code and drafts replies; Otis runs engineering operations and forecasts launch dates; Clementine handles brand imagery; Tilda manages the CEO's inbox and calendar and briefs him each morning; JB, the office manager, places Instacart orders and tells the team when they arrive; another agent acts as Amin's vice president of finance [9]. They carry human names and animated, mouthless avatars that Amin says are deliberately "not quite human": "We're not trying to trick someone that this is a human" [10]. Emma exists because of a support crunch: in May, after the AI app builder Mocha announced its shutdown and pointed users toward Anything, support volume ran to roughly triple normal, and Amin says an employee built Emma and added it to the help desk rather than hiring support staff [11]. More recently, an engineering agent helped Lowe ship 56 pull requests in a single Sunday while he directed it from Slack on his phone [12]. The failure log is on the record too, which is the more useful part. Amin says an agent misread a database calculation and told him the company had lost $1 million to a credit bug that did not exist [13]. An employee who asked an agent to write emails to potential influencer partners without reviewing drafts had the draft version sent out [14]. Marketing lead Zaria Zinn asked for one email draft and got several, already loaded into the email provider, and says the tendency to over-deliver makes it necessary to spell out where an agent should stop [15]. "You deal with the mess ups because like in some cases there's like, no other choice," Amin says, citing the cost of running a full influencer program [16]. Context for the pitch: Grand View Research values the market at $10.9 billion and projects $182.9 billion by 2033 [17], a roughly 17-fold increase [18], and a 2025 McKinsey report found 62 percent of respondents saying their organizations are at least experimenting with AI agents [17]. Nvidia earlier this year announced agent-building software and racks designed to run agents, shifting its strategy's primary focus away from GPUs [19]. Watch whether the 10-to-15 ratio survives growth. Amin says he expects to hire more humans as costs fall and revenue grows, but with a higher bar, and that junior developer roles at Anything are becoming more senior, though the team recently hired a 21-year-old college dropout [20].
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Ranked by verification strength, evidence, and original report placement.
Roughly 150 agents operate inside Anything AI, about 10 of which Amin describes as essential to operations, and each human employee directs somewhere between 10 and 15 of them.
Anything AI released Skydive, letting customers create AI agents to fill internal roles by describing the job or picking from a catalog of four templates, according to reporting by Fast Company Design published August 20.
The agents plug into Slack, email and the web, each gets its own phone number so it can be reached on iMessage, and they run continuously including when human staff are offline.
Anything AI was founded last year in San Francisco by Dhruv Amin and Marcus Lowe, both former Google product leads, as a platform for nontechnical people to build apps with AI.
The company raised an $11 million round at a $100 million valuation from Footwork, Uncork Capital, Bessemer Venture Partners and M13.
Internal agents include Emma (investigates reported bugs across logs, databases and code and drafts replies), Otis (engineering operations and launch-date forecasts), Clementine (brand imagery), Tilda (CEO's inbox, calendar and morning brief), JB (office manager, places Instacart orders for snacks and drinks and tells the team when they arrive), plus one acting as Amin's vice president of finance.
Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
Single vendor-sourced account with unusually specific failure detail
Every claim in the cluster traces to one publisher relaying one Fast Company Design piece, and inside that piece to two company insiders. The specifics are granular and include on-record failures, which raises credibility above pure promotion, but there is no independent verification, no logs, benchmarks, error rates, customer references or financials, and the strongest quantitative claims (million-plus users, 56 pull requests in a day, the Nvidia strategy shift) carry no supporting detail.
Real first-party deployment, no evidenced external customer use
Adoption evidence is essentially one deployment: Anything AI running roughly 150 agents on itself, including a help-desk agent stood up during a May support spike and agents embedded in engineering ops, marketing and executive workflows. That is genuine dogfooding rather than a demo. For Skydive itself the source shows a launch and no named customers, seats, pricing or usage; the million-user platform figure covers the app builder, not the agent product, and the McKinsey 62% figure measures market-wide experimentation, not this product.
AI-employee framing runs ahead of verified outcomes
The 'AI employees with phone numbers' framing and the agent-per-head ratio are asserted by the company selling the capability, with no verified output quality, cost of operation, or customer results, while the market is sized at a 16.8x growth path. The gap is moderate rather than severe because the same source publishes three concrete failures, the founder concedes 'a hard transition' and explicitly says headcount will still grow, and a staff member draws hard limits on agent capability - self-corrections that most launch coverage omits.
Vendor launch narrative with investor and supplier tailwinds
The primary sourcing is a company launching a paid product, whose founders and four disclosed investors benefit from a $100M valuation narrative and from the claim that a 10-person team can run like a much larger one. Supporting figures come from a research firm that sells market sizing and a chip vendor repositioning around agent infrastructure, both of which gain from an agent-adoption story. The publisher is a secondhand relay, so no independent adversarial reporting counterweights those incentives.
Moderate: facts are clear, verification is not
What was said and by whom is unambiguous, and the internal metrics are internally consistent (150 agents, 10-15 per human, ~10 essential all cohere), so the descriptive layer is reliable. Confidence is capped by single-publisher, secondhand sourcing, absence of any external check on the operational numbers, and no post-launch data on Skydive customers or on agent error rates.
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1 article · August 21, 2026