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Chronicles

The story behind the story

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Adept, which aims to build AI systems that can understand and automate any software process, emerges from stealth with $65M led by Greylock and Addition

TechCrunch Kyle Wiggers

Context & Ripple Effects

Adept's stealth exit is the opening move in a bet that the next interface for software is a model that operates it: rather than building another app, the company wants an AI that can understand and drive any software process on a user's behalf. Backers Greylock and Addition are underwriting that thesis at the seed-to-A stage, before the category has a name.

The bet aged quickly into a pattern. Within a year Adept had converted this round into a $350M Series B at a $1B+ post-money valuation, and the agent stack it implied began filling in around it — most visibly with Adapter's later emergence selling the data-control infrastructure those agents need. Parallel rounds like Augment's $227M Series B show the same capital thesis applied to coding specifically.

First-order effects

  • Greylock and Addition secure early positions in a company whose product category — AI executing arbitrary software workflows — had no established leader at the time of the round.
  • Adept gains the runway to train and evaluate models against real software environments, the expensive part of the problem that separates it from thin workflow-automation wrappers.

Second-order effects

  • The rapid follow-on at a $1B+ valuation signals to other investors that agent-style startups can raise at platform multiples, pulling new entrants like Adapter into adjacent layers of the same stack.
  • Software vendors whose products would be driven by such agents face a choice between treating them as a threat to their UIs or exposing APIs that make their tools agent-controllable first.

Third-order effects

  • If models that operate any software process mature, the work of gluing enterprise systems together shifts from human integrators and outsourced services toward model vendors — consistent with reported pressure on formulaic IT-services work and entry-level professional-services roles.
  • A working universal software operator would also concentrate value in whoever controls the agent's access to data and permissions, which is why infrastructure plays like Adapter's are emerging as a distinct layer rather than a feature.

The trend: Venture capital is funding a new class of AI-native companies whose product is not an app but an operator of all apps, with round sizes escalating as the agent-stack layers specialize.

Discussion

  • @reidhoffman Reid Hoffman on x
    Adept takes a different path from other AGI companies. Rather than building general intelligence to take over valuable tasks, they're building AI tools that empower humans to get stuff done.
  • @saammotamedi Saam Motamedi on x
    6/ The next phase of AI will come from building products that people use today - but that also contain the seeds of generality for the future. Adept is doing exactly this, and we're excited to bring Greylock's expertise in enterprise products to bear.
  • @scottbelsky Scott Belsky on x
    developing AI models to use our existing everyday software tools (and underlying APIs) on our behalf is a fascinating approach for near-term productivity gains. https://www.adept.ai/... congrats @jluan and team. https://twitter.com/...
  • @jluan David Luan on x
    A bunch of top ML folks from Google, DeepMind, OpenAI, etc have come together to build Adept! It's a pleasure to be working with this kind and extremely talent-dense crew, incl. the folks who invented Transformer. We're doing something a bit different... (thread) https://twitter.…