The first investor for technical founders. Early backers of Datadog, Chainguard, dbt Labs, Temporal, Modal, Hightouch, Luma, Scribe, and more.

Joined October 2012
144 Photos and videos
Amplify Partners reposted
Until next year, @aiDotEngineer
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The biggest hiring mistake at seed stage: hiring for where you think you’ll be in five years instead of where you are today. A great engineer is only great if they’re the right engineer for your current stage. How to build your hiring plan: amplifypartners.com/blog-pos…
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Amplify Partners reposted
Like @lennypruss I have become pi-pilled and think its approach –– tight, scoped primitives with extremely flexible extensibility built on top –– is going to be a powerful approach across a bunch of domains of software.
You can read a more in depth version in my latest post: amplifypartners.com/blog-pos… Thanks to @davidcrawshaw, @mitchellh, @armon, @schrockn, and @jthandy for pressure-testing these ideas!
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Amplify Partners reposted
“Features are a liability for agents. Every feature expands the decision space an agent must reason about. More features mean more edge cases, more ambiguity, and more failure modes.” so good, thank you for writing this @lennypruss
You can read a more in depth version in my latest post: amplifypartners.com/blog-pos… Thanks to @davidcrawshaw, @mitchellh, @armon, @schrockn, and @jthandy for pressure-testing these ideas!
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Amplify Partners reposted
🎯
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Amplify Partners reposted
FAKE NEWS / switcheroo -- see you Thursday morning at 9am! But it SHALL be lit
See you tomorrow @aiDotEngineer -- always one of my favorite reunions! I’ll be on the main stage at 9am with results from 1,000 AI engineers: models, tools, costs, who’s shipping what with AI, and (of course) whether we’re getting GPUs in space. 🚀
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Amplify Partners reposted
See you tomorrow @aiDotEngineer -- always one of my favorite reunions! I’ll be on the main stage at 9am with results from 1,000 AI engineers: models, tools, costs, who’s shipping what with AI, and (of course) whether we’re getting GPUs in space. 🚀
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Amplify Partners reposted
The power resides in the abstraction, not the capability. Always been true but agents emphasize this fact.
For most of software history, value accrued to whoever owned the most workflows. Features were the main currency. But agents have upended this logic. They don't navigate GUIs; they consume APIs and compose capabilities. In other words, every company is now a dev tools company! As @davidcrawshaw put it: "the best product for an agent is just the best product for a developer." Many know about @HashiCorp's "Tao" concept, which encapsulates this idea well. Users don't want features, they want outcomes. Thus, the job of a product is to collapse complexity into the smallest yet most powerful abstraction that enables those outcomes. Exhibit A is Terraform, whose declarative resource graph was (a) opinionated enough to drive immediate productivity, but (b) extensible enough that thousands of providers and modules emerged around it. Similarly, S3 reduced cloud storage to three verbs: Put, Get, List. The power resided in the abstraction, not just the technology. This provides a blueprint that every software company will need to internalize in the age of agents. Teams need to stop thinking in terms of features and prescriptive workflows and start thinking in terms of abstractions and capabilities. In an agent-first world, the most important question facing product teams is not what features and why, but instead how do I design the right primitive? My favorite example of the primitive-first approach is Pi, a coding tool created by @badlogicgames that I’ve fallen in love with. Pi bills itself as a "minimal agent harness." It is deliberately narrow, leaving it to the developer (or agent) to decide the particulars of their desired workflow. Rather than shipping a complete coding product, it exposes a simple, pluggable coding agent harness…a set of primitives, if you will. Building great developer tools (e.g. primitives) has always been an exercise in this balance. EC2 gave developers a raw virtual machine, powerful and minimally opinionated. Heroku gave them git push, magical but constrained. The best primitives feel deceptively simple because complexity has been compressed thoughtfully. All of this means the winning products and strategies going forward will look very different from those of the SaaS era. The most valuable companies will allow every use case to emerge instead of trying to solve each one themselves. Whereas the old moat was defined by feature breadth, workflow ownership, and data gravity, the emerging moat may be something much simpler: the elegance of the abstraction.
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Amplify Partners reposted
The lesson I learned at Retool (now the hard way): the abstraction is the product in developer tools
For most of software history, value accrued to whoever owned the most workflows. Features were the main currency. But agents have upended this logic. They don't navigate GUIs; they consume APIs and compose capabilities. In other words, every company is now a dev tools company! As @davidcrawshaw put it: "the best product for an agent is just the best product for a developer." Many know about @HashiCorp's "Tao" concept, which encapsulates this idea well. Users don't want features, they want outcomes. Thus, the job of a product is to collapse complexity into the smallest yet most powerful abstraction that enables those outcomes. Exhibit A is Terraform, whose declarative resource graph was (a) opinionated enough to drive immediate productivity, but (b) extensible enough that thousands of providers and modules emerged around it. Similarly, S3 reduced cloud storage to three verbs: Put, Get, List. The power resided in the abstraction, not just the technology. This provides a blueprint that every software company will need to internalize in the age of agents. Teams need to stop thinking in terms of features and prescriptive workflows and start thinking in terms of abstractions and capabilities. In an agent-first world, the most important question facing product teams is not what features and why, but instead how do I design the right primitive? My favorite example of the primitive-first approach is Pi, a coding tool created by @badlogicgames that I’ve fallen in love with. Pi bills itself as a "minimal agent harness." It is deliberately narrow, leaving it to the developer (or agent) to decide the particulars of their desired workflow. Rather than shipping a complete coding product, it exposes a simple, pluggable coding agent harness…a set of primitives, if you will. Building great developer tools (e.g. primitives) has always been an exercise in this balance. EC2 gave developers a raw virtual machine, powerful and minimally opinionated. Heroku gave them git push, magical but constrained. The best primitives feel deceptively simple because complexity has been compressed thoughtfully. All of this means the winning products and strategies going forward will look very different from those of the SaaS era. The most valuable companies will allow every use case to emerge instead of trying to solve each one themselves. Whereas the old moat was defined by feature breadth, workflow ownership, and data gravity, the emerging moat may be something much simpler: the elegance of the abstraction.
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Amplify Partners reposted
For most of software history, value accrued to whoever owned the most workflows. Features were the main currency. But agents have upended this logic. They don't navigate GUIs; they consume APIs and compose capabilities. In other words, every company is now a dev tools company! As @davidcrawshaw put it: "the best product for an agent is just the best product for a developer." Many know about @HashiCorp's "Tao" concept, which encapsulates this idea well. Users don't want features, they want outcomes. Thus, the job of a product is to collapse complexity into the smallest yet most powerful abstraction that enables those outcomes. Exhibit A is Terraform, whose declarative resource graph was (a) opinionated enough to drive immediate productivity, but (b) extensible enough that thousands of providers and modules emerged around it. Similarly, S3 reduced cloud storage to three verbs: Put, Get, List. The power resided in the abstraction, not just the technology. This provides a blueprint that every software company will need to internalize in the age of agents. Teams need to stop thinking in terms of features and prescriptive workflows and start thinking in terms of abstractions and capabilities. In an agent-first world, the most important question facing product teams is not what features and why, but instead how do I design the right primitive? My favorite example of the primitive-first approach is Pi, a coding tool created by @badlogicgames that I’ve fallen in love with. Pi bills itself as a "minimal agent harness." It is deliberately narrow, leaving it to the developer (or agent) to decide the particulars of their desired workflow. Rather than shipping a complete coding product, it exposes a simple, pluggable coding agent harness…a set of primitives, if you will. Building great developer tools (e.g. primitives) has always been an exercise in this balance. EC2 gave developers a raw virtual machine, powerful and minimally opinionated. Heroku gave them git push, magical but constrained. The best primitives feel deceptively simple because complexity has been compressed thoughtfully. All of this means the winning products and strategies going forward will look very different from those of the SaaS era. The most valuable companies will allow every use case to emerge instead of trying to solve each one themselves. Whereas the old moat was defined by feature breadth, workflow ownership, and data gravity, the emerging moat may be something much simpler: the elegance of the abstraction.
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Amplify Partners reposted
“Every company is now a dev tools company,” because your product must be easy for an agent to compose with and build upon. This means simple primitives beat compelling features. Stay simple to enable the most agentic complexity.
For most of software history, value accrued to whoever owned the most workflows. Features were the main currency. But agents have upended this logic. They don't navigate GUIs; they consume APIs and compose capabilities. In other words, every company is now a dev tools company! As @davidcrawshaw put it: "the best product for an agent is just the best product for a developer." Many know about @HashiCorp's "Tao" concept, which encapsulates this idea well. Users don't want features, they want outcomes. Thus, the job of a product is to collapse complexity into the smallest yet most powerful abstraction that enables those outcomes. Exhibit A is Terraform, whose declarative resource graph was (a) opinionated enough to drive immediate productivity, but (b) extensible enough that thousands of providers and modules emerged around it. Similarly, S3 reduced cloud storage to three verbs: Put, Get, List. The power resided in the abstraction, not just the technology. This provides a blueprint that every software company will need to internalize in the age of agents. Teams need to stop thinking in terms of features and prescriptive workflows and start thinking in terms of abstractions and capabilities. In an agent-first world, the most important question facing product teams is not what features and why, but instead how do I design the right primitive? My favorite example of the primitive-first approach is Pi, a coding tool created by @badlogicgames that I’ve fallen in love with. Pi bills itself as a "minimal agent harness." It is deliberately narrow, leaving it to the developer (or agent) to decide the particulars of their desired workflow. Rather than shipping a complete coding product, it exposes a simple, pluggable coding agent harness…a set of primitives, if you will. Building great developer tools (e.g. primitives) has always been an exercise in this balance. EC2 gave developers a raw virtual machine, powerful and minimally opinionated. Heroku gave them git push, magical but constrained. The best primitives feel deceptively simple because complexity has been compressed thoughtfully. All of this means the winning products and strategies going forward will look very different from those of the SaaS era. The most valuable companies will allow every use case to emerge instead of trying to solve each one themselves. Whereas the old moat was defined by feature breadth, workflow ownership, and data gravity, the emerging moat may be something much simpler: the elegance of the abstraction.
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Amplify Partners reposted
You can read a more in depth version in my latest post: amplifypartners.com/blog-pos… Thanks to @davidcrawshaw, @mitchellh, @armon, @schrockn, and @jthandy for pressure-testing these ideas!
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Every company is going to be a developer tools company
For most of software history, value accrued to whoever owned the most workflows. Features were the main currency. But agents have upended this logic. They don't navigate GUIs; they consume APIs and compose capabilities. In other words, every company is now a dev tools company! As @davidcrawshaw put it: "the best product for an agent is just the best product for a developer." Many know about @HashiCorp's "Tao" concept, which encapsulates this idea well. Users don't want features, they want outcomes. Thus, the job of a product is to collapse complexity into the smallest yet most powerful abstraction that enables those outcomes. Exhibit A is Terraform, whose declarative resource graph was (a) opinionated enough to drive immediate productivity, but (b) extensible enough that thousands of providers and modules emerged around it. Similarly, S3 reduced cloud storage to three verbs: Put, Get, List. The power resided in the abstraction, not just the technology. This provides a blueprint that every software company will need to internalize in the age of agents. Teams need to stop thinking in terms of features and prescriptive workflows and start thinking in terms of abstractions and capabilities. In an agent-first world, the most important question facing product teams is not what features and why, but instead how do I design the right primitive? My favorite example of the primitive-first approach is Pi, a coding tool created by @badlogicgames that I’ve fallen in love with. Pi bills itself as a "minimal agent harness." It is deliberately narrow, leaving it to the developer (or agent) to decide the particulars of their desired workflow. Rather than shipping a complete coding product, it exposes a simple, pluggable coding agent harness…a set of primitives, if you will. Building great developer tools (e.g. primitives) has always been an exercise in this balance. EC2 gave developers a raw virtual machine, powerful and minimally opinionated. Heroku gave them git push, magical but constrained. The best primitives feel deceptively simple because complexity has been compressed thoughtfully. All of this means the winning products and strategies going forward will look very different from those of the SaaS era. The most valuable companies will allow every use case to emerge instead of trying to solve each one themselves. Whereas the old moat was defined by feature breadth, workflow ownership, and data gravity, the emerging moat may be something much simpler: the elegance of the abstraction.
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Amplify Partners reposted
Avoca (W23) is building what it calls the AI workforce for the physical economy, starting with home services. In just a few years, the company has grown to eight figures in revenue and recently raised over $125 million at a $1 billion valuation. In this fireside, co-founders @apurvas96 and @thetysonchen sit down with @garrytan to share how they found product-market fit by helping businesses turn missed calls into revenue. They explain why AI is expanding what software can do, pushing past the 1% of wallet that traditional software captures, and why they see it as one of the biggest opportunities for founders. 01:28 - Finding the Right Market 03:25 - Why AI Is Bigger Than SaaS 06:59 - The AI Job Story Nobody Talks About 11:53 - How the Founders Met 16:59 - The Pivot 21:47 - Customer Love Beats Market Size 25:31 - Building an AI Workforce 29:35 - Why Customer Obsession Wins 34:12 - Growing to Eight Figures 37:22 - The Vision Beyond Home Services 38:54 - Building a Generational Company
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Amplify Partners reposted
Today we are introducing Tara. Biological datasets are a source of insights and a means to train biological AI models. As the ability to reason at scale emerges, they take on a new role: the ground truth for testing what reasoning models produce, and the environment in which those models operate, get feedback, and improve. Tara, our autonomous research agent, is embedded in our ever-expanding datasets, lab-generated and synthetic, and built to test and evolve the hypotheses frontier models generate, matching the pace at which they produce new ideas. By keeping those models grounded in a vast space of high-precision biological data, we believe we can compound biological reasoning and close the impedance mismatch between hypothesis generation and validation.
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Amplify Partners reposted
Lots of interesting conversation on the evolution of biotech VC the past few days. Risk tolerance has gone way down. A huge emphasis on mitigating “go to zero” risk across portfolios—which is a very different philosophy than tech VC. One argument for this is that tech and biotech have very different return profiles. But this can be a self-fulfilling prophecy. Returns can suffer from gripping the bat too tightly. At @AmplifyPartners, our deeply held belief is that we are leaning into the thesis that has delivered the best *historical* returns in the industry: betting on technical founders with big visions in the midst of technological paradigm shifts. As biotech VC has become specialized and professionalized, this can be challenging. Some tech VCs turn their brain off when asked to underwrite therapeutic risk. And some biotech VCs turn their brain off when they hear “AI” or see founders outside the usual roster from their network. But we have conviction that partnering with ambitious, risk-on biotechs is a truly generational opportunity right now. There’s so much left to build!
OG Third Rock may have been the platonic ideal of biotech VC Ironically, the opportunity set available to such a strategy is as broad & compelling today as it’s ever been. If only we had a modern reincarnation of the Mark-Kevin-Bob dynamic trio - we’d all be better off for it!
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Amplify Partners reposted
For enterprise AI, models aren’t the problem. The next challenge is a lot more human. I just co-hosted a dinner in NYC with AI and transformation leaders from firms like Amex, Citi, Morgan Stanley, and New York Life. A year ago, we would’ve talked about where AI was stalled out or not working. Now we share wins: SLAs cut from 48hr to 12hr, 93% accuracy across millions of automated docs, AI tools in the hands of every employee, and 900 advisors adopting AI on their own. Here's what else stood out: 1️⃣ Adoption gaps are widening In the same org, you'll find someone who has agentified their entire workflow and someone whose process hasn't changed in a decade. No AI mandate closes that gap. Change management does. 2️⃣ Consistency is the next frontier It’s no longer "can AI do this?” but “how do we make sure AI does it RIGHT every single time?” Most orgs don’t have a single governance plane across all their agents. 3️⃣ Smart orgs are building learning loops Not just deploying AI. But feeding what their people know into their AI, and using AI to surface what their org needs to know. Continuously. That’s how knowledge stops living in people’s heads and starts moving through the business to where it’s needed. The throughline is clear: Companies that move their organizational knowledge the fastest will naturally ADAPT the fastest. The models will just keep getting better. The question now is how can orgs make sure their people and processes keep up. (Big thank you to @dauber and @gwhtim from @AmplifyPartners for co-hosting.)
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95% of cancer drugs fail in clinical trials. Not because we can’t design molecules — we’re great at that — but because a drug that works on an isolated protein almost never works in actual human biology. How @tahoe_ai is solving the translation problem → amplifypartners.com/blog-pos…
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Amplify Partners reposted
Video models are hard. Real-time video models are even harder. Loved this breakdown by @itunpredictable on how we productionized Runway Characters, our real-time interactive avatar model. We had to figure out a lot of things like causal training, lipsyncing, parallelizing the VAE and pinning the KV cache so CUDA graphs don't break.
One of the most cracked, under the radar research teams is at @runwayml. They were on the OG Stable Diffusion paper and just released the first autoregressive video model. Did a technical deep dive on this some interesting inference optimizations: amplifypartners.com/blog-pos…
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Amplify Partners reposted
Great writeup on how we deploy real-time autoregressive diffusion models at scale.
One of the most cracked, under the radar research teams is at @runwayml. They were on the OG Stable Diffusion paper and just released the first autoregressive video model. Did a technical deep dive on this some interesting inference optimizations: amplifypartners.com/blog-pos…
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