Daniela Amodei

Here's how I think about recruiting at Clay in three analogies: 1. Recruiting is like a solar system 🪐 ↳ Like orbiting planets, Clay and candidates are both in constant motion. Recruiting is about timing and fit: sometimes alignment happens right away, and other times it takes years of circling back. For certain candidates, it may take multiple touch points over time before Clay and a candidate’s orbit come close enough to align. ↳ At the beginning, you start with a blank canvas, you try to fill in the colors as best as you can with interviews, references, etc. Then compare that picture with the spec for your role. ↳ I'm not making value judgments if someone is "good" or "bad". I'm just trying to understand their story, peek into their soul — figure out who they are! — to determine if Clay's role is a good fit for them at this moment. For example, someone may not be a fit if they need more support than Clay can give them at this stage of growth. 2: Recruit like you're fishing 🎣 ↳ Know when to chase with energy & urgency. Know when to ease up and let them come to you. ↳ It's a dance where you always need to sell and evaluate the fit at the same time throughout. The best people make that tension feel natural! ↳ People want to feel wanted. Bring high energy and enthusiasm even if you're hiring for a great role. 3: Interview like this emoji → 👯‍♀️ ↳ Interviewing shouldn't be combative, even though sometimes it feels that way. Ideally you and the company are working together to get the right information to determine if there is a fit. ↳ You get information at every part of the process. For example, in an offer negotiation, you learn more about the candidate by how they act and engage if there's conflict. Candidates also learn how companies act too (e.g. Clay does not make exploding offers). I hope this is helpful! Curious what people think and if there are any questions, feedback or ideas!

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I had dinner the other night with a fascinating group of folks building AI-enabled products. PMs from companies like Perplexity and Harvey, plus engineers and founders. One question kept coming up: Why haven't incumbent software providers been able to leverage their valuable data assets yet? Both giants like Salesforce and growth-stage companies with 10+ years of collected data seem to be struggling to deliver truly differentiated AI experiences. Most of us would assume these companies are sitting on data gold mines that should give them a massive advantage. But is that actually true? One perspective I found compelling: The historical data these companies have collected simply isn't the right kind to make AI models truly effective. The workflow data, outcomes data, or database info they've aggregated over the years might not be as useful as we'd think. What's most valuable in building agentic AI isn't just workflow data or outcomes data—it's a granular understanding of how humans actually perform tasks. This includes all the context switching between applications and even offline activities that traditional software workflows don't capture. The real value is understanding the detailed ontology of how tasks get done so you can replicate that in an agent. AI-native companies are building these task-level datasets from scratch and at the same time incumbents are trying to figure this out—potentially leveling the playing field. The counter perspective: Incumbents aren't failing; they're just slower. Over the next 12-18 months, we'll see them release AI that delivers differentiated insights thanks to this accumulated data. Their data is valuable; they're just taking longer to leverage it effectively. I don't know which view is right, but both offer interesting frames for thinking about the AI landscape and who might win in the long run. Curious what you all think—will incumbents' data moats prove valuable, or are AI-native companies starting on more equal footing than we might assume?

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"I'd never want to work in a huge public company" I've heard that a number of times and always provide a counter point, particularly for early career folks. You can learn a TON You'll get trained up really well You'll have experienced leadership Material changes in the comp plan are less frequent Recruiters will see a 'brand' on your resume Benefits and work set up are solid Sales support is well baked So if you're considering it, here's how to evaluate a public co. (and crush the interview): -- Read their quarterly financial releases, see what their current focus areas are. -- Research things like % of team hitting quota, culture, product market fit -- Look at data on RepVue, G2, Glassdoor (yup even reviews can be helpful!) -- Build a list of their competitors, and stack rank who you think is winning, and why -- Ask about onboarding, and then talk about why a well built out training curriculum is something you're excited about -- Research the sales leader(s) and understand where they came from and look at tenure and prior success -- Try to get a read on the territory or prospect segment you'll be targeting (it can make a huge difference) Big / public companies aren't for everyone, but in turbulent times they can offer a sense of stability. Below is a list of public companies which combines their culture/leadership score with their future outlook sentiment to get you started on your list: https://lnkd.in/dU8-dUc2 ✌️

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Oye, mira. 🚨 There are 375 Founding Member Spots Left for Justice A.I. GPT 🚨 I have to ask: why are we still funding corporations designed to harm us? Disney. Netflix. Starbucks. Every one of them profits off our pain, while politics, policy, and media keep extracting from the communities our governments never stopped exploiting. After these 375 spots are gone, the door closes. Founding members stay locked at $20/month. Everyone else pays more as Justice AI scales, and every dollar that flows back into the very communities white supremacy has tried to silence increases. Let’s stop pretending: Bias isn’t a glitch. "Bias isn’t an inconvenience." Social bias is bigotry. Bigotry is violence. Bigotry is not an opinion. It's a weapon. And when they coded that into AI, it spread at machine speed. That’s why Justice AI GPT exists. It's why I built it. It’s the first custom AI Framework built on ChatGPT’s architecture that solved the bias problem, running on its own Decolonial dataset and DIA Framework™. It pulls the training data form OpenAI's dataset to gather all the necessary information to answer your inquiries, and then immediately uses the Decolonial Data Set to identify and dismantle the bias in real time. Justice AI also dismantles Implicit Conditioning, the neurological and systemic programming that protects dominance, and lifts up the knowledge systems of people of the global majority, disabled, queer, Indigenous, and historically excluded communities. It's programmed with Decolonial social science, empathy frameworks, and ancestral knowledge from indigenous communities around the world. The build for this tool was incredibly intentional. If dismantling white supremacy isn’t for you, then you’re not our people. That’s fine. This is for those committed to collective liberation, innovators building with integrity, and communities ready to create futures the system of white supremacy told us were impossible. And we’re already in motion: 🌍 125 founding members strong around the world: educators rewriting racist curricula, nonprofits exposing algorithmic discrimination, SMEs backing their DEIBA commitments with real tools, and everyday people refusing to be erased. If you can boycott billion-dollar corporations, you can commit $20/month to liberation tech. This isn’t just about resistance, it’s about building tools to launch businesses, expand entrepreneurial offerings, and rewrite the rules of the digital economy. And, if you want help using it, I will personally meet with you to help guide you. ⏳ 375 spots left. After that, history will remember who stood up to launch the world’s first Decolonial AI at scale. Join now: https://justiceaigpt.ca Cancel anytime. Liberation isn’t optional. It's inevitable. You in? #JusticeAI #DecolonizeTech #EthicalAI #Intersectionality #SocialImpact #TechForJustice #AIForGood

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Traditional startup mentorship is dead and I have the data to back it up. In the 2010s (and even early 2020s), for Founders in our Techstars program the value was in mentor madness. A crazy 2 week period where they met ~100 folks that could help them take their business to the next level. It was stressful and dramatic. "Why did I get this meeting, why not that meeting?" and a lot worse than that. In 2025, what I'm seeing at Foundations is the opposite. While many of our mentor office hours fully book, they're not nearly as busy as our Slack or community presentations. The valuable mentorship today is peer mentorship, rapid knowledge sharing from those learning in real-time how to build and win in the new a AI-first world. There are no "experts" and those with vast experience from the "before times" aren't nearly as helpful as the startup sitting next to you. Wild, wild, wild times ahead folks. And I'll be frank, if anyone who hasn't been in the weeds building or shipping a product in the past 6 months is giving you advice on how to do it, don't listen. Maybe you should be the one advising them :)

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I killed a fundraising round by being too transparent. Not by hiding problems. By showing our metrics. ----- Here's what happened: We had industry-leading NRR. Great retention. Low CAC. I thought this would make fundraising easy. I was wrong. ----- In pitch after pitch, the same pattern: Investor: "What's your retention?" Me: *shares detailed cohort analysis* Investor: "Hmm, but Series B companies have..." Wait. What? We're raising seed. Why are you comparing us to Series B? ----- Then I watched a competitor raise $5M. No metrics. No retention data. No unit economics. Just "we're growing 50% month over month." That's when I understood the paradox. ----- Early-stage investors work with incomplete information. They don't know retention, NRR, or CAC for most startups they invest in. So they use heuristics: - Growth rate - Customer count - Founder energy They *hope* these turn into good metrics later. ----- But when you show real metrics early, you change the game. You go from "vision investment" to "numbers investment." Now they're not betting on potential. They're evaluating performance. Even good metrics get picked apart: - "What if this month was lucky?" - "How sustainable is this?" - "What happens at scale?" You've killed the suspense. ----- At BuildBetter, we face a double bind: Investors compare us to: 1. Pure potential startups (no metrics, just dreams) 2. Later-stage companies (established metrics at scale) We're being judged by Series B standards while raising at Seed. It's like comparing a rookie's stats to an all-star's. ----- The uncomfortable truth: Early-stage investing isn't about proof. It's about belief. Certainty is less exciting than possibility. Good metrics can feel limiting compared to infinite potential. ----- I'm not saying don't track metrics. I'm saying understand the game you're playing. Sometimes the best pitch isn't what you've proven. It's what you haven't had the chance to prove yet. Because in early-stage investing, suspense sells better than certainty. ----- p.s. Have you seen great metrics hurt a fundraise? Or am I just making excuses for companies without strong fundamentals?

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PSA to seed and early-stage founders: stop worrying about competition. I’ve spoken to hundreds of late-stage founders, and they all say the same thing. The biggest mistake they see at the early stage is founders obsessing over who else is in their space. When you are pre-PMF, comparing yourself to other buzzy AI startups is a distraction. They already have a defined GTM motion, some level of product-market fit, and a playbook to execute. Their “big new feature” might just be about expanding revenue or building parity. You’re still trying to land your first 10 paying customers. Here’s the truth: 1. Worry about competitors only when you actively lose a deal to them. THESE ARE THE COMPETITORS TO WORRY ABOUT. 2. When a customer chooses someone else, they will usually tell you why. Nine times out of ten it’s not the reason you think. It’s rarely a missing feature. More often it’s pricing, brand trust, or timing. 3. Competitors often move in completely different directions. OpenAI went after consumers, while Anthropic leaned into enterprise and APIs. Playbooks diverge fast. 4. The only real roadmap at the early stage is customer pain. Every conversation, every demo, every user insight. Talk to as many customers as possible. If you spend your time trying to copy what competitors are doing, you will always be a step behind. If you spend your time talking to users, you’ll know exactly what to build next. The path to PMF isn’t found on Twitter threads about “AI competitors.” It’s found in the feedback from the 5 people actually using your product every single day.

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Reduction in Force (Layoffs) and the 2025 Job Search: What to Remember RIF Reminders: Print everything out: Save performance reviews, commendations, your job description, and anything else that isn’t proprietary. Once you're locked out of internal systems, it’s gone. Save financial records: Print or download your most recent 401(k), insurance info, stock options, and W2s. These are often on internal platforms. Contact providers directly to explore rollover or contribution options. Take care of yourself: Whether you were there 5 years or 20, it’s okay to feel everything. Loss, anger, sadness, even relief. Let yourself process. RIF does not define your worth. You did nothing wrong: This wasn’t about your performance. Layoffs happen for business reasons, not personal ones. Give yourself grace—you are still a valuable employee and professional. Job Searching Tips for 2025: Update your materials: Refresh your resume and cover letter to reflect today’s market. Use action verbs, modern formatting, and current keywords. Start local: Make a list of companies in your area (or remote-friendly ones) you’d love to work for. Then dig into LinkedIn to see who you know. Use your network—you’ll be surprised who’s willing to help. Track your progress: Create a spreadsheet to avoid duplicate applications. Include: - Company name - Job title - Req number (if listed) - Date applied - Contact person (if available) - Follow-up notes Take a break: Yes, the job search is your new job—but even 9–5s have PTO. Go to your kid’s school play, grab lunch with a friend, take a mental health day. It helps you stay grounded. Watch for scams: Fake or outdated job listings are everywhere. If it feels fishy (or if a "prince" wants to fund your job search), trust your gut. No legit company will ask for your bank info or personal details upfront. Use job boards wisely: Focus on curated sites like: * LinkedIn Jobs * Otta – great for tech/startups * BuiltIn * Wellfound (AngelList) But always try to apply directly on the company site when possible. Some sites don't remove old posts or closed positions regularly. You don't want to waste time applying to ghost jobs. Mindset for the Long Game: Rejection = redirection: Don’t take it personally. Keep applying, refining, and learning. Celebrate small wins: A callback, a resume update, a new connection—it all adds up. Stay connected: Message friends, join professional groups, attend (even virtual) events. You never know where a conversation might lead. You are not alone: The market is tough right now, but people ARE getting hired. Your opportunity is coming too. Be ready. 💚

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Startups building for national security can’t afford to waste time fighting through bureaucracy just to test, iterate, and deploy. That’s why I’m fired up to share a new resource for Black Flag teams: thanks to our partnership with Second Front, participating companies now get free access to the 2F Workshop dev environment — the fastest on-ramp to fielding software on DoD networks through Game Warden. This isn’t a sandbox. It’s a launchpad. If you're building in defense, intelligence, or dual-use tech — apply to Black Flag and plug straight into the real-world infrastructure that matters. 🏴‍☠️

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30K in the past 7 days. 8 new clients. 8K closed yesterday alone. The velocity is insane right now. But here's what's actually driving it: lessons from a client and our YC group partner. Cole R. from Conduit AI (YC W24) taught me to stop underpricing. "If you charge very little, they think you're a shitty product. Charge more, drive results, and they'll believe in you." He also taught me his qualification framework: "On a scale of 1 to 10, how willing are you to move forward?" If they say 7 or below, dig for the blocker. Budget? Timing? Internal politics? That question saves hours of chasing ghosts. Pete Koomen, our YC group partner, taught me enterprise sales is mostly about listening and understanding their approval process. "Listen for a long period of time. Figure out how they buy, not just what they need." He told us to put some engineering effort in sales, and run someone's persona before we even meet them. Show up with their specific use case already built. These lessons are why we're closing faster now. Your best customers teach you how to win. Your best advisors teach you how to scale.

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In a small VC team, there’s no such thing as a defined role. It’s a lot like working in an early-stage startup—you’re navigating moving parts, wearing multiple hats, and figuring things out as you go. One moment, you’re evaluating companies. Next, you’re deep in compliance work. And somewhere in between, you’re helping founders, managing LP relationships, and figuring out fundraising. There’s no playbook. You learn on the go—because you have to. One day it’s legal docs. Another day it’s structuring internal processes or jumping on calls about something you’ve never done before. What makes it work is adaptability. You build muscle memory for switching gears, asking better questions, and knowing when to go deep and when to ship fast. The biggest challenge? Context-switching. You’re constantly jumping between different problem statements, each requiring a different skill set. But over time, you start recognising patterns, and things that once felt intense become second nature. That’s the reality of working in a small team: You grow fast, but only if you’re willing to stretch beyond what you already know. Atrium Angels, Atrium Ventures (fka Alluvium Capital), Consumer Collective by Atrium, Umang Kumar, Varun Laul, Siddharth Puri, Nikita Dhingra, Abhishek Nag, Karthik Bhat, Sushma Kaushik, Varun M., Rohit Kapoor, Pranav Obhrai, Naveen Jain, Ambika Pande, Arun Rao

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unpopular opinion: large referral bonuses are mega red flag, and they're ineffective to boot: I've seen this play out in practice. When I first joined Scopely (mind you, >10 years ago, so historical), the company leveraged all manner of referral schemes and gimmicks (ex: $11k in "bacon wrapped cash"). They really struggled with organic referrals - mostly because it sucked working there. I remember a particularly difficult period where the company lost >40 employees (of 120) in the course of a single quarter. All of them quit. And guess what? The referral gimmick didn't even work. People don't refer their friends into awful environments, even if there's a good chunk of change on the line. They just refer people they don't care about. Then I went to Riot, which didn't have a particularly generous referral bonus. But the vast majority of people I knew at Riot loved working at Riot. Guess what: they referred with vigor. No dollars needed. They just wanted to gift their friends with the awesome experience of working at Riot. That was it. I'm writing this to say something to two audiences... Recruiters and hiring teams: if you find yourself discussing a massive referral bonus, then take a long look in the mirror. Not only are you proposing an ineffective solution, but you also likely have pernicious cultural problems. Candidates: if you learn that a company leverages large hiring bonuses, think twice before joining - they're telling you "it REALLY sucks to work here."

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Revelio Labs launched a new labor market metric today -- a Workforce Confidence Pulse. In a newsletter announcing it, they write that the only alternative measure of employee confidence is Glassdoor's Employee Confidence Index and pointed to some of its limitations. It's worth looking closely at the inputs to the Revelio Labs metric -- measures like CEO and senior leadership approval, employee beliefs about internal advancement opportunities, and whether the employee would recommend the company to a friend. Anyone steeped in labor market and people analytics data will recognize these inputs as the core company ratings categories that Glassdoor has collected over the course of nearly two decades. It is abundantly clear to me that Revelio's new confidence metric is -- perhaps entirely -- derived from data collected by Glassdoor, with no acknowledgement of the source. While it is perfectly legal to scrape such public data, this kind of data rebranding without source attribution raises thorny questions about public goods in the data economy. (I'm obviously not a lawyer, but this is my understanding having worked in the data sharing space for the past decade and a half.) Perhaps imitation is the greatest form of flattery, and public exchange about improving technical methods is core to scientific advancement. Open data with attribution benefits society at large by scaling opportunities for insight. But when there is every incentive to repackage data collected by others with no obligation to attribute its source, we devalue the essential work of data creation. Many become data re-branders, and few become data creators -- a world of DJs with vanishingly few musicians. (In full disclosure, during my time at Glassdoor I worked with my team to develop the original version of Glassdoor's workforce confidence metric.) https://lnkd.in/ghn-7BeN

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I was on a call with a scaling California-based tech company client and they said to me: “What makes your work so valuable is that you’re not in the company.” Which sounds odd at first. But we’re not stuck in features, or company updates. We’re able to think and create from an audience-first & LinkedIn-first perspective. Which means we create content that actually resonates. We think deeply about somebody who hasn’t seen your name before and doesn’t have a clue what you do. And how we communicate your proposition & insights in the most effective way possible. So that someone landing on your post cold can determine if what you offer is a fit. And then thinking about how to communicate to someone who doesn’t even know they have a problem (that you solve), yet. It's not generic posts. It’s proven processes that are audience-first, LinkedIn-first. Pieces of content that reach, engage and compel your ideal decision maker to take action. And messaging that clicks with your ideal buyer. Having that outside perspective is valuable - you don’t know how wrapped up you are in what you do. 

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