Joshua Neckes
Los Angeles, California, United States
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About
Serial entrepreneur with significant experience across SaaS, consumer tech, and services.…
Articles by Joshua
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The Eight Reasons Your Triggered Messaging Doesn’t Work (in 2021)
The Eight Reasons Your Triggered Messaging Doesn’t Work (in 2021)
CRM marketers should, almost without exception, look to make triggered messaging the core of what they do. These…
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Why Facebook Really Acquired Kustomer...and what it means for Salesforce, Zendesk, Shopify, and You.Jan 12, 2021
Why Facebook Really Acquired Kustomer...and what it means for Salesforce, Zendesk, Shopify, and You.
More than any acquisition of 2020, Facebook’s Kustomer purchase left martech experts scratching their heads. Sure…
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Twilio acquired Segment. Why? And was it a good idea?Oct 12, 2020
Twilio acquired Segment. Why? And was it a good idea?
Last Friday, Twilio (TWLO) announced that it had acquired Segment in an all-stock deal worth north of $3.2B.
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Should your ESP be a "dumb pipe"?Sep 20, 2016
Should your ESP be a "dumb pipe"?
All-powerful email service providers have become ornamental centerpieces for the major marketing clouds. Adobe…
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Why doesn't my marketing cloud work?Sep 2, 2016
Why doesn't my marketing cloud work?
Marketing clouds have significant inherent complexity. Operating them, deploying them, and maintaining them requires…
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The Perks of Being (and Having) An Annoying CustomerFeb 19, 2016
The Perks of Being (and Having) An Annoying Customer
SaaS businesses often lament their “annoying customers” - as a customer, you should always endeavor to fall into this…
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How to buy marketing technology and not hate yourselfFeb 8, 2016
How to buy marketing technology and not hate yourself
Marketing solutions have come a long way since I signed my first agreement with some long-forgotten CRM ten years ago…
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The Basics of LTV ExtensionDec 14, 2015
The Basics of LTV Extension
Extending lifetime value is the name of the game when it comes to retention marketing. But as the concept becomes…
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Siloing Subverts Retention Teams - Here's How to Stop ItNov 12, 2015
Siloing Subverts Retention Teams - Here's How to Stop It
“Siloing” is preventing retention marketing teams from scaling effectively. Here’s why - and what you can do about it.
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The Rise of the Retention MarketerNov 6, 2015
The Rise of the Retention Marketer
The modern retention marketer is a fundamentally new breed - part quant, part creative, part ESP jockey, and wholly…
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Activity
10K followers
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Joshua Neckes shared thisAn essential article. The possibility of extreme wealth is often cited as a necessary precondition for a fair society. There are plenty of good arguments against it. But here's a familiar one in a new costume: flush with infinite wealth, the individual becomes incapable of operating as a moral creature. An old idea in a hyper-capitalist guise -- absolute power corrupts, and absolute power corrupts absolutely. Loved this one. https://lnkd.in/ecQyt_xhWhat I Learned About Billionaires at Jeff Bezos’s Private RetreatWhat I Learned About Billionaires at Jeff Bezos’s Private Retreat
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Joshua Neckes posted thisI can't be the only one who is just SO TIRED of the fear-based marketing of frontier labs (and associated folks). At this point, the collapse of civilization has been promised a dozen times over - reaching as far back as GPT 2. We are now well and truly in the season of panic and terror. It isn't enough for a product to be disruptive; it now has to be deadly to our very way of life. Measured, nuanced assessments are not en vogue. They do not sell Claude Max subscriptions. They don't get sovereign wealth funds to dump billions into the latest capital raise. Call it "Mythos" - a fitting name for an ominous engine of societal destruction - or GTFO. Whatever happened to "Capybara", the alternative name for Anthropic's next model? Too friendly and cute to be a vessel for societal upheaval, apparently.
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Joshua Neckes shared thisEvaluating more than 50 third-party datasets a year? Tired of the legal, compliance, and data engineering overhead just to get an initial look at a dataset — before you even know if it's valuable for your team? Something really interesting is emerging in how firms - particularly those in finance, on the buyside - are using Bobsled. They're creating an independent data evaluation layer — a buffer zone between their production data estate and the entire third-party data landscape. No ingestion into the core stack. No compliance gauntlet. No engineering tickets. Load the data into the buffer zone and start working. Query it in natural language. Understand coverage and relevancy. Bring your own notebooks and skills. Get to “Go” or “No Go” before anyone in legal even knows there was a dataset to look at. Ultimately, we're seeing this enable firms to eval 10x more data with significantly less overhead. A transparently massive win, by any standard. Question, tho: is suggestive of an emergent reference architecture for data eval? Here's an overly-futuristic, obviously-vibed visualization of the emerging architecture we're seeing:
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Joshua Neckes posted thisLLMs are great at coding - this makes sense, they're providing objective instruction to a machine. LLMs are great at customer support, when properly buttressed with well-semanticized structured data. This makes sense, as they're providing objective answers. But anywhere where there's not an objective "truth"? Where the goal of the communication is not a "fact", but is instead something deeper? There's a hollowness that, in my estimation, provokes the uncanny valley response so many of us experience. I've been increasingly aware of this divergence between "fact-based" LLM outputs and creative outputs -- and importantly, I would put "strategy" docs into this category. To that end, I've become increasingly hostile to engaging with any sort of AI-generated strategy document. Almost uniformly, these documents appear, on the surface, to be well-considered. Once deeper analysis is applied, though, they begin to collapse. Insidiously, the people who generate them view them as an effective substitute for deep strategy work, and can become weirdly defensive over their contents, despite not having generated any of it themselves. More importantly, though, the "authors" haven't gone through the actual cognitive process of generating the work, which helps codify thinking, check assumptions, and drive precision. As a result, downstream discussions tend to be meaningfully less productive. All of this is to say, I have a ton of optimism for the impact of LLMs on wrote, non-creative, fact-based work. And, increasingly, pessimism for this generation's impact on creative work.
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Joshua Neckes shared thisNow trending: tech bros applauding a leading VC's interview in which he advances the idea that "Great men in history had zero introspection." My god, guys. Have a look around -- and then inward. Perhaps the absolute desolate state of our collective moral/ethical compass might have SOMETHING to do with those holding the levers of power viewing introspection as weakness. What a time. https://lnkd.in/eJeivQBB
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Joshua Neckes posted thisVisiting with my dad in Boulder this weekend, and he was like, “Yo, I’m just an aging garmento, but AI sure seems like it’s gonna take everyone’s job”. He’s 77 and still working as a sales guy, slinging handbags wholesale like an absolute champion. Just an unmatched passion for the game. As we talked through the issue, I was really struck by his profound sense of concern for the younger generation. By his explicit desire for government intervention, his worry about tech barons hoovering up all the productivity gains. This is a man who has largely defined himself through the value of hard work. He’s a classic centrist, economically center right. We’ve had EXTREMELY FREQUENT conflict about my strongly-leftist orientation. And yet, faced with a potential impending AI jobpocalypse, he’s out here talking about the importance of UBI and effective government regulation. It’s also not the first time I’ve felt someone more typically centrist/center-right slide left in the face of this particular moment. The overwhelming futureshock seems to have unlocked some essential humanism that transcends traditional politics. It feels weird, but these conversations have left me oddly hopeful about the shared, species-level survival instinct that lurks underneath our political differences. Perhaps this kind of seismic moment will jerk us back towards a more human capitalism. You know, or not
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Joshua Neckes posted thisMy sense is that a lot of the anxiety around AI-driven job loss is not really about AI, but more another symptom of our late capitalist society's fundamental unfairness w/r/t resource distribution, safety nets, etc. We explicitly choose policies that put the rich above the poor. Rationalize tax codes that disproportionately benefit the wealthy, home owners, etc. Engage in performative liberalism while quietly perpetuating a system that's foundationally unequal. In building a society with no safety net, we create anxiety not just in those with less, but in all of us. We all, collectively, feel just a hair's breadth away from living on the street, even if we're in a great job. AI is not the issue. In a world where resource distribution and opportunity were more genuinely and ethically distributed, my sense is that optimism would be significantly more abundant (future shock notwithstanding). After all, the entire history of human civilization has been one of labor abstraction. Back in the day, everyone had to hunt and gather. Then we developed farming. Cool - surpluses meant that now people had free time. That free time created an opportunity for new/different work. Ultimately, much of that work was manual labor. Then we had the industrial revolution. Cool - now we had new surpluses that rendered a lot of basic labor unnecessary. Society adapted. People focused on what only people could do; machines handled the rest. Whether or not people incurred pain with these shifts was rather orthogonal to the tech in question - it was more about how well society managed the transition, and how much it cared for its citizens during that time. Perhaps AI is the necessary "prompt" we need to reevaluate some of our deeper, more unspoken commitments to an inherently selfish societal structure.
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Joshua Neckes shared thisLove Notion. And I love this take. https://lnkd.in/ejsT6ykK
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Joshua Neckes posted thisI'm sorry, but this idea that people are just gonna vibe data connectors (or like, *software*) in the enterprise is silly. The cost isn't high enough to justify the risk. Lower cost alternatives will emerge from SaaS vendors. Margins of software businesses may stay consistent, but revenue basis will just be smaller. Software companies will, resultantly, be smaller. They'll ship more with a smaller team. Be able to manage more with a smaller footprint. For certain web-scale companies, startups, and other outliers, maybe it makes sense to build your own infra. But for the overwhelming silent majority? No. People aren't just buying code. They're outsourcing risk, accountability, observability.
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Joshua Neckes liked thisJoshua Neckes liked this🔬The teams that win share one trait: they're genuinely curious about being wrong. They run tests not to validate their instincts, but to find out where their instincts fail them. In Stop Guessing. Start Testing., Madeline Eskind Litvack breaks down how to build a formidable experimentation culture, featuring POVC-backed founder Ethan Arpi. Inside: → The hierarchy of where to test first, from CTAs to pricing pages to navigation. → How AI agents are reshaping ideation, planning, and results interpretation. → The bad habits quietly killing your perceived learnings. → Ethan's playbook on data architecture, a workaround for early-stage teams that lack the organic volume for stat-sig results on deeper-funnel actions, how conversational products unlock user signal, and more. https://lnkd.in/gdZkXK8J
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Joshua Neckes liked thisJoshua Neckes liked this"Why not just build agentic analytics in the data platform you already own?" Answer: We've built a context layer that learns with you. Our learning agents watch real usage, flag where the model is falling short, and surface learnings your data team can apply in two clicks — no semantic view edits, no redeploys. Your agents stay in the loop. Your data stays in your platform. And those urgent Slacks from execs "asking for some quick help" stop showing up. Bobsled Solution Engineer Matt Ballantine walks through it in this short demo.
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Joshua Neckes liked thisJoshua Neckes liked thisFor modern software and AI companies, the in-app dashboard is just one piece of the analytics experience customers expect. Benjamin T. at Aampe saw it early. His customers want their data in their own warehouses, feeding their own tools and models. Building the pipes to get it there would have cost his team a quarter of engineering. He had better things for them to do. "That's engineering time I'd rather spend shipping the features only we can build," Ben told us in a new interview. Ben breaks down how he scoped the build, the edge cases that kept surfacing, and what customers are doing once the data actually shows up where they work. Link to interview in comments 👇
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Joshua Neckes liked thisJoshua Neckes liked thisWe analyzed 770 data and analytics companies to see how they're deploying AI-powered experiences. 31% have shipped an AI feature. That's past the tipping point on the adoption curve, and the next 6-9 months are when the market really tilts. A few things stood out from the research 👇 1. Two paths are emerging. 23% have built conversational analytics into their product. 16% have launched MCP servers or other AI partnerships. Only 8% have done both. 2. The gap between big and small companies is 10x. 85% of firms with 5K+ employees have shipped AI. Under 50 employees, it drops to 9%. Some of that is resources, but a lot of it is existential pressure. Big companies feel it. Small ones don't — yet. 3. Text-heavy verticals are out front. Healthcare, legal, and commerce lead the pack. No vertical has cracked 50% yet, so everyone still has a window. 4. "Chat with your data" is quickly becoming the killer app. Embedded analytics and AI search are showing up in nearly every roadmap we reviewed. 5. The real endgame is agents. The difference between a commodity data feed and an irreplaceable product is going to come down to how much intelligence gets baked into the data layer itself. If you're in the 69% that hasn't shipped, the next 6-9 months are decision time. Full research in comments. If you're at a data company, where are you on this? Building your own AI experience, plugging into someone else's, or still figuring it out? #dataproviders #daas #ai
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Joshua Neckes liked thisJoshua Neckes liked thisThis is how AI is changing the game. In a 24-hour run on a single A100, Disarray's MLE agent earned 28 medals across Kaggle competitions, with 9 top-10 finishes and even a #1 ranking ahead of all human teams. Congrats to Doris Xin, Moustafa AbdelBaky the entire Disarray team on pushing the frontier here. But the most interesting part is not the scoreboard. It is what it implies. Kaggle is a strong proxy for real-world Machine Learning work. Top data scientists typically spend weeks iterating on models, features, and data. What Disarray showed is that an autonomous system can now operate across domains and reach that level in a single day. Most Machine Learning Engineering agents today focus on improving model search. Disarray extends that to data. In one example, the agent independently found and incorporated external data to improve performance, something experienced ML engineers do instinctively, but most systems do not attempt. That shift matters. In practice, gains often come from better data, not just better models. At the same time, this does not replace ML engineers. Production systems still require human judgment around business alignment, infrastructure, governance, and accountability. The role of the ML engineer is evolving, not disappearing. Definitely worth a read from the team here: https://lnkd.in/e5R9XPRV Excited to be partnering with Doris, Moustafa and team as they continue to push the AI frontier!When an MLE Agent Beats Humans, What Does That Actually Mean? - DisarrayWhen an MLE Agent Beats Humans, What Does That Actually Mean? - Disarray
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Joshua Neckes liked thisJoshua Neckes liked thisLast week in DC with fellow board members of National Venture Capital Association, I spent time with policymakers talking about what venture capital actually looks like on the ground. At a high level, it is straightforward. This model takes ideas and research and turns them into products, technologies, and services that create jobs and drive American competitiveness. What tends to resonate are the specific stories. I talked about how .406 Ventures has leaned further into going earlier, sometimes even partnering at formation to help build companies from scratch. Our two last investments on the AI side very much fit this strategy. In one case, we partnered with a serial entrepreneur around a shared thesis to help found the company and build the team. In another, we worked closely with a professor founder to recruit a GTM CEO and help bring in early customers. Those stories make the role of venture capital real and underscore why the right policy environment matters to drive American innovation. Aziz Gilani, Shauntel Garvey, Nnamdi Okike
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Joshua Neckes liked thisLet us know if you're at #Shoptalk this week - we'd love to connect!Joshua Neckes liked thisShoptalk kicks off this week and one question keeps coming up in every pre-conference conversation we're having: Who actually controls the data - your marketers or your engineering backlog? 🤔 The biggest theme we're tracking: AI agents are everywhere, but most marketing teams still can't explore their own customer data without filing a ticket. You can generate 1,000 emails in seconds, but you still wait 3 days to get a segment pulled. That's the bottleneck we're obsessed with solving at Simon AI. We give marketers direct, conversational access to unified customer data so you can build, explore, and activate without waiting on anyone. Katharine Toll and Jordan Patrick will be on the ground at #Shoptalk2026 meeting with CRM and lifecycle leaders who are rethinking how their teams work with data. If you're heading to Las Vegas, come find us! We'd love to hear how your team is tackling the AI-meets-data-access challenge. And if you're following from home, we'll be sharing the sharpest takes all week. Stay tuned. 👀 #SimonAI #AICDP #RetailMarketing #MarketingData
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Love this - AI is the Oppenheimer moment for marketing. It fundamentally collapses the time and cost of production. A $2.5 million campaign that used to take four months can now be delivered for $500,000 in four weeks. Legacy firms that charge based on hours with the 'time-driven' model are existentially threatened by this.
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Varun Anand
Clay • 51K followers
Today we're changing Clay's pricing. Pricing changes are scary (for you and me!), but we are trying to do this in the most authentically Clay way possible - thoughtful, transparent and community-first. We've been thinking about this for almost a year (maybe too long Karan!), and have talked to hundreds of customers and partners. We want to align our business model with how our product is being used today, and set us up to grow sustainably together. When we launched, our customers used us for data enrichment. And now our community is using Clay to power their GTM engineering motions. But the pricing model hasn't evolved, so we're making some changes to reflect that. Here’s what’s important to note: First: nobody is getting forced off their plan. Every existing customer can stay on their existing plan. We have to earn the right for you to move to a modern plan. Second: most customers will see better value for their money with this change. If we think you'll save money by switching to the new pricing, we're going to reach out and tell you proactively. Third: we're separating the cost of data from the cost of the platform. We want to keep bringing down the cost of data in Clay, and make the value tied to what you orchestrate and build with it. Fourth: our best features are now more accessible. The new Growth plan includes things that used to live exclusively on Pro at a 38% lower price. Many of our most advanced users will save thousands a year. Our plans now come with a new metric called Actions, which measures the orchestration work Clay does for you: the workflows, AI research, and logic that turns raw data into a GTM motion. 90% of customers will never hit their Actions limit. We want most people to never think about it. That way, we’re only winning if you are. Data Credits now cover just the data - and they're cheaper. We’re reducing the cost of the data in our marketplace by 50–90%, making prices comparable to what you’d pay externally. Karan and I recorded a video walking through the full reasoning and who gets affected - positively and negatively (including us – we’re taking a 10% revenue hit). If you're a Clay customer and have questions, we want to hear them! Full pricing announcement and the internal memo we sent our team below. Announcement: clay.link/nF1eKMH Internal memo: clay.link/wiw2NHK
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Eric Franchi
8K followers
Excited to share that Aperiam has invested in Newton Research’s oversubscribed $9M Series A, alongside Greycroft, Bessemer Venture Partners, S4S Ventures, and LiveRamp Ventures. Newton is redefining what agentic AI can do for marketing analytics, enabling brands and agencies to: - Run customer 360 journey analysis across touchpoints - Segment audiences and build predictive models - Automate attribution, incrementality, and media mix modeling - Generate dynamic, natural language-driven reporting - Save up to 126 hours/month and achieve 10x analytics output Their pre-built “blueprints” let non-technical teams securely run advanced analytics on their own data with just a few clicks. John Hoctor, Matthew Emans, and Steven Bennett have successfully built and exited multiple companies. We love to back repeat founders on their most ambitious ideas, like Newton Research. Link to more in comments.
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Travis Kirk Lowry
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founders that have only ever worked within a large company tend to be empty vessels. the most valuable skill at a big company is identifying who has (or could soon have) power and then aligning with and advocating for their ideas as if they were your own. take a spin through the recent YC class and you see a pattern of big company mids all founding the same AI for xyz... its not really their idea, it just sounds good. the board meetings will be especially painful. founders acting like they work for the investors, seeking approval, lost in the woods. there are exceptions. but they tend to be founded before zirp. when founding a company was hard and low prestige and there wasn't capital at every turn. an empty vessel doesn't jump into the abyss without first raising a $5m round from the 9 figure 'seed firms'.
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