Jimmy E. Chan

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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Many AI-Native SaaS companies are taking too much of an opinionated approach to workflows for enterprises It's making it more challenging to get companies to buy and successfully implement. Instead of trying to build a "one size fits all" solution, here’s three approaches I’ve seen work incredibly well when going after the enterprise: 1. Start with a low-code/no-code custom workflow builder and layer on AI Features A company like Fuse Finance is making it super easy for lenders, credit unions and community banks to adopt as they have a customizable UI with agentic tools being built on top that deliver clear ROI in many ways. Unlike a customers current software, which require tons of engineering time to make any changes (which most banks don't have the resources for), Fuse now makes it 10X easier to make any changes to the process while still harnessing the power of AI. Companies with custom workflow builders find it not only easier to sell, but have quick implementation timelines, easy user onboarding, strong customer satisfaction scores + NPS and lower numbers of CX tickets vs. opinionated user interfaces that require a ton of change management 2. Build on top of the industry standard, outdated 3rd party app Datasnipper, a $1B startup backed by Index did exactly this. How? They built AI to empower Auditors with an intelligent automation platform, sitting on top of excel. By building on top of excel, Datasnipper's users feel right at home and it reduces the friction of change management in a big way for all parties involved. 3. Go into the company with forward deployed engineers, build custom workflows and integrations and then find ways to productize it for other customers Palantir is the obvious case study here and people are catching onto this playbook. My thesis is that we'll see dozens of companies launch with this consultative approach, going after Accentures ~$65B of revenue. What many VCs used to mistake as a services company model is now widely accepted as a new GTM strategy based on a two key things: 1. Palantir's success 2. Unlocked acceleration of software development driven by AI Firms, including ours, General Catalyst, Elad Gils and Jared Kushners are all partnering up with large enterprises to launch new companies that have an easy time selling in what feels like are custom builds, but in the longterm are clearly going to be products. Working on an AI software solution with either of these three approaches? We'd love to chat.

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The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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SaaS Buying Behavior Has Shifted: What Software Founders Need to Know The landscape for software purchasing has changed dramatically since 2021. Companies are no longer buying software for every need—instead, they’re categorizing products as either "nice-to-have" or "must-have." Here's what that means: Scrutiny is Higher Than Ever: Every purchase is heavily examined, with CFOs, head of sales, and RevOps teams now playing key roles in the decision-making process. Must-Have or Bust: If your SaaS product isn’t essential, you’re at risk. Companies demand solutions that solve critical problems. The game has changed. Has your SaaS strategy?

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We analyzed hundreds of SaaS companies and the landscape of social proof to see how it has evolved and where it’s heading. We found 3 core eras and we’re now entering a 4th. 1. Era I: Analyst-led Back in the day, firms like Forrester and Gartner were the gold standard for advice. Their deep industry knowledge and extensive research were invaluable. But as the pay-to-play model crept in, their credibility took a hit. Only vendors who could afford to subscribe had access to their insights, which diluted the trust factor. 2. Era II: Platform Review-led Enter the era of platform review sites like G2 and Capterra. They shook things up with crowd-sourced reviews and ratings, much like Yelp but for software. This user-generated content, often incentivized by rewards, became a goldmine of information. Other review platforms emerged, and many were eventually acquired by larger firms. 3. Era III: Community-led (in this era) Then came the community-led era, which we’re still in. Professional networking sites like LinkedIn and online communities on Slack have become the new hubs for software recommendations. Influencers play a big role, offering genuine advice at first, but soon monetizing their opinions, which is eroding trust. Companies lean heavily on customer advocacy and influencer programs, but overusage and compensation blur the lines between genuine and incentivized endorsements. 4. Era IV: Authentic Word of Mouth (entering this era) Now, we’re in the era of authentic word-of-mouth. Organic feedback from peers is more valuable than ever. Platforms like Noble facilitate honest conversations between prospects and existing customers, making uncurated, genuine opinions the new norm. This approach emphasizes transparency and trust. This fourth era changes the game. Vendors confident in their product-market fit will embrace this approach, even at the risk of occasional negative feedback. Transparency builds trust, and unlike G2 reviews or paid influencer posts, this model of facilitating authentic word of month ensures people hear directly from customers in a trusted way. Read the full post in The GTM Newsletter in the comments. For more of what we are seeing across go-to-market and the insights of 350+ of the best operators in the game, join 52,000+ other revenue leaders who read The GTM Newsletter weekly below. ✍️

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Unpacking AI Reseach Product Manager role so you don't have to. 🥅 The goal of AI Research Product Manager is to make sure that researchers work on something that will ultimately drive business outcomes. 🥅 Researchers want to research, a PM wants to create impact. 📜 AI Research Product Manager should be comfortable to read and understand scientific papers and should have a good idea on what might come next. 🗿 The challenge of the role is often to find the intersection of user and customer needs and the things where the research space is pushing towards. 🧪 It is also extremely challenging for a PM to build credibility and interact effectively with scientists. After all, you are not a scientist. 👩‍🔬 To be efficient in this role you should be comfortable building prototypes by yourself. These notes are from my recent podcast with Guillaume (Synthesia / Google Search). Subscribe to my substack to get more product management insights: https://lnkd.in/eF76ZQqD

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Trying to get from $0 to $1M ARR? Find your PMF. Obsess over identifying a problem that a big enough market urgently needs solving. Talk to your customers about their most painful problems and validate your assumptions. Not all feedback will be valuable. Focus on insights from your early adopters—they become your ICP and shape your product. Stay true to your core customer profile. Chasing revenue or artificially widening the PMF dilutes your product's effectiveness. Your product can only meet so many needs. Tailor every feature to your ICP. Your first version is your Minimum Sellable Product (MSP). Make the MSP easy to use and beneficial. With customers using your product, continually collect feedback to improve: 1 - Offering 2 - Marketing messages 3 - Sales calls 4 - Pricing structure 5 - Demo flow If you reach $1M ARR, congratulations! Your product has real demand. Next, it’s time to think about scaling to $10M ARR. More on that soon.

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AI could kill ARR as we know it. And, in doing so, it could kill SaaS metrics as we know them ⤵ Annual recurring revenue (ARR) is the building block of SaaS metrics. And it's the basis of SaaS valuation multiples. It's (usually) high margin, predictable and growing. Which means SaaS companies are (usually) on track to become highly profitable at scale. AI throws this off. What's not classic ARR: - Charging per successful AI resolution (Intercom, Zendesk) - Charging per credit used (Clay) - Charging per task completed (11x) - Charging per photo edited (Imagen) - Charging per demand package generated by AI (EvenUp) - Charging per conversation (Salesforce) We're moving away from charging for *access* to software and to a model of charging for the *work delivered* by AI & software. This might mean greater volatility. Variable margin profiles. Seasonal revenue. Project-based, non-recurring use cases 🤯 Welcome to the new "ARR": annual *re-occurring* revenue. Some implications of this shift: 1️⃣ Spending much more time unpacking the components of revenue. 2️⃣ Moving away from ARR multiple valuations to looking at last 12 month revenue (or, even better, last 12 month margin $). 3️⃣ Looking much more closely at revenue concentration -- I suspect there will be a far wider variance between the smallest & largest accounts. 4️⃣ Measuring newer things like "time to ramp" and "share of wallet" as predictors of future success. --- I unpacked this shift with CJ Gustafson in his Run the Numbers podcast (check the comments). And shoutout to Dave Kellogg for a great keynote on this topic 🙏 🎁 For more insights on SaaS growth & pricing, check out Growth Unhinged — my free weekly newsletter: https://lnkd.in/exTbjKaM #ai #saas #finance

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For anyone who's used one of the many YC AI developer platforms on top of gen AI knows that it's really easy to get to 80%, but really hard to get to 95+%. After spending hours modifying a prompt, switching where you make your function calls, toggling the many available parameters, etc. there's a very real, deep and elongated trough of disillusionment. It's fine, it's not a knock on those platforms, it just means AI developer platforms are just a middle layer for developers (as they should be) rather than a replacement for applications, and no, not every professional will become a prompt engineer using a developer platform. It will be up to the application layer to solve the most important challenges of how to make AI actually "good enough" for actual business use cases. To do so, those applications will need to be very focused on a narrow set of jobs to be done, and be much more opinionated (than say, deterministic workflow tools of the past.) For example when we were building Journey builder (a workflow engine for phone and sms customer engagement), we avoided "getting started templates" that wasted peoples' time, and instead provided a blank canvas to our customers because we knew they'd know what journeys to build that made sense for their customers and business. We didn't worry about them "shooting themselves in the foot" with nonsensical flows, infinite loops, etc. That was a choice to not be opinionated. We're taking the opposite approach to AI Phone Agents. A blank canvas this time around would lead to a very flat and elongated adoption curve for AI Phone Agents in the Contact Center, as opposed to the S-curve that we think is possible. But that S-curve is not going to just follow the improvement path of the LLMs. Product managers working on the application layer will be the deciding factor.

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The Problem with Customer Feedback Is actually a problem of attention Most startups do customer feedback wrong. They spray generic NPS surveys at all users indiscriminately, getting back 1-4% response rates of mostly shallow data. This feels like doing something, but it's theatrical busy work. The reality is that users who matter most to your business - those about to churn, those who just hit a key milestone, those who could be evangelists - need different questions at different times. And they need to feel heard, not just surveyed. Duonut (UP23) (founded by @ramyabhaskar14) is replacing traditional surveys, they are using GenAI to create contextual conversations with users at moments when feedback actually matters. Duonut adapts follow-up questions in real-time based on responses, much closer to how a founder would actually talk to users. The insight here is that good feedback isn't about asking questions - it's about having the right conversations with the right users at the right time. Most startups don't do this because it hasn't been scalable. Until now. This is the kind of tool I wish existed when we were building our user feedback systems at Upekkha early on. If you're drowning in low-response surveys and shallow feedback, you might want to check them out: https://lnkd.in/gCehKYPE

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Perfecting your product and GTM strategy are surely important. But Box CEO and co-founder Aaron Levie argues there's one factor that outweighs all others in startup success: recognizing and capitalizing on tech and macro tailwinds. Fighting against them? You're setting yourself up for failure. Our full conversation here: https://lnkd.in/gEAPFR-A

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ICYMI: SaaS Spending Benchmarks - 2023 vs. 2024 Been digging into how AI is reshaping SaaS spending behavior lately. The most surprising finding? The broader SaaS market hasn't really seen the cost structure changes you'd expect. While the short-term impact seems to be minimal, I've still uncovered some interesting insights about where things are heading. Still putting the finishing touches on my analysis, but I'll be sharing the results in my newsletter early next year ✌️ #saas #startups #ai #financials #benchmarks

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A lot of companies are still dealing with the fallout from the 2019 growth at all costs. Many overhired without really knowing how to use all those people, and now we’re seeing layoffs as they try to fix that. Lot's of good people on the market. Now, with copilots of all sorts boosting productivity I think companies need to be smarter about hiring and focusing on real value. Building smaller teams of more senior and motivated people.

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Going from Zero to Product Market Fit is like getting out of a Bermuda Triangle For every direction that works out, an equal and opposite direction works too It is shocking how few get to product market fit Only 15 out of 100 SaaS companies get to a million dollars in revenue When you have PMF, it is an awesome feeling Everything is going smoothly for you It also feels scary and out of control You push one unit, and ten units of progress happen But when you don’t have it…. You push ten units of effort, and not even a unit of progress happens It feels like a Sisyphean punishment Moving a boulder up the mountain In the GenAI age, I hear arguments that go “The focus should be on the core functionality. Like how musicians concentrate on the music itself” This is clearly a faulty analogy Businesses providing playback formats (cassette tapes, CDs) are obsolete today Gen AI brings AI below the API No more does one need to stand up a team in ML Spend a few million dollars to “translate languages” or “transcribe accurately” GenAI now assures that Getting to PMF boils down to the basics - Do you understand the problem deeply - Can you build a 10x better solution - Can you execute & iterate faster - Don’t die in the process (cash) What has worked for you to get out of the Bermuda triangle?

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An increasing percentage of startups have solo founders and no VC funding, which is EXACTLY what you'd expect in an era when AI makes building businesses easier. The data for this comes from Carta, an equity-management platform, so it might even UNDERSTATE the situation because solo founders with no need to distribute equity (e.g. those with no employees) probably aren't represented. Also note how steep the climb was in 2024. The growth rate from 2022 to 2023 was 3.53%, while the growth rate from 2023 to 2024 was 17.65%. This may be a harbinger of things to come, and have widespread implications, including for the VC industry.

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Have you ever looked for theory that can answer - how to approach investors - what kind of product to build first - how you’ll defend the company against competitors This new article from CDA partner, Kevin Maney extends Steven Johnson's writing from the book Emergence to #CategoryDesign. All #Startups and #VentureCapital teams should dig in. (link to article in comments)

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Interesting answer from AlphaSense CEO Jaakko (Jack) Kokko on why they acquired Tegus - all about access to proprietary data, in this case the transcript library on private companies. You recently acquired a company called Tegus. Was this about having more proprietary data to feed into your models? First and foremost, it was about providing our customers with the best possible intelligence on the market, especially on private companies. A couple of years ago, we acquired a company called Stream, the No. 2 player in what you might think of as a library of interview transcripts between investors and experts on a company — their former executives, competitors, customers, partners. It allowed us to produce information that you can’t access anywhere else, and our customers loved it. Tegus is the market leader in that sector and was the natural next step for us. They have the world’s best library of private company content, and we immediately wrapped our search capabilities, our generative AI, our monitoring capabilities around their content and made it much more valuable. We now have the broadest coverage of insights on both public and private companies, allowing our AI to provide even better answers to our customers.

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A big part of getting into YC is recommendations by alumni. Don't know any YC alumni? Work at a YC startup. https://lnkd.in/gq67hQwN If you've helped YC founders achieve an exit, you've been given the best possible exposition of the YC way. With that experience under your belt and the blessing of the founders, you'll have a strong application to YC. If you want to do a startup, I would completely skip FAANG and just work at YC startups. At a FAANG, you will condition yourself in ways that make it very difficult to do a startup, that would take tens of thousand of dollars of therapy to undo. These are divergent paths.

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My saas.wtf newsletter just cracked the 2,500 subscriber mark. 🎉 These were the 5 most popular articles: 1️⃣ My Go-To SaaS Benchmark Reports: https://lnkd.in/eVwEqJPj 2️⃣ SaaS Valuation Myths: https://lnkd.in/e2sA_Bgn 3️⃣ Time to hit $1M ARR: https://lnkd.in/emmyt-Hy 4️⃣ Micro M&A for SaaS Startups: https://lnkd.in/e5sCK2mB 5️⃣ Cohort Analysis using AI: https://lnkd.in/ebAxhdma I hope you enjoyed the others too. 🙂 On to the next 2500 subs! #saas #startups #founders #kpi #analysis #valuation #ai #benchmarks #pricing #newsletter

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