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This Week in SaaS July 14 – 20, 2026 You can read the full breakdown here: https://lnkd.in/dgV-5YjA Here's what stood out this week👇 💸 Big SaaS VC Rounds ↳ Fireworks AI raised $1.505B Series D at a $17.5B valuation. Daily token volume climbed from 15 trillion to over 40 trillion year over year and the platform passed a $1B annualized run rate ↳ Meshy raised $400M Series B at $1.38B for generative 3D modeling into games, film and spatial hardware ↳ Emergent raised $130M Series C at $1.5B. Over 12 million apps built on the platform in a year, many by non-technical solo operators ↳ Spectro Cloud raised $100M Series D led by Goldman Sachs Alternatives for AI infrastructure management ↳ Flex raised $70M at $1.2B for AI-native private banking. Annualized revenue tripled and payment volume passed $10B ↳ InstaLILY raised $60M Series B with Home Depot Ventures and United Rentals for AI forward-deployed engineers ↳ Sable raised $45M co-led by Sequoia and 8VC for autonomous AI customer-facing employees 🤝 SaaS M&A Deals ↳ SAP completed its acquisition of Prior Labs with a €1B+ committed investment. The bet: enterprise decisions run on tables and structured records, not large language models ↳ ZetaDisplay acquired retailmediatools, adding retail media planning to its digital signage platform 🧠 The takeaways Software isn't dead. Workflow automation is what's at risk. → Rory O'Driscoll argues roughly 10% of a large pre-GPT portfolio was effectively dead on arrival when ChatGPT shipped. → If your product only automates a workflow, move up the stack or expect to get rolled. Your agents should beat your best rep. Not match your average one. → An agent matching median human performance isn't a product. → Deployment standards should be set against top performers. When the model becomes a commodity, the moat moves → Operators across six verticals landed on the same conclusion. Everyone has frontier model access now. → Defensibility comes from the data surface and the workflow you own end to end, not from which model you call. Write the spec before you build the agent → SaaStr published the full 20-page spec behind their AI VP of Marketing after five months in production. → Output quality tracks input detail. Invest in the spec. The through line: the model isn't your advantage. The data you own, the workflow you control and the standard you hold your agents to...that's the whole game now. Today I run two private communities that might help you: ▪️ SaasRise: for SaaS CEOs and founders scaling from $1M to $100M in ARR ▪️ GrowthRise: for experienced B2B marketing leaders and founders under $1M leading their own GTM efforts Links to apply in the comments below 👇

Harry Stebbings' $600K Salesforce Story Sounded Familiar. We'd Already Done It For a Client. Harry Stebbings shared it on the 20VC x SaaStr episode this week, and it's been making the rounds since. A CEO told him their team replaced Salesforce with a CRM they vibe-coded themselves. Built for their own workflows, connected directly to their AI agents, and live in three weeks. The $600,000 annual Salesforce bill disappeared. We read that story and immediately thought of one of our own clients. They were paying a significant Salesforce subscription every year for a CRM that handled only a fraction of what they actually needed. We built them a custom CRM around the way their sales team worked instead of forcing them into a generic platform. Within weeks of going live, they dropped their Salesforce subscription. Stebbings also shared another example on X: the CEO of a $10 billion public company said their Chief of Staff replaced software costing $1.2 million annually with an internally built solution completed in just three weeks. Not everyone agrees this approach scales. Some SaaStr contributors argued that most companies still rely on Salesforce for its integrations, app ecosystem, and collaboration capabilities. That's a fair point. For large organizations with complex workflows and dozens of integrations, replacing an enterprise CRM overnight isn't realistic. But one thing is becoming clear: the cost of building custom software has dropped dramatically. A CRM, internal tool, operations dashboard, or workflow that once required months and massive budgets can now be built in weeks with the right team and AI-powered development. Where this approach actually works The businesses seeing real wins with this approach tend to share a pattern: ✓ The team is small enough that a custom system doesn't need to be a collaboration platform for hundreds of users ✓ They're only using a small slice of what the expensive platform actually offers ✓ Their workflows are specific enough that off-the-shelf software forces awkward workarounds ✓ They have engineers or a partner who can build it properly, not just fast Miss those conditions, and a quick build can become a maintenance problem. Get them right, and you eliminate a recurring software cost that may no longer make business sense. This is the work we do at iTechnolabs Our client's CRM wasn't built in three weeks. It was carefully planned, developed around their actual sales process, and integrated with the tools they already depended on. The outcome was the same: a Salesforce subscription they no longer needed and a system built specifically for their business. If a large part of your budget goes toward a CRM, support platform, or internal tool you've outgrown, it's worth exploring whether a custom solution makes more sense. Get in touch with iTechnolabs Inc. Tell us what you're paying for and what you actually use, and we'll honestly tell you whether a custom AI-powered solution is the right fit.

Most CS teams know which accounts need attention. The problem is proving it when the data lives across six systems that don’t talk to each other. Salesforce. Amplitude. Jira. Slack. Backstory. Public sources. Pulling it all into one defensible tiering model used to take weeks of cross-functional back-and-forth. Haya Kamal, Head of Customer Success at Backstory, did it herself in days. At the SaaStr Ai day tomorrow, she’ll show how she used Claude to reconcile signals across those tools, build a four-tier account model with revenue projections and reprioritize resources around the accounts with the most headroom. This is a working session, not a slide tour. Real backend. Real iterations. And the exact sequence: which tools, in what order, and where a human still has to make the call. Wednesday, July 22nd, 2026 | 09:00 - 09:30 AM PDT Come see it live! Register now: https://lnkd.in/e_Cz5Apd #SaaStrAI #AI #RevenueAI #Backstory

New!! 5.4M Impressions Driving 11,749 to Their Signup Form: What Advertising on SaaStr.AI Media Actually Delivers: Most B2B media buys are … a black box. You sign up, wire the money, the ad “runs,” and 60 days later you get a PDF with an impressions number and no way to check any of it. You have no idea if a single real buyer even saw it. We do it differently at... Continue Reading

New SaaStr Fund portfolio company CRO his first week on the job: Note: he also started before he started. We’ve talked a lot on SaaStr about how to make that critical hire: the VP Sales.  I&#…

In six months, Jobright.ai went from $5M ARR to approaching $10M. But the bigger milestone was not the revenue. It was completing the other half of the hiring market. We entered 2026 helping job seekers across the U.S. and Canada find fresher opportunities, understand their fit, navigate sponsorship constraints, and avoid wasting time on roles where they had little chance. Then the year started moving fast: → February: Crossed $5M ARR. → March: Reached 2M+ users. → April: Grew to 3M+ users with a team of fewer than 10. → May: Brought our employer platform to SaaStr with one message: Hire Top Talent at AI Speed. → June: Expanded Jobright TNT to 52,000+ vetted builders and 150+ hiring companies. → Early July: Approaching $10M ARR. The numbers matter, but the product evolution matters more. We saw the same problem from the other side.  Hiring teams were struggling with the same system from the opposite direction: AI made application pipelines exploded, and recruiters had to find real signal inside thousands of increasingly similar applications. That is why Jobright is evolving beyond a job-search platform. For candidates, we provide clarity, stronger matching, and better access.  For companies, we provide qualified, high-intent talent with less noise and more context. The future of hiring should not be more applications or more resumes. It should be a better connection between the right person and the right team. #Hiring #AI #Recruiting #FutureOfWork #JobSearch #Startups #Jobright

Q2’2026 @ Hyperline :) I shared an informal Q2 update with our partners last week. Feedback was so good that I decided to share the public version here too. TL;DR: - strongest quarter to date - 350+ customers now using Hyperline - real momentum in the US - much smarter product - partner ecosystem becoming a true GTM engine - but also… our annual offsite in the South of France, the World Cup in NYC, the Knicks parade, and heatwaves in Paris and NYC 🥵 We welcomed a new batch of ambitious companies, incl. AI companies backed by tier-1 global investors, fast-growing European businesses now scaling in the US, companies valued $1B+. (and many more we can't share there yet!!!) The companies joining Hyperline are getting bigger, their business models more complex, and the problems they expect us to solve now go far beyond simply generating invoices. (which is *exactly* where we want to be) Product-wise, our AI vision is turning into very real use cases: 👾 MCP server to explore data and automate workflows through AI tools 🚨 Sentinel to continuously monitor what needs attention 🤝🏻 Customer Intelligence to surface churn risks and upsell opportunities much earlier We also launched our Rillet integration, deepened relationships with some amazing advisory partners (incl. Polaris Ops, Onside Accounting, 2CFinance, 28 North Consulting, Ocobo, RevOpsAI, Novutech and more) - helping us across implementations, leading to great collaboration between teams and intros going both ways. Together with our partners, we hosted 4 events in Q2: - Finance & Monetization Dinner in SF during Stripe Sessions with Northlane - CFO Dinner in SF during SaaStr week with Anrok and Quanta - RevOps Breakfast in NYC with Qobra - Finance Leaders Lunch in Paris with Fincome for PE-backed companies Thank you to everyone who sent a lead, made an intro, joined an event, or recommended Hyperline when we weren’t in the room. Let’s go for Q3.

For b2b marketers or media nerds, this is a best-in-class content + social playbook worth following, studying, and emulating. Consider this little media microcosm: If someone was starting a company in 2015, you would tell them to read: TechCrunch, VentureBeat, and maybe Saastr as a deep cut. If someone is starting a company today, you tell them to follow Peter Walker at Carta, Matt Schulman at Pave, and maybe the Mercury blog for founder stories. i am continuously flabbergasted by the level of high-density information people like Peter give away for free as he essentially builds his own mini-media company on behalf of Carta. It has put the Carta brand front + center of any and all founders (Peter has 170k followers, and considering how deeply niche/technical his content is, you can bet they are Carta's ICP). This is a playbook worth following, even if you operate in a completely different industry.

Most of the alarming SaaS statistics going around are true. That is not the interesting part.   I spent a morning checking them against their sources, expecting to debunk a few. Nearly all of them held.   Bain found that about 60% of Google searches now end without a click to any website. Pew measured what happens when an AI summary appears above the results: clicks on ordinary links nearly halve, from 15% to 8%. The channel that built a generation of software companies is quietly closing.   Meanwhile the cost of growth keeps rising. To win $1 of new annual recurring revenue, the median SaaS company now spends $2 on sales and marketing. The least efficient quarter of companies spend almost $3. That is Benchmarkit's data. And Alexander Group found 3 out of 4 software companies reporting declining retention, part of a slide in gross retention now running three years deep.   Even the giants are telling us something. SaaStr's analysis estimates that most of Salesforce's recent growth came from price rises, not new customers.   So the numbers are real. Here is the interesting part.   Every one of them is also an M&A diligence question when it is time to sell your business. Buyers stopped paying for growth some time ago. They pay for efficient growth: payback, retention, distribution that does not rent its audience from an algorithm.   Which means this is not only a growth problem. It is a valuation event, arriving early.   The founders who treat it that way, now, while it is still theirs to fix, will be the ones with options later.

Our SaaStr AI pitch deck analyzer has graded over 5,000 startup decks. Two passes through Claude. Complex data extraction, error correction, scoring. It took me a month of manual QA to get it stable. It worked perfectly for months, and reported back results daily. Then around January, it started telling every startup they had $100K in revenue growing 500%. I kept getting notified. Startup after startup. $100K, 500% growth. Over and over. I knew it couldn't be right. Nobody changed a line of code. What I think happened: a subtle model update (probably a dot release, not even a major version) introduced hallucinations into the complex multi-step workflow. When the model didn't have data, instead of saying so, it just decided every startup was at $100K growing 500%. I fixed it. It broke again. Fixed it again. Broke again. This is something almost nobody talks about in the vibe coding world. Your code can be perfectly stable and a model update you didn't ask for can break your production app. If you're building anything with multiple LLM passes, complex extraction, or chained reasoning, you need to monitor outputs continuously. Not just when you ship. Forever. Because the model vendor isn't going to tell you when an invisible "minor" release changes how your specific workflow behaves.

New!! Jason’s Takes on This Week’s 20VC: The Token Governor, the Net-New-Logo Test, and Why Renewal Stopped Being Safe: Going a level deeper on the points Harry, Rory and I hit this week, and what they actually mean if you’re building a B2B company right now. Here were the top SaaStr learnings I wanted to share from this weeks’ deep dive with the 3 of us. 1. If You Can Hire the Expertise Legally,... Continue Reading

New!! What a Great VP Sales Does In Their First Week. Watch For It.: One of the most controversial things I’ve said over the years on SaaStr is: You know within 30 days if you hired a VP right.  Especially a VP of Sales. Not at 90 days. Not at 6 months. By day 30, the split is visible. And if you’re not paying attention, you’ll miss it. Just... Continue Reading

The SaaStr piece frames the pricing shift well: everyone's fleeing per-seat toward usage, credits, and outcome-based models. What it glosses over is that your quoting tool isn't the bottleneck. Your revenue recognition is. I've sat in enough month-end closes to know how this breaks. You sell "credits." Marketing calls them credits, sales quotes them as credits, but revenue can't recognize a credit — it recognizes delivery against a performance obligation under ASC 606. So now finance is retrofitting a consumption model into a system (SAP RAR, NetSuite's Advanced Revenue Management) that was architected for ratable subscriptions and milestone billing. The Nue pitch is that a purpose-built revenue platform fixes this end to end. Maybe. But the hard part was never the quote-to-cash tooling. It's that usage data lives in your product telemetry, billing lives in your ERP, and the mapping between "customer burned 40K tokens" and "here's the recognizable revenue" is a reconciliation nightmare nobody demos. I've watched consumption pricing die in implementation because the metering data couldn't be trusted at close. Sales loved the model. The controller couldn't sign off on it. For those who've actually shipped usage-based billing: did your metering source survive an audit, or did you end up truing up manually every quarter? #RevenueRecognition #UsageBasedPricing #QuoteToCash #ASC606

This was maybe the craziest build week yet at SaaStr. Three humans, 21+ agents, and by Friday the question wasn't "what can we build?" It was "can we even operate everything we've already built?" #1. We connected Claude directly into Replit over MCP and it became my AI VP of product. Two models now debate features, share code, and ship, with a third (Codex) running underneath for free. #2. We moved 10 years of data and 450,000 people off Adobe Marketo to Salesforce. Agencies quoted a year and $100K. The hard part took 10K one hour and cost about $14. #3. And our Replit agent killed a $10K/year app in an hour. Unprompted. It looked at a vendor's dated API and said "why would you use that? I'll just build it," then did. The takeaways go deeper than the numbers: data-migration moats are dissolving, agent recommendations are the new shelf space, and the real bottleneck is no longer building. It's operating. Full recap of The Agents #010 below.

A year. $100,000. Two apps running side by side. 🫠 That's what we were originally quoted to migrate 10 years of Marketo data to HubSpot. So I let our agent scope it instead. It came back with ~300 campaigns worth moving over to Salesforce Marketing Cloud Next: SaaStr Annual attendees, sponsorship inbounds, everyone who's ever talked to our qualified agent, folks on our sites. I said: "Great. Are you moving them? Because I'm not touching 300 campaigns. That would take a year." 😅 10K said: "No, I'll just do it tonight." It didn't take a night. It took an hour. All headless through the agent and the API. 10 years of data. Every member, every detail, attached to the right campaign. Cost us $14. 🤯 Episode 010 of The Agents is live on SaaStr Ai👇

New!! Dear SaaStr: Do Any Top B2B Events Give You “The List”?: Dear SaaStr: Do Any Top B2B Events Give You “The List”? No, no good ones do, and for good reason. Top tier B2B events, especially ones focused on quality over volume, deliberately don’t hand over attendee lists to sponsors as a blanket data asset. If events sold attendee data freely, they’d destroy what makes them valuable... Continue Reading

New!! Claude Became Our AI VP of Product. We Moved 10 Years Off Marketo for $14. Our Agent Killed a $10K App in an Hour: The Agents #010: Amelia and I just published Episode #010 of The Agents. Same setup as always: three humans, 21+ agents in production, an 8-figure B2B + AI business and $200m of investments at SaaStr AI Fund, revenue running 140% of last year and growing again. Every week we get into what’s actually working, what broke, and what... Continue Reading

Vercel allegedly just got 32x ROI by replacing a 10-person lead qualification team with one engineer and 20% of another. That's the number every VC will share today. And almost all of them will miss the two things that actually made it work, because Jeanne DeWitt Grosser laid it out at SaaStr Ai and the boring parts don't screenshot well. Number one. The tripod. Vercel didn't hand an engineer a prompt and say go. They built every agent with three people shoulder to shoulder: a GTM engineer, a data scientist, and the single best subject-matter expert for that function. The SME is the control. The data scientist is the auditor. Most founders will skip this because it's slower and requires people who actually know the process. Number two. The six-week shadow mode. The engineer literally sat behind Vercel's best SDR and watched every tab she opened. LinkedIn, BuiltWith, the CRM, Slack history. Every step became a step in the agent's workflow. She reviewed every single output for six weeks. They only pulled the human out when she couldn't improve it anymore. The tripod is the control layer. The shadow is the audit period. Vercel built both before they gave the agent autonomy. Most founders will do it backwards. Automate first, build no oversight, then wonder why the pipeline went sideways. This is why I keep saying RevOps is becoming AI governance, not just CRM hygiene. To me...you bring in that operational infrastructure before you scale the automation. The absolute waste of time and money is forcing a sales leader to learn Salesforce and HubSpot architecture. Forcing a sales team to govern AI agents is the same mistake at higher stakes. Vercel didn't get 32x ROI from replacing people with AI. They got it from building the control structure first and letting the automation prove itself under supervision. Govern first. Then automate.

Over half of what CSMs do every day has zero correlation with whether a customer renews. That's not opinion — it's platform data from Retention Intelligence, presented at SaaStr AI 2026. McKinsey's latest piece on the agentic era, published this week, confirms the shift. The old CS playbook was built on activity: schedule the QBR, send the check-in, log the touchpoint. We optimized for volume because we couldn't measure impact. Activity looked like work, and work looked like retention. Agentic AI is exposing the gap. When AI monitors 100% of your base for behavioral signals in real time — login patterns, feature adoption, support velocity — the "I checked in last month" model collapses. The customer who went quiet three weeks ago doesn't care about your February QBR. The shift isn't "AI replaces CSMs." It's that AI forces the question CS has avoided: what does CSM activity actually cause? One thing to do this week: pull your last 20 saved accounts. Look at what intervention actually moved the needle — not what you logged. If you can't draw a line from action to outcome, that's the first thing to fix. What's the one CSM activity you suspect is busywork but haven't killed yet? #CustomerSuccess #CSLeadership #AIinCS #CustomerRetention #B2BSaaS #CSMLife

My co-founders and I listen to Lenny Rachitsky's Podcast religiously. It's basically our second standup, and our Slack blows up every time a new episode drops. Here are a few favorites from the last few months 👇 1/ Adam Mosseri (Head of Instagram, Meta) - his take on AI-generated content 🔥 Lenny asked: "Is the rise of AI a headwind or tailwind for Instagram?" Adam: "I think it's going to be a tailwind, but it's going to be a challenge. In a world where there's an abundance of synthetic content, I think people are going to seek out creativity and authenticity… Instagram was always about the person behind the content and their perspective, and I think that becomes more important, not less." Go off, Adam!! Preach. 2/ Catherine Wu (Anthropic) — the roles are merging into one 👬 Cat: "I think all of the roles are merging. PMs are doing some engineering work. Engineers are doing PM work. Designers are PMing and also landing code." Her point: the best engineers have great product taste - which is exactly how we see our eng team. P.S. we're hiring on the engineering side at Hue.!! 3/ Noam Segal (Tech Sentiment Survey) - people are happiest at early-stage startups His data: startups of 1-10 people have the happiest employees in tech, and "managers are still the biggest lever for happiness." He also splits 2026's workforce into four types — the Energized (41%), the Conflicted (35%), the Disoriented (12%), and the Resentful (12%). The thread across all three: as AI scales what we can make, the human parts turn into the whole advantage. Happy to swap notes on any tech podcasts that people enjoy! We also like Acquired podcast, SaaStr Ai, and 20VC's podcast 🙌