The Wire

Real-time updates from ANZ tech sales

24 days ago
News

Shopify hits $13B revenue at age 20, growing 34%

## The Numbers Shopify turned 20 and shipped its strongest quarter since the pandemic surge. Q1 revenue hit $3.17B, up 34% year over year. GMV crossed $100B in a single quarter for the first time, up 35%. Free cash flow margin held at 15%. Most software companies decelerate at this scale. Shopify accelerated. Growth rate improved from 27% a year ago to 34% now, at a $13B+ run rate. That does not happen by accident. ## The Revenue Mix Shifted Subscriptions (the monthly merchant plans) grew 21% to $750M. That is 24% of total revenue. Merchant solutions, mostly payments and lending, grew 39% and now makes up 76% of revenue. Shopify makes roughly 3x more when merchants succeed than it does selling them software. The subscription is the wedge. The business is taking a cut of $100B+ in commerce flowing through the platform. Shopify Payments alone processed $67B in GMV, up 41%, handling 67% of all platform volume. Look only at MRR and you would think this was a 16% grower. MRR hit $212M, up 16%. Total revenue grew 34%, more than double the MRR growth rate. The gap is success-based revenue: payments, Shop Pay, Capital, the revenue that scales with merchant volume rather than merchant count. ## Why This Matters for B2B Sales Shopify figured out a decade ago what many SaaS companies are learning now: the highest-growth revenue is usage-based and success-based, not seat-based. The model where you grow when your customer grows is the one compounding fastest. For go-to-market teams, this is the playbook. Land with subscription, expand with usage. Nearly 90% of Q1 revenue came from merchants who have been on the platform more than a year. That is expansion revenue, not new logo hunting. Shopify's commercial motion is platform-led, not classic enterprise field sales. Merchant success, partner ecosystem, and payment take-rates drive growth. That structure scales differently than a traditional SaaS sales org, but the numbers show it works at $13B. The company was founded in 2004 by Tobi Lütke, Daniel Weinand, and Scott Lake after building an online snowboard shop. Twenty years later, it is still accelerating. Durable does not have to mean slow.

SaaStr
25 days ago
News

Earlytrade raises $14.2M, US revenue up 7x since 2024 launch

**Earlytrade closed $14.2 million** led by S3 Ventures and Brick & Mortar Ventures. The construction fintech, founded in Sydney in 2018, runs a payments marketplace for contractors and subcontractors. The round brings total funding to $35.5 million. Previous raises: $12.5 million Series A in September 2022, $8.4 million seed in 2019. ## US revenue up 7x Earlytrade launched in the US in 2024. Since then, US revenue grew sevenfold, the company says. They now claim 211,000 subcontractors on the platform and have processed over US$3 billion in early payments globally. The fresh capital goes toward US expansion and building agentic AI into the payments workflow. Worth noting: no detail yet on what "agentic AI" actually means in practice or how it changes the product. ## What this means for hiring Earlytrade has not announced headcount plans tied to this raise. Construction fintech sits in an active funding segment: competitors like Trayd (payroll/workforce software) and Grand (AI trade credit) have raised capital in the past 18 months. That usually signals commercial hiring, but Earlytrade has not confirmed numbers, roles, or locations. For context, a $14.2 million round at this stage typically funds 8 to 12 commercial hires over 12 to 18 months, split between AEs, SDRs, and customer success. No confirmation yet on ANZ versus US hiring split. ## Open questions Earlytrade has not disclosed: current ARR, team size, sales leadership, or whether the US expansion means closing ANZ roles or keeping dual operations. Founders Guy Saxelby and Piers Symons remain at the company. For sales professionals watching construction tech: the category is getting capital, but comp data and hiring specifics remain sparse. If you are tracking construction fintech opportunities, this is one to monitor for role postings over the next quarter.

SmartCompany
25 days ago
News

Why B2B vendors pay $1 per AI call while customers pay $20/month

## The Margin Squeeze Nobody Talks About Here is the problem: when your B2B product does something genuinely complex with AI, you might be paying $1.00 or more per API call. Your customer can do roughly the same thing in Claude for a fraction of a cent, amortised over their $20/month subscription. SaaStr CEO Jason Lemkin laid out the actual numbers this week. A simple chatbot reply on Haiku costs about $0.004. Fine. But a moderately complex document analysis (20,000 input tokens, 2,000 output on Sonnet) runs $0.09 per call. A sophisticated 100-page analysis hits $0.375. Run that on Opus 4.6, the model that actually impresses a CFO, and you are at $0.625 per call. Add extended thinking and you regularly clear $1.00. Meanwhile, a Claude Pro subscriber at $20/month can run hundreds of those same complex analyses per day. Rough math: 10 complex document analyses per day is 300 per month at $20 flat, or about $0.067 per analysis. You just paid $0.375 to $1.25 for the same call. ## What This Means for Enterprise Software To build a profitable business, a $1.00 API call needs to become a $3 to $5 per-query charge once you factor in infrastructure, engineering, support, and margin. Or it gets buried in a monthly subscription where you are quietly hoping users do not run complex queries too often. Neither option feels great when your customer's other browser tab is open to claude.ai. SaaStr runs 12+ internal and external AI apps. Most use under $200/month total in API tokens. But two of their most complex apps cost $0.30 to $1.00 per usage. For a startup with high ACV and low margin sensitivity, that works. For a B2B leader at $100M+ ARR with thousands of customers running queries at volume, the math gets uncomfortable fast. ## Why AI Features Feel Thin The market pressure runs one direction: toward using cheaper models and simpler workflows. That is why so many AI features in enterprise software feel mediocre compared to what you can do in Claude directly. It is not that engineering teams are bad. It is that the cost of making features genuinely great does not fit the pricing model they have already committed to. McKinsey notes that B2B sales leaders are using AI for opportunity identification and value-based pricing, but competitive advantage depends on using data and AI to improve decision-making, not just adding AI features. The bottlenecks are leadership alignment, data quality, and organisational change, not model access. For sales leaders evaluating AI tools: ask what model is running under the hood, what the token usage looks like for your use case, and whether the vendor is paying $0.004 or $1.00 per call. That will tell you whether you are getting genuinely sophisticated AI or a thin wrapper that will feel outdated in six months.

SaaStr
25 days ago
News

AI vendors grow without big sales teams: Anthropic lands 54% of enterprise logos self-serve

# Sales is the caboose at AI leaders, not the engine Anthropic landed 54% of new enterprise logos through self-serve in 2025. They built the motion as an MVP in January, shipped it in February, and it was already closing more than half of enterprise deals. The sales team is real. It matters. But it is catching demand the product generated, not manufacturing pipeline. When growth runs at 300% to 500%, reps process a firehose instead of filling an empty funnel. That is the new reality at frontier AI vendors. OpenAI, Anthropic, and the other AI platform companies all have sales teams. No one is saying the function is dead. But the math changed. Before AI, great execution could take a B2B company from 80% growth to 120% growth. That gap mattered. The best sales org was often the category winner. Now the spread is 500% versus 15%. And when the gap is that wide, the sales team is not creating it. The demand environment is. If you are growing 500%, you are in the right category at the right time. If you are at 15%, no comp plan fixes that. ICONIQ's 2026 GTM benchmarks show high AI adopters generating $640k of net new revenue per GTM head versus $370k for everyone else, a 73% gap. In post-sales it was wider: $1.1m versus $600k per head. That is not a story about better reps. It is a story about which side of the AI demand line a company sits on. ## What this means for sales teams Sales still matters in both worlds. At 500% growth you need a team that can scale fast enough to capture demand without breaking. At 15% you need discipline to defend the base and win every contested deal. Both are hard. But in neither case is sales setting the trajectory. The trajectory was set before the reps picked up the phone. That changes hiring priorities, team structure, and what quota attainment actually measures. AI vendors are building around product velocity and self-serve adoption first, enterprise sales motion second. Columbia's GTM analysis says firms are shifting toward GTM engineers, AI agents, and leaner lead-generation functions. Challenger's research shows AI is improving forecast accuracy, lead scoring, pipeline analysis, and coaching, but the human seller is focusing on complex deals and retention, not top-of-funnel volume. For sales professionals, the split is clear: you are either working harder than ever to process 10x demand, or you are in slow-growth territory where every deal is contested. The middle is gone. And that means the skills that matter, the roles that exist, and the comp structures that make sense are all changing. Sales is not dead. It is just not the engine anymore. At least not at the companies growing fastest.

SaaStr
25 days ago
News

Labor considers startup CGT carve-out after founder backlash on tax changes

The federal government is considering a carve-out for startups from its capital gains tax reforms, according to the Sydney Morning Herald. Treasurer Jim Chalmers is examining options that would let qualifying startups keep the existing 50% CGT discount instead of moving to the proposed inflation-indexed model. The shift comes after sustained backlash from founders, investors, and venture capital groups who argued the changes would materially increase tax on exits and deter investment in the sector. ## What's changing Labor's budget proposal would replace the 50% capital gains tax discount with cost-based indexation for assets held longer than 12 months, starting 1 July 2027. The plan includes a minimum 30% tax on real gains. Some existing small-business CGT concessions would remain. ## The pushback ABC reported the government was caught off guard by the response from startup founders and small business owners. Industry Minister Tim Ayres did not rule out future changes, saying the treasurer was in talks with the startup community. Financial Review coverage also noted growing unrest on the Labor backbench about exempting startup investment, suggesting the issue has become politically sensitive inside the party. ## The design challenge Ministers are still working through how any startup concession would be structured. The core challenge: defining what qualifies as an innovative startup for tax purposes without creating exploitable loopholes. Options reportedly include using existing eligibility frameworks from startup incentive programs and employee share scheme concessions as templates. ## Why it matters For sales professionals at startups or considering startup roles, this matters for equity compensation. CGT treatment directly affects the after-tax value of share options on exit. If your startup sells and you have vested options, the tax bill on that gain could change significantly depending on how this policy lands. The debate also signals broader market uncertainty for the ANZ startup sector. Founders are watching comp structures and exit planning closely. VCs are reassessing risk models. That uncertainty flows through to hiring plans and OTE structures for sales teams. No timeline yet on when Treasury will land on a final design or whether the carve-out will actually ship.

SmartCompany
25 days ago
News

Australia Post lifts parcel prices 4.95%, small businesses passing costs through

Australia Post is raising parcel delivery prices by an average 4.95% from July 1, forcing small businesses to pass shipping costs through to customers. MyPost Business rates, MyPost Business Pickup, and retail parcel services will all increase. Contract customers, typically businesses shipping hundreds to thousands of parcels monthly, will see rates rise 4.25%. The government-owned business cited global fuel market disruptions and Middle East tensions as drivers behind the annual price review. Australia Post operates 4,118 outlets nationally and is wholly owned by the Australian Government. **Market context matters here.** Australia Post acquired last-mile delivery platform Rendr in April 2026, aiming to expand same-day delivery coverage to 90% of Australia's population. The company has made at least three acquisitions recently, including an earlier investment in Shiperoo in March 2025. Translation: they are defending parcel market share against private logistics providers while also needing to maintain margins. Small retailers are not happy. Gold Coast baby goods shop Tiny Trader posted on Instagram that it has historically absorbed nearly a third of shipping costs under flat-rate postage. "Until now, we've absorbed a large portion of shipping costs ourselves," the business wrote, signalling that practice is ending. **Why this matters for sales teams:** If you are selling to retailers or ecommerce businesses, shipping cost inflation is now a real line item pressure. Customer acquisition costs stay flat or rise, but so do fulfilment costs. That squeezes margins and makes budget conversations harder. Worth flagging in discovery if logistics spend is material to your prospect's P&L. For logistics software or fulfilment tech sellers, this is fuel. Australia Post's push into same-day delivery through acquisition suggests the market is moving toward speed and flexibility, not just cost. If your product helps businesses optimise carrier mix or automate shipping decisions, this pricing change is a conversation starter. Australia Post is Melbourne-headquartered and self-funded, operating under both commercial and community service obligations. The tension between those two mandates shows up in pricing decisions like this one.

SmartCompany
26 days ago
News

AI-forward GTM teams run 43% leaner, double revenue per rep: ICONIQ data

# AI-forward GTM teams run 43% leaner, double revenue per rep: ICONIQ data ICONIQ Growth's January 2026 State of Go-to-Market report surveyed 150+ B2B GTM executives and combined it with portfolio operating data. The headline: companies embedding AI across revenue functions are running significantly leaner teams while generating roughly double the net new revenue per FTE. ## The headcount gap is structural At $10M to $25M ARR, AI-forward companies run about 20 GTM FTEs. Lower-adoption peers at the same revenue run 35. That is a 43% difference. The gap holds up market. At $25M to $100M it is roughly 45 FTEs versus 65. At $100M to $250M it is 125 versus 165. At $250M to $500M it is 275 versus 350. Median GTM headcount growth at $100M+ companies is 9% in 2026 versus 25 to 40% five years ago. The shift is not just tooling: revenue orgs are being redesigned around AI, hiring more builders (engineers who understand revenue workflows) rather than adding more operators. ## Pipeline is now sales-sourced Sales-sourced pipeline makes up 62% of total pipeline. Marketing-sourced sits at 19%. If your forecast assumes marketing fills most of the top of the funnel, that assumption no longer matches the data. ## Conversion is the problem, not volume Demo-to-close rates have fallen 5 to 10 points. Sales cycles have lengthened. Buyers are still entering the funnel at similar rates. The decline is in closing. The driver is buyer caution: in a fast-changing market, choosing the wrong vendor is costly, so buyers are evaluating longer and harder. More leads will not fix a conversion problem. Invest in the close: ROI cases, proof-of-value, faster time to result. ## POC converts at 50%, up 14 points YoY Conversion from POC or free trial now runs around 50%, roughly double the SQL-to-close rate. Most companies still run POCs informally, without success criteria, a timeline, or an owner. Make the POC a defined stage with measurable success criteria. ## Comp is shifting toward durable revenue Net New Recurring Revenue as a component of AE comp rose from 25% of companies in 2025 to 33% in 2026, an 8-point move and the largest single-year change ICONIQ tracked. Net Dollar Retention as an AE comp metric rose another 5 points. Median NRR now sits in the 108% to 110% range, while the top quartile holds above 123%. If a durable, expanding account pays a rep the same as a deal that churns in a year, the comp plan is misaligned with where the value is. The leaner teams also attain quota at higher rates: 67% of ramped AEs hit quota at AI-forward companies versus 59% elsewhere. Source: ICONIQ State of Go-to-Market 2026, January 2026 survey of 150+ B2B GTM executives.

SaaStr
26 days ago
News

AI agents bypass Marketo, Outreach, Salesloft: why automation tools face existential risk

## The Tools Agents Don't Need SaaStr ran an experiment: ask Claude, OpenAI, and Gemini which APIs work best for agentic workflows. Stripe topped the list. No surprise there. What caught attention: when asked about marketing automation and sales engagement, all three models said the same thing. Marketo, Outreach, and Salesloft have no use in an agent-driven workflow. The reasoning: an agent crafts and sends better emails itself. It does not need a sequence builder, a template library, or a cadence tool. These platforms exist because humans cannot manually send thousands of personalized emails or track hundreds of follow-ups. Agents have no such constraint. They generate each message in real time, pull context from the CRM, and execute the cadence natively. The entire productivity layer disappears. ## What This Means for Sales Stacks SaaStr's own numbers show the shift. Salesforce spend went from $12k to $22k annually. Seats dropped from 10 to 2 plus one agent. Token consumption is up because agents run constantly. Meanwhile, the Marketo equivalent got cut entirely. Their AI VP of Marketing writes campaigns, segments audiences, sends emails, and measures results without touching a marketing automation platform. The pattern extends across categories: **Marketing automation:** Marketo, HubSpot enterprise, Eloqua. Built to let marketers template communications at scale. Agents generate each email fresh with full context. **Sales engagement:** Outreach, Salesloft. Built to run sequences because SDRs cannot track 400 cadences manually. Agents run cadences natively and generate each touch based on actual prospect behavior. **Conversation intelligence:** Gong, Chorus. Built to extract insights from call transcripts for humans to act on. Agents ingest transcripts directly and act without a dashboard layer. **Project management:** Atlassian (Jira, Confluence), Monday, Asana. Built for human coordination. Agents have memory and context windows. They do not need Kanban boards or wikis. Atlassian, the $4.4 billion Sydney-headquartered company, sits in an interesting position. Its tools are built for human coordination in software development and IT service management. Strong market position, but fundamentally designed around biological constraints agents do not have. ## The API-Native Advantage The reason some platforms work for agents while others do not comes down to architecture. Stripe exposes clean APIs with structured data and predictable workflows. Legacy B2B tools like Marketo, Outreach, and Atlassian were built around human-driven processes: configuration, manual handoffs, UI-centric usage. That makes them less agent-ready. Agents favor API-native workflows over platforms that require a human to click through screens. When the product itself is the workaround for human limitations, agents bypass the product entirely. ## The Ratio That Matters Gartner data shows vendor consolidation taking 30-50% of new AI spend. The first cuts: tools that exist purely as productivity layers for humans. Even if agents only handle 30% of these workflows by end of 2026, that is 30% of the customer base with no native need for the product category. This is not about agents using the same tools faster. This is about entire categories becoming redundant because the constraint they solved no longer exists. The sales engagement platform was a workaround for humans who could not personalize at scale. The agent does not need the workaround. ## ANZ Context For Australian sales teams evaluating AI tools: watch what gets consolidated first. Marketing automation and sales engagement platforms are high-risk renewal categories. The comp model for SDRs and AEs may shift as outbound volume becomes less about human touches and more about agent-generated quality. Atlassian remains the standout ANZ company in this conversation. Founded in Sydney, major global enterprise footprint, but the core products are still built around human coordination. The question for any ANZ tech employer in the collaboration or productivity space: does your product exist because humans have constraints, or because the problem itself requires human judgment? The agents have an answer. Some categories stay. Some become features inside broader AI systems. Some just get cut.

SaaStr
26 days ago
News

Startup Year axed: $15M program drew eight students, spent $80k

## Program cut after massive miss on targets The federal government's Startup Year program is dead. Eight students signed up. The budget was $15.4 million over four years, designed to support 2,000 loans per year. Total funding released: $80,000. Department of Education officials confirmed the numbers at Senate Estimates on Friday. The program was discontinued in May's federal budget. ## What the program promised Startup Year offered HELP-style loans for students and recent graduates joining university-run accelerator programs. Participants could access up to two loans of $11,800 each, repayable through the tax system like HECS debt. Labor first floated the idea in 2016, revived it in Anthony Albanese's 2021 budget reply, then allocated $15.4M after forming government in 2022. Years of consultation and legislative changes followed. The gap between plan (2,000 loans per year) and reality (eight total) suggests the problem was not awareness or legislation. Universities were supposed to deliver accredited accelerator programs. Either they did not build them, students did not want them, or both. ## Why this matters for sales hiring Startup accelerators are a common talent pipeline for early-stage companies building SDR and AE teams. When accelerators do not launch or attract participants, that pipeline stays dry. The university angle matters too. Campus recruiting and graduate programs are standard plays for building sales teams in ANZ tech. If university-backed startup programs cannot get traction, companies relying on that channel need other sourcing strategies. Worth noting: $15M is not massive funding by startup standards, but it is real budget. The 0.4% uptake rate (eight participants versus 2,000 target) is a data point for anyone building programs that assume students will trade future tax liability for current opportunity. No word yet on whether the eight participants who did sign up will keep their funding or face clawback.

SmartCompany
26 days ago
News

Nine writes off $49M Pedestrian investment, hands it to Vinyl for nothing

## The Numbers Nine Entertainment exited Pedestrian Group for nominal consideration. Total investment: $49 million across two acquisitions (60% stake for $9.3M in 2015, remaining 40% for $39.3M in 2018). Peak valuation: $100 million. Current valuation: effectively zero. Vinyl Group (ASX:VNL) is acquiring 100% of the youth media brand with no cash, debt, scrip, or ongoing royalties. Nine's ASX filing confirms the transaction followed unsolicited buyer enquiries, not a planned divestment. ## What Happened Pedestrian, founded in 2005, became part of Nine's digital media strategy during the traditional media pivot to online audiences. The business expanded through licensing deals (Business Insider, Lifehacker, Kotaku, Gizmodo, Vice, Refinery 29) and launched Pedestrian Television on 9Now as a youth-focused VOD channel. The cracks showed in 2022 when Business Insider's licence ended. A pivot to web3 content (The Chainsaw) failed as crypto imploded. By mid-2024, CEO Matt Rowley departed alongside multiple redundancies. Licensing deals for Lifehacker, Kotaku, Gizmodo, Vice, and Refinery 29 ended. Only PopSugar remained. Nine, which has collapsed from a $4 billion valuation to $1.18 billion in seven years, is offloading assets across the board: Domain sold, ACM regional print divested, radio stations and regional TV network also gone. ## Why It Matters This is a textbook case study in failed digital media investment. Nine bet $49 million on youth audience growth and digital advertising revenue. The hypothesis: build reach, monetise through programmatic and branded content deals. The reality: licensing costs, restructuring cycles, and a shrinking digital ad market. For sales teams in media or publishing, the lesson is clear: audience does not always equal revenue. Pedestrian had brand recognition and traffic, but could not convert that into sustainable margins. The web3 pivot showed desperation, not strategy. Vinyl is picking up a known brand for nothing, which either means they see capital-efficient upside in the audience, or they are betting on cost-cutting and cross-platform synergies with their existing publishing portfolio. Either way, Nine walked away from the entire investment rather than continue funding losses. Pedestrian co-founder Chris Wirasinha now runs adtech startup Linkby, which raised a $23M Series B in 2024. He got out early.

Startup Daily
26 days ago
News

Goterra enters administration after failing to raise growth capital

## Goterra enters administration after failing to raise growth capital Canberra-based ag-tech startup Goterra has entered voluntary administration after failing to secure the investment needed to scale operations. Teneo's Daniel Walley and Martin Ford were appointed administrators on Wednesday. The company is pursuing a going concern sale. ### The business model Founded in 2016 by former sheep farmer Olympia Yarger, Goterra uses modular, container-based systems housing black soldier fly larvae to process food waste on-site. The output: insect protein and soil conditioner. Customers include farms, restaurants, hotels, supermarkets, hospitals, and local councils across Australia. The pitch was decentralised waste processing with lower emissions and costs than landfill. The company raised an $8 million Series A co-led by Grok Ventures (Mike Cannon-Brookes' VC fund) and Tenacious Ventures. ### What went wrong In a statement to SmartCompany, a Goterra representative said the company has "working technology, contracted customers, and operating licences that are genuinely difficult to replicate." "This was not a product failure or a market failure," they said. "We ran out of runway while pursuing the investment we needed to scale." Worth noting: the gap between contracted customers and securing growth capital. That suggests the business was selling but could not close the funding needed to expand fast enough. ### What it means For commercial teams in climate-tech and ag-tech: contracted customers do not guarantee your next round. Goterra had technology, licences, and revenue. Still entered administration. Administrators are looking for a buyer to keep the business operating. If they find one, this becomes an acqui-hire story. If not, the contracted customers and operating licences get unwound. No public information on current sales team size or commercial leadership. The job was likely operations-heavy given the on-site processing model.

SmartCompany
27 days ago
News

Aurasell CEO: His old GTM stack cost $3M, 22 tools, 11 ops people

## The Stack Audit No One Wants to Run Jason Eubanks, CEO of Aurasell, skipped the AI vision slides at SaaStr AI 2026. He showed his old GTM stack from Harness instead: 22 products, $3M per year in software fees, 11 ops people to keep it standing. Those 11 were not driving revenue. They were stitching integrations, patching workflows, reconciling data across silos. Reps worked inside 10 to 12 products daily. They spent 24 to 30% of their time actually selling. The rest went to context switching, manual research, prep, follow-up, internal busywork. You are paying quota-carrying salaries for that. Eubanks calls it Project X-Ray: the audit he ran mid-COVID when his board asked him to cut burn. The finding that stuck was tool sprawl killing productivity, not headcount. ## Why Bolting Agents Onto Legacy Tools Fails Every niche tool brings its own database. That silo might sync with your CRM at the field level, but the context stays trapped. Conversations, activities, signals: all siloed. Agents need that context to act intelligently. Without it, they guess. Legacy vendors bolting agents onto fragmented data get what Eubanks calls "agentic thrash": low-quality automation, agents stepping over each other, costs going up. Aurasell's play: one unified data layer first, then agents. Structured and unstructured data in one place. 900M contacts, 85M accounts, auto-enriched. Conversational context across every channel feeding one graph, not a dozen silos. Automation layer on top, some agents prebuilt, others you build in natural language. The deployment is smart: run it as your AI-native CRM and migrate off legacy tools, or lay it on top of Salesforce or HubSpot with bidirectional sync. Rip and replace is optional. ## The Proof: $2.7M Closed in 41 Days Aurasell showed a new rep's first 41 days, ending in a $2.7M closed deal. Day one: territory already built and prioritized by ICP. No spreadsheets, no other tools. AI columns ran custom research at scale. Which accounts hired a new CRO this year? One prompt, pulled from reputable sources. Contacts pulled and ranked by propensity to engage, auto-enriched with email and phone. Sequences built by prompt, unique messages for every account and persona off the custom research. Cold call blocks surfaced with context attached: recent events, discovery questions, talk tracks. The company raised $30M seed from N47, Menlo Ventures, and Unusual Ventures. Co-founded by Eubanks and CTO Srinivas Bandi, both with prior runs at VMware, Twilio, Nutanix, Meraki, and Harness. The platform has logged 41 million agent runs. No ANZ office or local traction visible yet. Offices in San Francisco Bay Area, Bangalore, and London. ## What It Means for Sales Ops Stack consolidation is not new talk. What is different: Eubanks is not pitching a better dashboard. He is pitching one data layer that makes agents useful instead of adding more noise to a fragmented stack. The math is simple. If your reps are selling 25% of the time and working inside 10 tools daily, you have an ops problem masquerading as a headcount problem. The question is whether collapsing the stack into one AI-native platform actually works at scale, or if you are just trading 22 vendors for one very expensive vendor with a better pitch deck. Worth watching: does this play as consolidation theatre, or do the agent runs translate into quota attainment? The $30M seed says investors think the latter. The proof will be in customer retention and expansion, not demo walkthroughs.

SaaStr
28 days ago
News

Anthropic, OpenAI hiring sales faster than engineering: 20% of roles are GTM

## The numbers tell the story Anthroptic and OpenAI are hiring sales roles faster than any other function. At both companies, roughly 20% of open positions are in go-to-market: account executives, solutions engineers, partnerships, revenue operations. This is not a vanity hire. Anthropic's ARR sits at $47 billion, up from $9 billion six months ago. Enterprise customers account for 80% of that revenue. More than 1,000 businesses now spend over $1 million annually with Anthropic, double the count from two months earlier. OpenAI is planning to nearly double headcount this year, from 4,500 to 8,000 employees. Enterprise account executives are among the most aggressively recruited roles. ## Self-serve hits a ceiling The driver is deal size. A $1 million annual contract does not close through a checkout page. It closes through an AE who can navigate a multi-stakeholder buying committee, a solutions architect who maps the model to the customer's technical stack, and a CSM protecting the renewal. Anthroptic recently posted a Head of GTM Systems role. The job description says the company is scaling toward "multi-billion dollar revenue organisation" and needs systems for CRM, CPQ, finance, billing, order management, and revenue recognition. That is enterprise sales infrastructure, not startup motion. Other openings include GTM Strategy & Operations roles for Startups and Industry/EMEA segments. The company is hiring sales ops and planning, not just quota-carrying reps. ## What this means for sales professionals Three role types are prominent: **Forward-deployed engineers and solutions architects:** Technical sellers who embed in customer environments and ship integrations. Not pitch-deck AEs who present and leave. **Enterprise account executives:** Multi-stakeholder deal runners. The companies building the most sophisticated AI products in the world still need humans to close enterprise logos. **Revenue operations:** CRM admins, sales ops, systems architects. The back-office that makes a high-velocity sales org function. Anthroptic filed confidentially for a U.S. IPO on 1 June, days after a $65 billion round pushed valuation to $965 billion. A company that still leads with AI safety is walking into public markets on the back of an enterprise sales engine it has been building for months. Comp data for these roles is not public yet. Worth watching: if Anthropic and OpenAI are competing for the same enterprise AE talent as Salesforce, Google Cloud, and AWS, they will need to match or beat those OTE packages. That will set a new baseline for AI startup sales comp.

Sales Hacker
28 days ago
News

Vercel AI agents handle 93% of support, replaced entire SDR team

## The numbers Vercel is running hundreds of AI agents internally and posting specific metrics, per CPO Tom Occhino at SaaStr AI Deploy: - **96% of marketing content** now starts as AI-generated drafts from Slack threads - **93% of customer support inquiries** handled with no human intervention - **SDR team reabsorbed** after a lead qualification agent took over repetitive work Those are operating claims from a conference talk, not verified company disclosures. But they match what Vercel sells: AI infrastructure for developers building agents. ## What this means for sales teams Vercel's model is product-led, not sales-led. The company has historically run on self-serve developer adoption with enterprise layered on top. So when Occhino says they replaced SDRs with an agent, context matters: this is a developer tools company with a different motion than traditional enterprise software. The agent that replaced SDRs handles qualification, the mechanical part. The people who did that work moved to higher-impact roles, per Occhino. No word on whether "higher-impact" means sales, customer success, or something else entirely. ## The GTM agent: DealOne Vercel's go-to-market agent, DealOne, ingests sales calls, generates notes with action items, and posts coaching suggestions. It is built on Vercel's own AI SDK and runs in production. Occhino's thesis: every company will build custom agents, not buy off-the-shelf "agents as a service." You cannot buy a one-size-fits-all agent any more than you can buy a one-size-fits-all website. What you buy is the tooling to build and run agents cheaply. ## The reframe Occhino and CTO Malte Ubl (ex-Google, created Core Web Vitals) frame agents as a shift from UI-first software to autonomous software that acts on your behalf. The UI becomes a leaf node for human decisions. The trunk is headless automation. Their conclusion after two years: humans stop doing work an agent can do and move to work that compounds. The support team only touches hard or valuable tickets. Everything else is signal: product gaps or misconfigurations. ## What we do not know Vercel is a late-stage private company. Revenue is not disclosed. ANZ headcount or local operating scale is unclear from public materials. The SDR reabsorption and support/marketing percentages come from Occhino's talk, not a company earnings report or verified HR data. Treat this as a case study in how one well-funded developer platform is restructuring around agents, not a benchmark for sales org design across B2B. ## The broader pattern Multiple AI SDR and agent vendors (Artisan, 11x, AiSDR, others) are pitching automation for outbound and inbound qualification. Salary surveys show SDR comp holding steady in 2024, but role elimination concerns are real. Vercel is not eliminating the function, they are redeploying it. That distinction matters when you are deciding whether to take an SDR role in 2025.

SaaStr
28 days ago
News

Anthropic hits $183B valuation in 4 years, $9.4M revenue per head

## The numbers Anthropic, founded in 2021 by former OpenAI researchers, raised a $13 billion Series F in 2025 at a $183 billion post-money valuation. That is four years from founding to one of the highest private company valuations ever recorded. The company is doing roughly $5 billion in annualised revenue with about 5,000 employees. That works out to $9.4 million in revenue per person. For context: when Salesforce crossed $30 billion in revenue, it had roughly 79,000 people. When Google hit that mark, it had around 32,000. Anthropic got there with 5,000. Apple generates about $2.5 million per employee. Alphabet does $2.1 million. Anthropic is at nearly four times that rate. ## What this means for sales The old model assumed revenue and headcount scaled together. Every dollar of new ARR required more AEs, more SDRs, more managers to manage the managers. Revenue per employee crept up slowly as businesses matured. That link is breaking. When your product is intelligence delivered through an API, you are not adding humans to serve each new customer. The marginal cost of the next million in revenue is compute, not a bigger org chart. Anthropic's growth has been driven by enterprise API usage and strategic partnerships with Amazon and Google, not a traditional long-cycle B2B sales motion. The company does have a sales organisation, but its structure and comp are not publicly disclosed. For ANZ, Anthropic's footprint appears limited and mostly indirect. No large ANZ office or disclosed headcount, which suggests the region is served through global enterprise sales and cloud channels rather than a local team. ## The compression continues Cursor, the coding tool built by Anysphere, went from founding in 2022 to a $60 billion deal with SpaceX in 2026. Four years. The fastest path to an exit of this scale on record, and the largest tech acquisition ever. Wiz, the cloud security company, went from founding in 2020 to a $32 billion all-cash acquisition by Google in 2026. Six years, 30x ARR multiple. These are not outliers anymore. This is the new ceiling for AI-native companies that sell intelligence, not software. The old benchmarks do not apply.

SaaStr
28 days ago
News

SaaStr built AI VP of Marketing, runs it alongside human CMO

# SaaStr built AI VP of Marketing, runs it alongside human CMO Jason Lemkin's SaaStr spent over $500,000 building what it calls an AI VP of Marketing. The agent, named 10K, does not write content. It orchestrates: plans campaigns, manages pipeline in Salesforce, connects to Zapier and vendor APIs, and assigns daily tasks to the two humans running marketing. The build happened because existing AI marketing tools only generate content. SaaStr already has 5,000+ pieces of content across 13 years. The bottleneck was not more blog posts. It was knowing what to do, when, and executing a coherent plan across channels. 10K pulls from 5+ years of SaaStr data, feeds it into Claude Opus, and outputs a Replit app that plans every marketing action through mid-2026. It updates daily. When a campaign underperforms, it flags it on day two, not day sixty. No ego, no sunk cost fallacy, no defending pet projects. The operating model: run the AI VP and the human VP in parallel. Compare outputs. Debate recommendations. SaaStr says the AI's honesty and lack of agenda beats most human analysis, even if it is not better than a great human marketer. What it cannot do yet: manage third-party AI agents directly. 10K can analyse outputs from tools like Artisan or Qualified, but it cannot train them or change their behavior. Each agent has different guardrails, input processes, and training requirements. Lemkin says that orchestration layer, a single pane of glass that manages all AI agents, is a massive opportunity for someone to build. The broader shift: SaaStr had 20+ full-time employees in 2020. Today it runs 3 full-time humans and 20+ AI agents at the same revenue scale. The AI layer connects to 10+ years of data and automates workflows for a media/events business with multiple revenue streams: content, conferences, sponsorships. For sales teams watching this: the AI VP is not hypothetical. The comp is real, the workflow is live, and the early signal is that orchestration matters more than content generation. If your go-to-market stack includes multiple AI tools, the next question is who (or what) coordinates them.

SaaStr
29 days ago
News

SaaStr runs on 3 humans and 21 AI agents, books 614 meetings

# SaaStr runs on 3 humans and 21 AI agents, books 614 meetings SaaStr, the B2B media company and $200M VC fund, operates with 3 humans, 1 dog, and 21 AI agents in production. Their Amelia agent handled 2.2M website sessions, 442,000 chats, and booked 614 qualified meetings. Average sponsor ASP: $85k. The stack matters because it shows what GTM efficiency actually looks like when you cannot afford traditional headcount. No BDR team. No army of CSMs. Just agents that started as dashboards and evolved through 600 to 1,000 commits each. **The agents doing sales work:** - **Amelia AI:** Qualified inbound. Replaces 3 BDRs SaaStr could not afford. 614 booked meetings, $85k ASP. - **Agent Force:** Dead lead revival inside Salesforce. Highest open rate because it has the most context. - **Ava/Artisan:** Warm outbound to past attendees and lapsed sponsors. Recovered ~$500K this year. - **Monaco:** Cold ICP look-alikes. Fills its own funnel, books meetings autonomously. - **QBee:** Sponsor success. Manages 150+ accounts with personalized outreach and real-time risk flagging. No full Salesforce integration yet, already outperforms 85% of human CSMs at force-ranking health. **Why this matters for sales teams:** Owner.com provides the other half of the story. The restaurant SaaS company hit $100M ARR with 83% of new customers starting via Gradr, their free AI website generator. The product costs $1 in compute per restaurant and converts into $1/month subscriptions. Owner's CRO Kyle Norton took ARR from $3M to $21M in 22 months using AI-first GTM. Klaviyo, the public marketing automation company, is rebuilding product and engineering processes around agents. Not customer-facing features. Core internal workflows. **The connective tissue:** Headless Salesforce. None of these agents work if they have to use the UI. They use the API directly, in real time. **What this means for ANZ sales teams:** If a sub-10-person media company can book 614 qualified meetings and manage 150+ enterprise sponsors with agents, the traditional SDR-to-AE ratio is under pressure. Not next year. Now. The question is not whether to deploy AI agents. The question is whether your comp plan assumes headcount that will not exist in 18 months. Owner showed the blueprint: build the free AI product, convert to paid, bundle from there. 83% of pipeline starts with the agent. The AE closes the expansion. SaaStr showed the ops model: agents own qualification, revival, warm outbound, and sponsor health. Humans own high-touch enterprise closes and strategy. The gap between these models and traditional sales org charts is widening fast.

SaaStr
29 days ago
News

Lovable hits $400M ARR with 197 people: PLG at $2M per head

## The Numbers Lovable, the AI app-building platform, hit $400M ARR with 197 people. That is $2M+ in ARR per employee. For context: most B2B SaaS sits at $150k to $250k per head. They doubled from $100M to $200M in four months, then doubled again. Elena Verna, Head of Growth, spoke at SaaStr AI about what is actually working when AI writes 80% of your code and competitors can clone your features in a weekend. ## Feature Differentiation Is Dead Verna's thesis: when AI collapses the cost of building, feature leads last weeks, not years. You ship something new, competitors replicate it by next sprint. The 15-year B2B playbook of "better product, better engineers" does not hold. What still works as a moat: - Hardware (genuinely hard) - Network effects (hard to create, compound over time) - Proprietary data - Security and compliance (slow and expensive, which is the point) - Brand ("Brand is back, baby") Notably missing: SEO, SEM. When everyone can build the product, the relationship with the customer is what is left. ## The GTM Implication Lovable runs product-led growth, not enterprise field sales. Self-serve, rapid adoption, AI positioning. The scale came from distribution and workflow integration, not a 50-person sales org. Verna's take on org structure: everyone ships code, no traditional PM-to-engineer ratios. A `#shipped` channel with multiple production releases daily. A `#feedback` channel where ideas go live in 24 hours if one other person agrees. She also went from leading a 200-person growth team at Dropbox to firing herself out of marketing at Lovable and going back to IC. Her read: the next decade's career flex is the high-powered IC with AI agents, not the VP title. Worth noting for sales leaders watching quota-carrying AEs debate management tracks. ## What This Means for ANZ B2B If feature parity is a weekend sprint, sales teams cannot lean on product superiority. The pitch shifts to: distribution, integration, trust, compliance. Enterprise AEs selling against AI-native competitors need to know what moats their company actually has, because "better roadmap" is not one of them anymore. Verna says Lovable is still "on the product-market fit treadmill" at $400M ARR. The category moves so fast they recapture PMF every month. Scale did not let them slow down. It raised the stakes on velocity. No public data on Lovable's ANZ presence or sales team size. The growth engine appears to be product-led, not field-sales-led. That $2M per head number? That is the new benchmark when AI does the building.

SaaStr
30 days ago
News

Seven ANZ startups raised $17 million this week, quantum tech leads

Seven ANZ startups raised $17 million combined this week. That is split across early-stage deals, not the $50 million Series B rounds that come with 20-AE hiring plans. QuantX Labs led the pack with $7 million USD ($7 million AUD) in seed funding from Serendipity Capital. The South Australian quantum sensing startup builds optical atomic clocks for defence, satellite networks, and critical infrastructure. The tech is 10 to 100 times more stable than existing microwave-based systems, smaller, and portable. Applications include GPS resilience, radar networks, and telecommunications timing. Founded in 2016, QuantX Labs is commercial now, selling into defence and infrastructure. Quantum sensing is a crowded global market, but the defence angle and Australian sovereign capability positioning give them an edge for government contracts. The other six startups in the funding tally include Nardo (sports tech, backed by former Socceroo Tim Cahill), Alloovium and Gutgutgoose (both Y Combinator-bound), and four others not detailed in the source material. Combined, they raised $10 million. What this means for sales teams: These are seed and pre-Series A deals. Hiring plans will be measured, not explosive. QuantX Labs might add 1-2 commercial or partnerships roles given the defence sales cycle. The Y Combinator startups will focus on product-market fit, not go-to-market scaling yet. For context, ANZ startup funding is down 60% from 2021 peaks. Seed rounds are steady, but growth-stage capital is tight. The companies closing deals now are capital-efficient and commercial-ready. If you are tracking ANZ startup hiring, focus on startups 12-18 months post-seed: that is when the first AE hire happens. QuantX Labs is Adelaide-based. Quantum tech is a federal priority, which helps fundraising but does not guarantee fast deal cycles. Defence procurement moves slowly. Expect long sales cycles, high ACVs, and a small, specialised sales team structure.

SmartCompany
30 days ago
News

Harvey hiring enterprise CSMs at $190M ARR, Assembly AI ditches CS title

## The Old CS Playbook Does Not Work for AI Products Three fast-scaling AI companies shared what they are doing differently in customer success, and it cuts against a decade of SaaS conventional wisdom. The details matter because these are the companies setting the pace: Harvey at $190M ARR serving 100,000+ attorneys across 60+ countries, Assembly AI selling API infrastructure to technical buyers, and Lovable scaling product at speed. ## Harvey: Enterprise CS Is Still High-Touch Tom Ronen, VP Customer Success at Harvey, runs an on-site, executive business review motion that looks like 2001. That is deliberate. Harvey is selling AI into 200-person law firms where partners have practiced the same way for 30 years. No automated health score gets you there. The company is hiring Enterprise Customer Success Managers with explicit focus on value realization, adoption, ROI, and C-suite legal stakeholders across the US, Canada, and Latin America. Harvey is backed by Sequoia and OpenAI's startup fund, positioning against legal workflow and document automation platforms. **Takeaway for sales:** If your product requires deep behavior change inside a skeptical enterprise org, high-touch CS is a competitive moat, not a relic. ## Assembly AI: The CSM Title Is Broken Ryan Seams, Head of Customer Success and Solutions at Assembly AI, watched technical buyers change body language the second he said "customer success." They got defensive. The title now signals QBRs and renewal pressure, not technical partnership. Assembly AI tried renaming roles to Technical Account Manager. Recruiting died: two candidates in two and a half months. They switched to Forward Deployed Engineer. Pipeline filled immediately. The actual job barely changed. The brand did. **Takeaway for sales:** Audit what your titles signal to technical buyers. The words on your Slack profile change whether a customer leans in or shuts down. ## Lovable: Stop Saying AI Monica Perez, Global Head of Customer Success at Lovable, stopped leading with AI in customer conversations. Her argument: saying "AI-powered" signals you are behind, not ahead. AI is becoming baseline infrastructure. Lovable's onboarding opens with what the customer will unlock, not what the technology can do. The moment you stop saying AI in every conversation is the moment you become AI-native. ## What Actually Drives Retention Bobby Cooper, founder of Retention Intelligence, shared platform data showing over 50% of CSM activities have no correlation with retention. CSMs accrete work over time, become jacks of all trades, and teams bloat. The fix: map every activity to whether it changes the product outcome for the customer inside the intended timeline. If it does not, it has no place in the playbook. **Takeaway for go-to-market:** Run a time-and-motion study on your post-sales team before you add headcount. You are likely funding work that does nothing for retention. ## Adoption Is Not Enough Anymore For traditional SaaS, decent seat utilization plus the same buyers at renewal meant CS did its job. That breaks with AI products. A law firm can log into Harvey constantly without changing how it operates. Logins are not transformation. Harvey tracks five ROI pillars and asks CSMs to tag value stories against them, because the goal is reducing non-billable work and speeding up matters, not racking up active users. **Worth noting:** These are AI-native companies redesigning CS around product speed and technical buyer expectations, not legacy process. The competitive set matters. Harvey competes with legal-tech enterprise vendors. Assembly AI competes with speech-to-text and voice AI infrastructure providers. Lovable is growing extremely fast in a category where users build software in plain English. The CS model follows the product motion, not the other way around.

SaaStr
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