The Wire

Real-time updates from ANZ tech sales

2 months ago
News

65% of Australian startups have less than 12 months runway

## The Numbers Carta's Australian Startup Outlook 2026 surveyed 500 senior decision-makers. The runway data is bleak: 65% have less than 12 months of cash, 32% have 12 to 17 months, and 3% have 18 to 24 months. Zero startups reported runway beyond two years. The burn rate tells the real story. 86% increased burn over the past year, with 29% calling the increase significant. Victorian startups are under sharper pressure (71% with less than 12 months) compared to NSW (49%). ## What Changed Australian startups raised $5.48 billion in 2025, up 31% on 2024. That sounds good until you factor in concentration: the top 10 Q3 deals accounted for 70% of the $1 billion deployed that quarter. One company raised $330 million, Firmos closed $500 million. Most founders are fighting for scraps. 76% plan to raise capital in the next 12 months. 86% describe the fundraising environment as intensely competitive. Valuations are rising, but only for companies with clear AI positioning. If you raised Series A in late 2021 or early 2022, you are stuck: growth has not justified boom-era prices, so graduating to Series B is near impossible. ## Sales Team Impact Startups are responding predictably. 42% increased prices. The rest split between slowed growth plans, marketing cuts, bridge rounds, and layoffs. Hiring freezes are standard now, especially for roles that do not directly generate revenue. For Series A and Series B companies, this means tighter headcount planning. The 2021 playbook (raise big, hire 8 AEs, figure it out later) is dead. Now it is: prove unit economics, extend runway, hire only when quota relief is locked. IPO timelines stretched. 47% now view public listing as a five-plus-year outcome versus 10% targeting two years. Carta's managing director Bhavik Vashi frames this as maturity, not distress. Maybe. But shorter runways mean fewer bets on unproven sales talent and more pressure on existing teams to hit number. ## What This Means If you are looking at a startup role, ask about runway and burn rate. Not vague answers: actual months of cash and monthly burn. Ask what happens if the next raise takes six months longer than planned. If they raised in 2021 or 2022, ask about the path to Series B and whether the valuation makes that realistic. Runway is not just a finance problem. It determines whether your patch gets cut, your quota gets adjusted, or your role gets eliminated when the bridge round falls through.

SmartCompany
2 months ago
News

Tesla chair Denholm wants manufacturing tax credits, expanded R&D grants for ANZ startups

Tesla chair Robyn Denholm delivered a government-commissioned R&D review recommending expanded research grants and new manufacturing tax credits for Australian startups. The Strategic Examination of Research and Development (SERD) panel issued 20 recommendations after 49 roundtables and 785 submissions. ## The Numbers Australia's R&D investment sits at 1.69% of GDP, well below the OECD average of 2.73%. The report targets raising that figure through: - Reversing cuts to research grants - Introducing manufacturing tax credits (no dollar figures specified) - Reforming the R&D Tax Incentive (RDTI) for startups and scale-ups - Establishing a National Innovation Council to coordinate efforts Worth noting: the review provided no budget, timeline, or specific credit amounts. That matters for tech sales teams planning territory strategies around R&D-heavy prospects. ## What This Means for Sales If the federal government adopts these recommendations, expect: 1. **More qualified pipeline in manufacturing tech**: Tax credits typically unlock budget for automation, AI tooling, and process software. That is addressable market expansion. 2. **Longer sales cycles initially**: Companies wait for policy clarity before committing to R&D-dependent purchases. The review lacks implementation details. 3. **Shift in buyer priorities**: Prospects may delay purchases until they understand RDTI qualification criteria. Your discovery needs to include their R&D tax strategy. The panel included Emeritus Professor Ian Chubb, Professor Fiona Wood, and LaunchVIC CEO Dr Kate Cornick. LaunchVIC runs about 20 staff, no disclosed revenue. Tesla employs roughly 140,000 globally, over $100 billion annual revenue, but has no major owned manufacturing in ANZ. ## The Reality Check This is a recommendation, not policy. The government commissioned the review. They have not committed to funding it. Sales teams selling into R&D-intensive accounts should track legislative progress but not bank pipeline on it yet. Historical data says government R&D programs take 18-24 months from announcement to first payments. Budget accordingly.

Startup Daily
2 months ago
News

AI layoffs in tech: 4.5% of cuts, not the story companies tell

## The Narrative vs The Numbers Tech companies are calling it AI-driven efficiency. Salesforce cut nearly 1,000 employees in early 2026. Block shed 40% of staff. Amazon, Atlassian, and dozens more followed. The pitch: AI made these roles redundant. The data tells a different story. Only 4.5% of 2025 US job losses cited AI as the reason. That is 55,000 positions out of 1.2 million cuts. In Q1 2026, 37,045 tech workers lost jobs across 59 firms. Most of those cuts had nothing to do with automation. ## What This Means for Sales Teams Salesforce replaced 4,000 customer support roles with AI agents. Support, not sales. The distinction matters. Goldman Sachs data shows computer programmers and data entry workers sit at highest risk. Sales roles, particularly those requiring relationship management and strategic problem-solving, show limited AI displacement so far. Accenture cut 11,000 non-AI roles while hiring 37,000 workers with AI skills. Their AI and data team grew from 40,000 to 77,000. That is not replacement. That is rebalancing toward different capabilities. For ANZ sales professionals, the implications are clear: high-salary roles without AI proficiency face pressure. Entry-level transactional roles are vulnerable. Complex enterprise selling, consultative approaches, and strategic account management remain largely human-led. ## The Real Story Most tech layoffs are reactive cost management, not proactive AI transformation. Companies overhired during pandemic growth, overcorrected in 2023-2024, and are now using AI as cover for standard restructuring. Salesforce CEO Marc Benioff dismissed widespread AI layoff concerns before announcing targeted "rebalancing." That language matters. It is workforce management, not technological obsolescence. Worth noting: workers in AI-exposed occupations show no higher job loss rates than others, per Goldman Sachs. Early strain appears in marketing consulting, graphic design, and call centers. Sales, especially relationship-driven enterprise work, remains comparatively stable. The threat is real for certain roles. The scale is overstated. The timeline is longer than headlines suggest. If you are carrying quota in enterprise or mid-market, your biggest risk is not AI. It is quota changes when the CRO leaves.

Startup Daily
2 months ago
News

Leigh: uni degrees no shield from AI cuts, judgment beats credentials

## The take Your degree is not protecting you from AI-driven layoffs. Andrew Leigh, Assistant Minister for Productivity, says the traditional credential ladder is breaking down as AI devalues cognitive expertise. For sales teams, this means rethinking how you hire and what skills you build. ## What Leigh actually said In a Brisbane speech, Leigh argued the relevant split is no longer "has degree vs no degree." It is "judgment vs execution, oversight vs production." Meta-skills matter more: framing problems, spotting errors, allocating attention, taking responsibility. The coding degree you were told to get? Less valuable than knowing when AI output is wrong. Jobs and Skills Australia estimates nine out of ten roles face augmentation, not full automation. But augmentation still changes headcount and comp structures. ## What this looks like in practice Atlassian: 1,600 layoffs (10% of workforce) to self-fund AI and enterprise sales investments. 30% of cuts hit Australia. Even the CTO is out. CEO Mike Cannon-Brookes was direct: AI changes skill mixes and role numbers. Cost: $225-236 million, mostly complete by June 2026. That is a major ANZ employer restructuring its sales and tech org around AI. Not in 2030. Now. ## What this means for sales professionals If you are hiring, credentials matter less than judgment. Can this person frame a complex enterprise deal? Can they spot when the AI-generated email is tone-deaf? If you are building skills, focus on oversight, not execution. AI can draft the follow-up. You need to know if it is any good. Leigh's point about inequality: previous tech waves increased the wage premium for degrees. This one might do the opposite. Worth noting if you are advising team members on upskilling paths. ## The ANZ context Atlassian is Australian-founded, Sydney-headquartered, dual-listed (ASX and NASDAQ). When a company this size restructures 30% of ANZ headcount around AI, that is a market signal. Leigh estimates AI could add $116 billion to GDP over a decade if adoption is broad. But adoption means different headcounts and different comp structures. No one is safe because they have a degree. You are safe if you can do what AI cannot: exercise judgment, take responsibility, know when the machine is wrong.

SmartCompany
2 months ago
News

Founder leaves $100M ARR SaaS with 10,000 customers to start AI company

# The Numbers Behind The Exit Another founder just announced they are leaving a $100M+ ARR SaaS business. Not struggling. Not burning cash. Not losing customers. **What they walked away from:** - $100M+ ARR - 100%+ Net Revenue Retention (existing customers are expanding) - 10,000+ customers - Real distribution, real revenue, real product-market fit The reason: "excited to explore what AI could do in the space." The employees learned about it on LinkedIn. ## Why This Matters For Sales Teams If you are working at a Series B+ company right now, watch for this pattern. Founders leaving profitable businesses to chase AI is becoming common. That decision has downstream effects: **What usually happens next:** - Leadership vacuum while the board finds a replacement - Quota adjustments (sometimes up, rarely down) - Strategic direction shifts - Team morale takes a hit when the founder exits via social media For reps at high-growth SaaS companies: this is your signal to ask hard questions about roadmap, leadership succession, and whether the company plans to compete or get disrupted. ## The Part That Does Not Add Up Starting from zero means starting from zero. No distribution, no customer relationships, no revenue. Every AI founder is making the same bet right now, in the most crowded market in tech. Meanwhile, the company they left has: - 10,000 customers who already trust them - Years of usage data and workflow insights - $100M+ ARR to fund R&D without begging VCs - 100%+ NRR proving customers want to expand The obvious move: build the AI product for the customers you already have. Use the revenue to fund it. Use the relationships to design it. Use the data to train it. Instead, most founders are walking away to fight for attention in a market where every pitch deck starts with "AI-native." ## What Sales Teams Should Watch For If your founder starts posting about AI exploration: - Ask about the product roadmap in your next leadership call - Check if comp plans are changing (they usually do during transitions) - Watch for headcount freezes or territory restructures - Update your LinkedIn: leadership exits often trigger team turnover The grass is not greener. It is just different grass. And the sales team is usually the last to know.

SaaStr
2 months ago
News

SaaStr runs 4 AI SDR tools for 10 months: here is what actually works

## SaaStr runs 4 AI SDR tools for 10 months: here is what actually works Jason Lemkin's SaaStr has been running AI SDR agents for 10 months. Four different vendors: Artisan, Salesforce AgentForce, Qualified, and Monaco. They have sent hundreds of thousands of outbound messages, processed 1.5 million inbound sessions, and made every mistake available. Here is what they learned. ### You probably only need one vendor SaaStr runs four tools. You do not need to do that. They hyper-segment across platforms because each does something slightly different, but for 90% of use cases, one vendor handles the bulk of what you need. At most, you might end up with two: one for outbound, one for inbound. But do not start by buying three or four tools. Pick one that covers the majority of what you want and go deep with it. The tool matters far less than the strategy you bring to it. ### Your human playbook has to work first This is the single biggest mistake. Lemkin sees it from raw startups at $1M ARR and from multi-billion-dollar public companies. The pattern is always the same: they want to turn on an AI SDR without first proving that their human sales motion works. Or they use the AI SDR to test new copy they have never tried before. That is backwards. SaaStr did not deploy their first AI SDR until they knew exactly what was working with their human SDRs: which messaging converted, which segments responded, what cadences performed. Then they fed all of that into the agent. The goal of an AI SDR is to clone the best person on your team. These tools are cloning machines. They take context word for word and use it to build out their brain. If you feed them garbage context, or untested context, they will produce garbage results. You have to have done founder-led sales before you hand it off to an agent. The playbook has to work, at least a little, before you automate it. ### Segment ruthlessly SaaStr runs roughly 100 effective segments across about 1,000 contacts at a time. That sounds like a lot of work. It is. But it is exactly where the leverage comes from. They initially treated their inbound agent as one big bucket: "they are inbound to the website." But that was wrong. They actually have brand-new visitors, people who came via a social ad, prior sponsors returning, current customers checking on something, and lapsed customers browsing the pricing page. Each of those segments needs completely different context. A lapsed customer who churned in 2022 and is now browsing your pricing page? Your agent should know they are a former customer, highlight what has changed with the product since then, and speak to them totally differently than a brand-new cold visitor. Important caveat: none of the AI SDR tools today can auto-segment well enough to deliver these results on their own. You still need a human (or a tool like Claude) to define and manage the segments. The platforms default to "run one campaign, keep adding leads." That is the wrong approach. ### Consistency beats brilliance Your AI SDR does not need to write the greatest email on Earth. It needs to write a pretty good email, every time, without fail. SaaStr has sent 40,000+ messages through Artisan alone, 100,000+ through Qualified, close to 200,000 through Salesforce. Are these the greatest emails since sliced bread? No. They are solid, on-message, and they ship every time. **The context:** AI SDR deployment is moving fast. Salesforce AgentForce launched publicly in October 2024. Qualified raised $54M Series C in September 2024. Artisan has been in market since early 2023. The cost comparison matters: a human SDR in the US runs $60k-80k base plus benefits. An AI SDR agent costs $300-3,000/month depending on volume and vendor. ROI shows up when your playbook already works. What SaaStr learned is that the tech is not the bottleneck. Your sales process is.

SaaStr
2 months ago
News

Scopey Onsite raises $850k pre-seed, keeps ANZ presence after Ireland move

## Scopey Onsite raises $850k pre-seed, keeps ANZ presence after Ireland move Scopey Onsite, a construction tech startup founded in Australia in 2022, closed a €523k ($850k AUD) pre-seed round led by UK fund SFC Capital with backing from Enterprise Ireland. Co-founders Jenna Farrell and Gillian Laging moved the company's headquarters to Ireland while maintaining an Australian presence, with Laging still based in Melbourne. The platform converts WhatsApp messages and voice notes from construction sites into structured data records. The pitch: reduce project disputes and documentation gaps that cause delays in commercial construction. Teams text updates from site, AI turns it into searchable records for the office. ### What this means for sales roles No sales team size or hiring plans disclosed. Pre-seed stage typically means lean operations, founder-led sales, maybe one or two early AEs. The construction tech sales market in ANZ has seen hiring activity from established players like HammerTech (founded 2015, serves 80% of major Australian builders) and international expansions, but early-stage startups usually wait until post-Series A to scale sales teams. Construction software sales roles differ from standard SaaS: longer cycles, relationship-heavy, selling into project managers and site supervisors rather than IT buyers. Entry-level construction tech sales jobs often require understanding of construction workflows, not just sales fundamentals. Comp data for construction tech sales in ANZ is limited. Standard tech sales salary progression applies, but construction software typically sees lower velocity than pure SaaS plays. Enterprise deals can take 6-12 months, affecting commission timing. ### Market context Scopey competes with Irish firms like ConstructionBOS and Smart PMO, plus Australian players focused on safety and project management. The agentic AI angle (software that acts autonomously on site data) is newer positioning in a crowded construction tech space. The Ireland move gives access to Enterprise Ireland support and UK investor networks. Dual presence in Ireland and ANZ is common for founders with ties to both markets, though it complicates go-to-market strategy when you are selling into two regions with a small team. Worth noting: no revenue figures, prior funding rounds, or customer count disclosed. Pre-seed with $850k suggests early product-market fit validation, not scaled sales motion yet.

Startup Daily
2 months ago
News

Four ANZ startups raise $31.9M: MGA Thermal leads with $17M clean energy round

# Four ANZ startups raise $31.9M: MGA Thermal leads with $17M clean energy round Four Australian startups raised $31.9 million this week, marking a slower period after months of sustained funding activity. The deals spanned clean energy, legal tech, health, and construction. ## The Numbers MGA Thermal closed the largest round at $17 million. Main Sequence and IP Group Australia backed the Series A for the Tomago, NSW clean energy startup. The company previously raised $5.7 million in 2024. MGA Thermal builds electro-thermal energy storage systems that convert renewable electricity into heat stored in thermal blocks, then released as industrial-grade steam. Target market: heavy industry looking to replace fossil fuel heat sources. The funding will support commercial rollout, workforce expansion, manufacturing scale-up, and new customer projects over the next two years. ## Market Context This week's activity sits within a broader 2025 rebound. ANZ startups have raised $5.1 billion across 390 deals, up 24% year on year. But funding remains top-heavy: the top 20 deals captured 58% of total capital. AI-powered solutions led sector funding at $1 billion, followed by fintech at $868 million and biotech/medtech at $829 million. Enterprise software with AI integration continues to attract VC attention, particularly from firms like Airtree, Blackbird, and Square Peg. Series A cheques in the ANZ market typically range from $3 million to $15 million. Pre-seed and seed medians sit at $1 million to $3 million. NSW captured 33% of deals, Victoria 37%. ## What This Means for Sales Teams Clean energy deals like MGA Thermal signal growing enterprise sales opportunities in industrial decarbonisation. These are long sales cycles with technical validation requirements, but contract values run high. The quieter week reflects investor focus on execution over expansion. Portfolio data shows 77% of investors reported layoffs in their portfolio companies, suggesting cautious headcount growth even as funding increases. For sales professionals evaluating startup opportunities: check funding runway, historical burn rate, and realistic path to next round. Strong quarters matter more than strong pitch decks right now. 86% of founders report confidence in raising capital in 2026, but VCs are prioritising vertical-specific traction and demonstrated revenue growth over growth-at-all-costs models.

SmartCompany
2 months ago
News

Pay.com.au eyes $850M ASX listing, no sales team details disclosed

Pay.com.au is planning an ASX listing in April at an $850 million valuation, according to the AFR. The Melbourne-based fintech is raising another $85 million as part of the float. The company, founded in 2019 by Damien Waller, Edward Alder, and Grant Austin, lets businesses earn rewards points on B2B payments: supplier bills, payroll, super, tax. The points (PayRewards) convert to Qantas, Virgin, Marriott programs. Numbers: 50,000+ businesses using the platform, $10B+ in payments processed, $164M annualised gross revenue. Previous raises include $15M angel round (Hugh Robertson, Adam Schwab backing), $18M, and $53M at roughly $633M pre-money valuation. What is missing: any detail on the sales org. No disclosed headcount, no CRO or VP Sales named, no recent AE or SDR hiring announcements. For a business scaling this fast (385-529% CAGR reported), the go-to-market motion is unclear. Growth appears driven by partnerships with bookkeepers and CFOs rather than traditional B2B sales. Worth noting: less than 20% of Australia's 2.6 million businesses engage with loyalty programs. Pay.com.au is targeting that gap in construction, FMCG, retail, and finance verticals. Competition includes NorthOne and Lili. The company confirmed to SmartCompany it is "considering an ASX listing as one of several growth pathways" but offered no timeline specifics. IPO timing is bold given current market volatility. The $850M valuation implies confidence in the revenue model, but without visibility into the sales team structure, it is hard to assess how they plan to scale acquisition beyond channel partnerships. For sales professionals: if Pay.com.au is hiring post-IPO, expect focus on enterprise and mid-market segments where the reward arbitrage story sells itself. No comp data available yet.

SmartCompany
2 months ago
News

Firmable raises $20M Series A, plans US expansion with APAC sales data

## The Round Firmable, a Sydney-based B2B sales intelligence platform, closed a $20 million Series A led by Airtree Ventures. The company sells proprietary Asia Pacific company data through AI tools. Funding is earmarked for US market expansion. No word on current revenue, headcount, or sales team size. Leadership details remain undisclosed: no CEO or CRO named in available reporting. ## What They Actually Do Firmable provides sales intelligence focused on APAC companies. Think ZoomInfo, but built for the region where global sales intel platforms have weaker coverage. The company targets B2B sales teams looking for accurate prospect data in Australia, New Zealand, and broader Asia Pacific. The AI layer presumably enriches data or surfaces buying signals. Specifics on product features or customer count: not disclosed. ## Market Context This raise fits into Australia's 2025 VC rebound: $5.1 billion across 390 deals, with B2B software and AI attracting consistent capital. Series A median rounds are hitting around $11 million. Firmable's $20 million sits well above that benchmark. Airtree's lead signals strong ANZ VC backing. The firm typically writes $3 million to $15 million Series A cheques and has a track record with B2B SaaS companies scaling internationally. The catch: 2025 funding remains "top-heavy." The top 20 deals represent 58% of total capital. NSW and Victoria capture 70% of investment. Firmable benefits from being in the capital-efficient B2B category VCs still back. ## What This Means for Sales Teams If you are selling into APAC and frustrated with outdated contact data or thin company intelligence, watch this space. US expansion likely means product improvements and potentially ANZ enterprise customers as case studies. For sales professionals: Firmable will probably be hiring AEs and SDRs for the US push. No job postings or comp details available yet. If they follow typical Series A playbooks, expect 6 to 12 new sales hires over the next 12 months. Worth noting: sales intelligence platforms live or die on data accuracy and coverage. Firmable's APAC focus is a wedge, but converting that into US traction means proving the data works outside their home turf.

Startup Daily
2 months ago
News

Atlassian cuts 1,600 jobs, 10% of workforce in AI restructure

## The Numbers Atlassian is cutting 1,600 jobs, roughly 10% of its 16,000-person workforce. Restructure costs sit at US$225-236 million, including US$169-174 million in severance and US$56-62 million in office space exits. The Sydney-based software giant (NASDAQ: TEAM) notified affected staff by email 20 minutes after CEO Mike Cannon-Brookes sent an all-hands message. Share price rose 2% in after-hours trading, despite being down 50% year-to-date and 66% over the past year. ## What This Means for Sales Cannon-Brookes says the company is "self-funding further investment in AI and enterprise sales" through the cuts. Translation: they are reshuffling headcount to back their AI product roadmap and enterprise motion. Atlassian's Teamwork Collection passed 1 million seats and 1,000 customers in Q2 FY26, with 10%+ seat expansion per customer. Their Rovo AI agents drove 2.4 million workflow automations in the last six months of 2025. At Team '25 Europe, 74% of surveyed customers said they would increase Atlassian usage because of generative AI features. That customer sentiment creates upsell runway, but it also changes the skills mix the company needs. Cannon-Brookes was direct: "It would be disingenuous to pretend AI doesn't change the mix of skills we need or the number of roles required in certain areas. It does." ## Market Context Atlassian's market cap now sits below US$20 billion, less than privately-held Canva. New CFO James Chuong was brought in recently to sharpen execution amid investor concerns about AI disruption to their product suite. The company is also pushing Data Center pricing changes (effective February 2026) to accelerate cloud migration, which supports sales of premium AI features. The bet: AI features drive expansion revenue faster than the headcount cuts slow growth. No breakdown yet on which functions or geographies are hit hardest, or what this means for the ANZ sales organisation specifically. The company has significant Sydney operations but has not disclosed regional headcount splits.

Startup Daily
2 months ago
News

Anthropic tracking real Claude usage at work, 60% of employee tasks now AI-assisted

Anthropic, the $40 billion AI company behind Claude, released research tracking how AI is actually being used at work, moving past theoretical predictions into observed reality. The company introduced "observed exposure", a metric combining AI capability with real Claude usage data. The framework weighs fully automated work higher than AI assistance, tracking what is happening now rather than what could happen. The numbers: Internal surveys show employees at adopting companies use Claude in 60% of work tasks. Directive (automated) use rose from 27% to 39% between January and August 2025. Power users, 14% of the sample, report productivity gains exceeding 100%. Engineers handling complex coding and planning saw the strongest lift. Programming leads AI coverage at 75%, followed by data entry at 67% and customer service roles. Knowledge work shows high exposure, but adoption remains uneven. The data comes from workplace Claude conversations, not surveys or speculation. For sales teams, the implications are mixed. Customer service coverage suggests AI is handling routine inquiries, potentially freeing AEs from low-value interactions. SDR prospecting and lead generation sit in the target zone: repetitive, data-heavy tasks with clear automation potential. Discovery calls and demos remain human-led, but note-taking and call documentation are obvious AI plays. Anthropic's internal productivity gains (up to 50% for standard users) align with what sales ops teams report from AI note-taking tools and meeting assistants. The question is whether that translates to quota attainment or just faster admin. What the research does not show: hiring impact. Anthropic provided no data on headcount changes, role eliminations, or compensation shifts at companies with high Claude adoption. The company also disclosed no sales team size, CRO, or VP Sales details. Operations appear U.S.-centric with no mentioned ANZ presence beyond a recently announced Sydney office. The labor market impact remains theoretical until we see hiring data. Observed exposure tells us where AI is landing. It does not tell us who is losing their job or what happens to comp when a tool does 60% of the work. Anthropic competes with OpenAI, Google DeepMind, and xAI. The company has raised over $18 billion, including $4 billion from Amazon in 2024. No public revenue figures are available. Bottom line: AI is showing up in knowledge work, including sales-adjacent roles. Whether that means fewer SDRs or just better-equipped ones depends on how companies respond. The data says usage is climbing fast. The hiring signals are not here yet.

SmartCompany
2 months ago
News

Cloudflare hits $2.2B revenue, adds 37,000 customers in Q4, posts best ACV since 2021

## The Numbers Cloudflare posted $614.5M in Q4 2025 revenue, up 34% year-over-year. That is acceleration, not deceleration. They grew 27% in Q4 2024. For a $2.2B ARR business, moving the growth rate the wrong direction for slowdown is unusual. The enterprise motion is working. Customers spending over $1M grew to 269, up 55% YoY. They added 96 million-dollar customers in 2025 alone, compared to 55 in 2024. New ACV grew nearly 50% YoY, the fastest rate since 2021. They closed their largest ACV deal ever at $42.5M annually, and their biggest total contract at $130M over five years. Net new paying customers hit 332,000, adding 37,000 sequentially in Q4. That is 40% YoY growth in customer count at this revenue scale. Most B2B companies at $2B ARR are not adding customers at that rate. Growth at this stage usually means expanding existing accounts, not stacking new logos. Cloudflare is doing both. ## What This Means for Sales Teams Dollar-based net retention reached 120%, up from 111% a year ago. That is a 9-point improvement in 12 months at $2B ARR. The base is compounding without requiring new customer growth. Every new logo is additive. Sales productivity increased YoY for eight consecutive quarters. Quota attainment hit the highest level in four years. That is rare when scaling the sales org aggressively. Most companies see productivity per rep decline during ramp periods. Cloudflare grew the team and increased output per head. The company plans to reduce sales and marketing expenses from 36% of revenue in 2025 to 27-29% long-term, while projecting 28-29% revenue growth in 2026. That implies efficiency gains, not headcount cuts. Worth noting: specific ANZ headcount and comp details are not disclosed in public financials. ## Market Context Cloudflare competes with Akamai, Fastly, AWS, and Google Cloud in cloud security and edge computing. They hold 38% of the Fortune 500 as customers, and serve 4,298 accounts spending over $100K annually, up 23% YoY. Gross margins sit at 75% non-GAAP. Operating margin reached 15% in Q4, non-GAAP. The AI-driven demand is visible in the numbers. An $85M AI contract and a $45M Fortune 500 tech deal were flagged in Q4. These are not incremental expansions. These are platform consolidations. For sales professionals watching the public SaaS benchmark data: this is what re-acceleration at scale looks like. New ACV growing 50% YoY. NRR moving from 111% to 120%. Productivity and attainment both at multi-year highs. The metrics are not lagging. They are leading.

SaaStr
2 months ago
News

70% of AI features shipped zero revenue impact, SaaStr data shows

## The Numbers Do Not Lie Seventy percent of AI features launched in 2025 had zero measurable impact on revenue metrics. Another 20% drove usage but could not be tied to retention or expansion. That leaves 10% that actually moved the needle. These are not vanity stats. This comes from Jason Lemkin at SaaStr, who deployed 20+ AI agents across his own go-to-market function eight months ago. The result: $4.8M in additional pipeline, $2.4M closed-won, deal volume doubled, win rates nearly doubled. One full-time AE and a part-time "chief of AI" replaced a team of 10 SDRs and AEs. Most companies cannot show those numbers. ## What Counts as Impact Lemkin's threshold is specific. AI counts if it: - Lets you charge 20-50% more per seat - Increases retention by 20%+ for users who adopt it - Drives measurable expansion revenue (actual dollars, not engagement) - Closes deals you would have otherwise lost What does not count: chatbots 3% of users tried once, better search rebranded as AI, features built to check a box, anything you are "still measuring" after nine months. ## The Copilot Problem Everyone built a copilot in 2023-2025. Most are ghost towns. Saving users 10 minutes a day sounds great in demos. If those 10 minutes do not translate into something a CFO cares about, you built a nice-to-have. The copilots that worked did one of three things: made users measurably better at outcomes (not faster, better), replaced headcount, or unlocked new use cases that were not possible before. If your AI feature does not do at least one of those, it is a feature looking for a problem. ## What Winners Did Differently The 10% who drove revenue impact started with a relentless focus on building a materially better product. They figured out pricing on day one, before writing code. They measured ruthlessly: cohort analysis on retention, A/B tests on pricing, win/loss tracking specifically for AI features. They killed features that did not work. They sold it, not just shipped it. Sales enablement, training, marketing that positions AI value clearly. ## The Market Has Moved On We are past the "just ship something with AI" phase. Customers do not get excited about AI for AI's sake. Investors have seen too many demos that went nowhere. If you removed your AI feature tomorrow, what would happen to revenue in 90 days? If the honest answer is "probably nothing," you know what needs to happen. Start with revenue impact and work backwards. Everything else is a press release.

SaaStr
2 months ago
News

AI SDR works without brand, but lead warmth beats cold outbound 2x

## AI SDR Performance: Brand vs Execution SaaStr founder Jason Lemkin published performance data on AI SDRs after a year of testing. The summary: brand helps a lot, but it is not what determines whether AI SDRs work. The numbers: 11% positive response on recent SaaStr Annual attendees, 5.5% on older, less engaged audiences. Industry baseline for cold outbound sits at 2-4%. Even SaaStr's coldest segments beat that by 37-175%. Lemkin's take: brand gives you a warmer starting point, but execution drives the difference. Teams waiting for "enough brand" before testing AI SDRs are optimising for the wrong variable. ## What Actually Drives AI SDR Performance **Lead warmth over brand recognition.** You do not need a 13-year-old conference brand. You need a specific reason to reach out: prior event attendance, website visits, trial signups, lapsed customers. Any company with customers has these segments. **ICP precision.** Tight targeting matters more when you lack brand tailwinds. The question is not "do we have enough brand?" It is "do we know exactly who buys from us and what they look like before they buy?" **Human-written messaging frameworks.** Vendor templates optimised for average performance will not cut it without brand doing the heavy lifting. Your best AE needs to write the frameworks. The AI handles personalisation and volume. **Segment selection.** Do not launch on your coldest list first. Start with leads your human team ignores: dormant trials, return event attendees, inbound that came in off-hours, lapsed accounts. These segments do not require brand. They require the AI to say: "You showed interest before. Here is a reason to revisit." ## The Campaigns That Work Without Brand CRM reactivation is the clearest win. SaaStr's Agentforce deployment hit 72% open rates on ghosted leads. Not because of the SaaStr name, but because these leads already expressed interest. A $2M ARR SaaS company with 500 dormant trials has the same raw material. Inbound follow-up is second. Speed-to-lead still matters. A company without SaaStr's brand but with fast, contextual follow-up will convert inbound at higher rates than the same company letting leads sit for 48 hours. ## Market Context AI SDR adoption sits at 40% in North American B2B SaaS. The blockers are not typically brand strength. They are unclear ICP, poor list hygiene, and teams expecting plug-and-play performance without daily oversight. Lemkin's deployment uses Artisan AI for cold outbound (60,000+ emails) and Salesforce Agentforce for CRM reactivation. Both require human oversight. Neither relies on brand to hit baseline performance. The implication for smaller companies: you do not need to wait. You need clean segments, tight messaging, and realistic expectations. Cold outbound will not hit 11%. It might hit 5%. That still beats most human SDR teams on the same lists, at a fraction of the cost.

SaaStr
2 months ago
News

AI agent churn coming: why prompt portability kills SaaS retention

## The Portability Problem Jason Lemkin runs 20+ AI agents at SaaStr. His team copied their best-performing AI sales agent prompt into a competitor. It worked first try. Took 20 minutes. That is the retention crisis facing AI agent vendors. Prompts are portable. Switching costs are low. And buyers are figuring this out. ## Four Levels of Risk **Level 1: Copy-paste portable (80-85% retention)** AI SDR tools, outbound sequencers, meeting summarisers. The entire prompt transfers: tone, ICP, objection handling, qualification criteria. Switching time: hours. The only real moat here is not AI. It is email deliverability and domain reputation. If your vendor spent months warming sending domains, that is hard to replicate. Just not an AI moat. Expect annual vendor bake-offs to become standard. Buyers will test 2-3 options every renewal because they can. **Level 2: Prompt plus data (85-90% retention)** Customer support agents, sales coaching tools. The prompt transfers easily. The training data (thousands of tickets, months of call recordings) takes 2-4 weeks to migrate. Vendors overestimate this moat. Buyers underestimate the switching cost. Reality: it is a real project, not a weekend task. But it is not a six-month CRM migration either. **Level 3: Workflow embedded (90-95% retention)** Cursor, Copilot, AI agents deep in your CRM. The prompt is irrelevant. The value is in IDE integration, codebase indexing, the 47 workflow automations built on top. Switching means relearning an environment. That is a real cost, measured in lost productivity. **Level 4: Proprietary data moat (95%+ retention)** The article cuts off here, but the pattern is clear. ## What This Means for Sales Teams If you are buying AI SDR tools, run the test. Copy your prompt into a competitor during your trial. If it works, you have zero switching cost. Negotiate accordingly. If you are selling AI agents, be honest about your moat. "Our model is fine-tuned" buys you six months before models converge. UI is nice but not sticky. The only durable advantages: proprietary data, deep workflow integration, or infrastructure moats like deliverability. The AI agent land grab is real. But so is the churn wave coming behind it. Prompt portability is not a bug, it is the new reality of SaaS retention economics.

SaaStr
2 months ago
News

Anthropic opens Sydney office, hiring sales roles for ANZ enterprise accounts

Anthropic is opening a Sydney office in the coming weeks, making it the company's fourth location in Asia-Pacific alongside Tokyo, Bengaluru, and Seoul. The expansion comes as ANZ shows some of the highest per-capita usage of Claude globally. The AI company, founded in 2021 by former OpenAI executives, now serves over 300,000 enterprise customers worldwide, with nearly 80% of usage outside the US. ## ANZ enterprise focus The Sydney office will focus on supporting enterprise, startup, and research customers. Anthropic already works with Australian organisations including Canva, Quantium, and Commonwealth Bank. The company is recruiting for sales, research, and engineering roles to support local clients. "Establishing a local presence will help us to develop strong partnerships in ANZ and ensure Claude is built with respect for the unique goals, opportunities and challenges of the region," said Chris Ciauri, managing director of international at Anthropic. ## Market context Anthropic has seen a sevenfold increase in large accounts (over $100,000 run-rate revenue) in the past year. The company competes with OpenAI in the enterprise AI space, emphasising constitutional AI for regulated sectors like banking and government. The company registered subsidiary Anthropic Australia in Sydney via Baker McKenzie, appointing directors including Jeffrey Bleich, former US Ambassador and General Counsel. The move addresses data sovereignty needs for local enterprise clients. Globally, Anthropic plans to triple its workforce and recently announced a $50 billion investment in US AI infrastructure. The company expanded its ecosystem with 10 enterprise partnerships including Salesforce and Google Workspace via the Claude Cowork platform. ## Sales tooling angle For sales teams, Claude integrations now include HubSpot connectors and CRM tools via the Model Context Protocol (MCP) server framework. Enterprise AI adoption in regulated sectors creates demand for sales roles focused on financial services, healthcare, and government verticals where constitutional AI approaches matter for compliance. No public data yet on specific ANZ headcount, sales leadership appointments, or comp structures for the Sydney office.

SmartCompany
2 months ago
News

Flexischools CEO moved customer service next to engineering, changed product roadmap

## Customer service sits next to product now Flexischools CEO Rachel Debeck made a real estate decision that changed how product gets built. The company ran two Sydney offices: customer teams in one location, product and engineering 15km away. That distance meant customer feedback rarely reached the people who could act on it. Workarounds became permanent. Feature requests died in ticketing systems. Debeck brought the teams together. Same floor, same lunch breaks, same conversations. ## What changed Feedback loops that took weeks now happen in real time. A support ticket becomes a product conversation. A workaround becomes a sprint item. Engineers hear directly what breaks, what confuses, what customers stopped asking for. Flexischools serves 1 million parents across Australia. When your product shows up in that many daily routines, you cannot afford slow feedback loops. Debeck got an email from a doctor after mentioning Flexischools during an appointment. The email: detailed product observations and suggestions. The actual medical notes: one paragraph. When users care enough to invest that time, the feedback matters. ## Why sales teams should care This is not just a product story. It is a go-to-market structure question. Most B2B orgs keep customer-facing teams separate from product. Support sits in one place, success in another, sales somewhere else, product in their own building. Each team hears different parts of the customer experience. None of them talk. Result: sales sells features customers do not want, support handles problems product does not know exist, success renews accounts product is about to break. Proximity fixes this. Not Slack channels or Monday stand-ups. Physical proximity. Overhearing conversations. Lunch. If your AEs are closing deals on a roadmap your support team knows will not work, you have a structure problem, not a communication problem. Flexischools put the people who hear customer pain next to the people who can fix it. Revenue impact: not disclosed. Cultural impact: clear. Worth asking: where does your customer service team sit? And who is listening to them?

Startup Daily
2 months ago
News

Mary Technology raises $7M, opens SF office, no sales team disclosed

## Mary Technology raises $7M, opens SF office, no sales team disclosed Sydney legal tech startup Mary Technology closed $7 million from OIF Ventures. The round brings total funding to roughly $9.3 million AUD since the 2023 launch. The money funds a San Francisco office and a self-serve product aimed at small law firms. The company's Fact Management System converts litigation documents into structured, searchable chronologies. It targets the manual work lawyers do across spreadsheets and document management systems. Mary serves 100+ Australian legal teams including A&O Shearman, Shine Lawyers, and Maurice Blackburn. The company reports 2,000 lawyers using the platform globally. No revenue figures disclosed. ### Sales structure unclear Mary has not disclosed sales team size, recent AE or SDR hires, or whether it has a CRO or VP Sales. The founding team includes a Chief Growth Officer, which typically signals early-stage sales and marketing operations rolled into one role. For legal tech sales professionals: this market typically involves longer enterprise sales cycles, relationship-heavy deals, and comp structures tied to annual contract value. Most legal practice management software companies run lean sales teams early, prioritising product-led growth before building out traditional SDR and AE pipelines. The self-serve product launch suggests Mary is testing a lower-touch motion for SMB firms while maintaining enterprise sales for larger accounts. That usually means different comp plans: enterprise AEs on longer ramp periods with higher ACVs, and inside sales or customer success handling self-serve conversions. ### ANZ legal tech context Mary differentiates from traditional document management systems by focusing specifically on fact extraction for litigation. The company won Best New Legal Startup at Legal Innovation & Tech Fest. OIF Ventures partner Oliver Darwin backed the round, citing "fact chaos" as an unsolved problem in litigation workflows. The US expansion puts Mary in competition with more established legal case management software providers, though specific competitors are not named in the announcement. For sales professionals tracking legal tech: this is a niche within legal software, separate from broader practice management platforms. Quota and attainment data would help assess the opportunity, but that information is not public.

SmartCompany
2 months ago
News

AI replacing reps who never did the work: response times, follow-up, selling internally

## AI Replacing Reps Who Never Did The Work Jason Lemkin published a take on which sales reps AI will actually replace. Not the great ones. The ones who were never really doing the job. The trigger: a buyer trying to spend $100k-$250k on software. Could not get an AE on the phone for a week. Followed up four times on emails. No reply. The bar is low enough that AI starts looking like an upgrade. ## What Great Reps Actually Do Lemkin breaks down the work: - **Own scheduling.** Not "let me know what works," but sending the calendar link, coordinating five stakeholders, handling timezone math, rescheduling when the CFO gets pulled into a board meeting. - **Run real demos.** Not generic product tours. Customised flows with the prospect's use case and data. - **Know the product, industry, and competition cold.** So they can tell you exactly when and where they win. - **Solve actual problems.** When a technical snag hits, they do not just loop in an SE and disappear. They quarterback it until it is fixed. - **Sell the room you are not in.** They give your champion the deck, the ROI calculator, the one-pager, the talk track. They arm your internal buyer to sell when you are not on the Zoom. - **Follow up relentlessly.** Not "just checking in" emails. Real follow-up with the case study you asked about, the comparison you wanted, the answer to the question from three calls ago. ## What This Means For Sales Jobs AI sales agents already handle scheduling, follow-up sequences, and basic product questions. They do not ghost buyers. They do not wait a week to book a meeting. They do not forget to send the deck. The reps at risk are not the ones closing enterprise deals with custom ROI models and internal champions. The reps at risk are the ones who were already underperforming the basics: response time, follow-through, and making it easy to buy. Lemkin's point: AI is not coming for your job if you actually do the work. But if your version of sales is waiting for inbound leads and forgetting to reply to emails, the cost comparison between you and an AI agent starts looking bad. ## ANZ Context ANZ markets already run leaner sales teams than US counterparts. When a Series B company in Sydney hires eight AEs, that is a big expansion. When AI can handle 60% of the SDR workflow, companies will hire four SDRs instead of eight. The reps who survive will be the ones doing the work AI cannot: building relationships, handling complex objections, and selling internally when they are not in the room. The comp implications: if half the team was never really doing the work, quota gets redistributed to fewer reps who can actually close. OTE stays the same or goes up for top performers. Headcount goes down. That is already happening in ANZ tech. Real talk: if a buyer with a $100k budget cannot get you on the phone, you are already competing with AI. And losing.

SaaStr
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