LTX Studio now supports SDR to HDR. AI video has looked good right up until post. Then the grade starts, highlights clip, shadows lose detail, and the shot loses the range finishing teams need. No need to start over. Upgrade your existing videos from SDR to HDR right inside LTX Studio.

DEMO: @Siemens just showed what happens when agents handle partner onboarding end to end @tryqualified’s AI SDR Piper turns website interest into qualified pipeline. Then Agentforce Supply Chain keeps onboarding moving: → Coordinates work across agents + people → Brings in humans when judgment is needed → Learns the backend process → Turns what it learns into trusted actions → Executes the final steps hands-free in @SAP The key: the agent doesn’t improvise. It follows the same trusted process every time. Weeks of onboarding gets done in days

Snowflake on the ROI they're getting from running their own AI products internally, per their earnings call: - Marketing: SEO brought in-house, $400K of annual agency spend eliminated - Finance: long-range planning went from a 3-person team to 1 analyst - Sales: prospecting automated across 100K+ leads, with 70% of initial outreach emails to inbound leads generated before an SDR is involved

One thing that was “supposed” to happen was AI would turn AEs into Super Reps. It should have happened: - AI can do all the meeting prep for you - AI can do the follow-up - AI SDRs for outcome do work, trained right - AI BDRs for inbound can qualified leads better than almost any human - AI can extract all key meeting details, add to you CRM automatically, etc. - AI training tools can help train you on how to sell and how your app really works - AI SE and knowledge tools can help you with tougher technical and product questions All that came to be Yet, I have few-to-none AE Super Reps calls or demos. Basically, humans have put about the same energy into sales as before. The best still crush it, and often crush it even more. The main benefit seems to be somewhat smaller sales teams. But even there, the data is conflated by AI-native leaders with extremely strong market demand, running leaner teams. And the classic email based SDR may no longer be necessary. But IMHO experience at least, AEs haven’t really gotten much more … Super.

These five Grok Bots are doing the jobs a startup used to hire a whole team for 👇 ⤷ 1 Jess - your executive assistant link: recaps your email, calendar, notion and slack before you've opened any of them. you walk in already briefed instead of digging. ⤷ 2 Marketing Bot - your CMO link: give it the product once and it turns out the copy, the emails, the offers and the conversion ideas - nstead of you answering one marketing question at a time. ⤷ 3 Prospecting Sheet Builder - your SDR link: wakes you to a fresh sheet of qualified b2b accounts every morning. ⤷ 4 Reaper - your ops hawk link: audits the subscriptions, meetings, and processes that no longer earn their place, then makes the case to kill each one. the best cost-cutter you'll have. ⤷ 5 Human Copywriter - your copywriter link: rewrites the ai-sounding draft into something that reads like a person wrote it. what you ship stops getting marked as slop. no salaries. no onboarding. no notice period. 250+ more →

We’re hiring @RivetTax If you’re new here: Rivet is the AI-native accounting firm (tax, bookkeeping, and a third new product in stealth) We’ve built a T500 firm in 2.5 years, working with Cursor, Physical Intelligence, and 1000+ clients As we head into Q4, we’re expanding our GTM & sales teams: - AE roles (4), full cycle - SDR roles (4), sourcing - GTM / growth, help build our outbound engine Shoot me a DM 🤙

corgi (yc s24) is running one of the strangest b2b gtm systems... everyone keeps talking about the "corgi girls" but there are so many other things that make the GTM pumping alongside a very aggressive outbound sales floor, it built something you almost never see in b2b: a 24/7 physical coffee shop underneath its san francisco headquarters here is why their gtm is worth studying: 1. physical distribution commercial insurance is usually bought when something forces the purchase: a funding round, an enterprise contract, a landlord, or a board requirement. corgi created another touchpoint before that buying moment arrives. downstairs from its headquarters is corgi cafe, open 24/7 and packed with founders, engineers, and investors. the cafe itself loses money -- but corgi's ceo stated the value comes from community building, recruiting customers, and recruiting talent. the cafe does not replace outbound sales, it places corgi physically inside the community it wants to insure. 2. full-stack carrier corgi is not an insurance broker, it underwrites and issues policies directly through its own software. startups buy d&o, cyber, and tech e&o coverage directly. corgi states quotes can arrive in under 10 minutes and policies can bind the same day, compared to the traditional broker process which it estimates at 2 to 4 weeks. 3. sponsored attention the cafe has another distribution layer: brands can pay to "own a drink" on the menu (brex, elevenlabs, qodo), and startups host community events inside the space. other companies have an incentive to bring their own audiences into a space owned by corgi. this creates recurring third-party reasons for their target buyers to enter their physical footprint. 4. outbound never disappeared corgi did not replace cold outbound with coffee. its startup-insurance bdr role in austin requires 100+ outbound calls per day!! its sdr role in san francisco expects 100+ dials and 30+ emails per day!! outbound generates direct pipeline. the cafe and girls generates community, brand presence, and ambient demand. but what most of you don't see is the amazing outbound system behind it (I'm writing another article on it later) the takeaway: - software infrastructure underneath. - aggressive outbound on one side. - unique distribution on the other (physical community for Corgi) keep pumping, Namanyay

The Latest CRO Confidential with @samdblond s out! "$0 to $600M in Under 4 Years. The ElevenLabs GTM Playbook" Carles Reina was the fourth employee and first GTM hire at ElevenLabs. Four years later: $600M+ ARR and an $11B valuation. Today he's a Partner at Baobab Ventures, helping the next generation of startups scale. In the latest CRO Confidential, hosted by Sam Blond (CEO and Co-Founder of Monaco), Carles breaks down exactly how they built it. The revenue ramp alone is jaw-dropping: $0 to $100M ARR in 20 months $100M to $200M in another 10 months $200M to $330M in another 5 months $330M to $600M+ in another 6 months #1#. Distribution from day one ElevenLabs never bet on a single channel. Every market got its own thesis: why launch here, direct or reseller, what does GTM look like in the first 90 days? Core markets were sold direct; everywhere else, resellers got the technology into market fast. Distribution plus relentless experimentation was the whole game. #2#. The grants program that killed the competition Carles came back from holiday with one question: how do I kill my competition? The answer: give startups with 25 or fewer employees free credits for 3 months. They gave out tens of thousands of grants, made a ton of noise about it, and pulled demand away from every competitor in the market. Over 10% of ElevenLabs' enterprise revenue eventually came from upselling those grant recipients as they grew. #3#. The 20X quota model This one's controversial. At ElevenLabs, a $100k base salary meant a $2M quota — 20x — with uncapped commissions. Reps regularly hit 300-600% of target, and average attainment across the entire GTM org ran 167% every single quarter. Carles' theory: he'd rather have a lean team of killers crushing quota than a bloated team missing it and complaining. And when a market genuinely broke, they'd grant quota relief — the commitment was that comp would always stay fair. #4#. Only pay commissions on recurring revenue Sign a giant one-off or a 2-month POC? Zero commission — because it added nothing to valuation. Roughly $1M of real recurring revenue was worth ~$33M in valuation, and reps were paid generously on that. Incentivize the outcome that matters, not the inputs. #5#. What he'd do differently: enablement and senior sellers, earlier Two regrets. First, sales enablement came too late — it's always underinvested until the team is too big to onboard properly. Second, hire senior sellers earlier. Hungry young reps are great, but people with 20 years of relationships who know procurement compress timelines dramatically. Either regret fixed might have gotten them to a billion faster. #6#. Wire AI into GTM, but sell the team on productivity, not replacement Carles pitched the founders on building an AI SDR, an AI AE, and an AI customer success manager. The team's first reaction: "am I going to get replaced?" They proved the opposite. The AI SDR answered inbound emails in minutes and improved conversion rates. The AI CSM worked long-tail and mid-market upsells automatically — and the human account owners still got paid the commission. Productivity story, not replacement story. #7#. Test 100 things. You only need one to work Carles told the team he only needed one experiment to hit to add another $100M in ARR. So run everything in parallel, kill what fails fast, and treat a failed experiment as a win — you learned something. Add to that: make it insanely easy to do business with you, from pricing to onboarding to time-to-value. 👉 Distribution is everything, incentivize recurring revenue (not meetings), be famously fair to your salespeople — reps who get rich make everyone around them believe — and let AI do the work agents are best at while humans do the relationships and the creative bets no agent would ever come up with. Watch the full episode:

Went on one of the biggest AI channels in the world to reveal two things: 1) How we're killing consulting. It needed to be taken out back, and rebuilt from the ashes. 2) The MetaHarness - an AI-native workflow + SDLC we used to 10x productivity Def watch the full video (@DavidOndrej1/@dan_zakon crushed it), but here are the highlights: 1) How to make a slow company AI-native - Meet the company where it actually is: systems, people, and where they sit on the AI journey. - A 2026 consulting business for Fortune 2000s has to be a full-stack partner, not a deck shop. - Old model: consultants take a problem, sit on it for months, deliver a 150-page deck, say peace, leave the client to implement. - Post-AI: you have to touch the whole chain. Strategy, change management around the tech, and forward-deploying into the company. - For a lot of Fortune 500s, the first move is teaching the C-suite Claude CoWork/Claude Code or Codex. - The job is to drive friction as close to zero as possible. 2) How we're killing & reviving consulting - Me and @ArmanHezarkhani did not dream of being consultants. - The vision: modern-day Bell Labs - My bar: if I'm not home with my wife and daughter, the work has to be worth it. Worth it = frontier problems with the smartest, most driven people you have worked with. - Arman read The Innovators in school (Babbage and Ada Lovelace through mobile revolution). The book’s lesson on innovation centers: Bell Labs / the transistor, DARPA / the internet, Xerox PARC. The raw materials are always brilliant people given autonomy, plus direction, distribution, and resources. - We did not want to raise hundreds of millions on day one. Helping the biggest companies solve their hardest AI problems cashflows the business, tells us what actually hurts, and supplies the materials for an applied AI lab that incubates consequential post-AI tech. - Consulting is a dirty word. It's growth & innovation as a service. - Classic consulting pitch: go in, problem, the answer is often firing people, deck, walk away, never see the fruit. That is why great technical talent never went there. - Reposition: messy, important problems, no answer, an A-team for innovation and implementation, actual ROI. - Consulting by-design gives engineers diversity of work without job-hopping. “ADHD is satisfied, same company.” 3) Singleplayer vs. multiplayer AI - Single-player: swap Google for GPT/Claude on the same query. Zero behavioral change. Still valuable. - Enterprise version of that: sign a $5–10M token agreement with OpenAI or Anthropic, get secure Codex/GPT or Claude Code/Co-work, put SOTA models in front of people, teach daily use. Most companies should start here. No compounding. - Multiplayer: reinvent a horizontal process so leverage hits a whole function or the whole company. - Examples: an SDR/sales agent that gives sellers back time from logistics so they talk to clients. Data engineering when an “AI problem” is actually a data-readiness problem. - Exponential, messy, painful value is multiplayer. 4) Breaking down the MetaHarness - Engineers have always been methodical about files. Knowledge workers are playing catch-up. Tenex is taking it further: extremely prescriptive context for coding agents. - Old SDLC: human-to-human coordination via Agile ceremonies (standups, retros, a calendar full of meetings). - New SDLC: humans, other humans, and agents. Agents are the main character doing the work on the ground. - Written-first culture matters more than ever. Markdown briefs humans and agents. - Agents can hold more coherent information than humans, so you can write bigger blueprints further in advance than Agile ceremonies ever allowed. - Concrete markdown artifact types on every project. Agents pull the right context at the right moment and keep it in sync. Dan calls it a “software machine.” - The problem the harness is solving: agents give speed that did not exist. Longer tasks increase entropy and diversion from plan. Minimize entropy without giving up speed.