Jordan Olivero

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Jordan Olivero

Jordan Olivero

@JordanOlivero1

Ai-powered GTM Strategist | 20+ years in customer success, business development, and operations strategy. | DJing, $BTC, skiing, and Jesus.

Denver, CO Katılım Temmuz 2013
2.4K Takip Edilen1.8K Takipçiler
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Patrick OShaughnessy
Patrick OShaughnessy@patrick_oshag·
A list of surprising and mind-boggling stats from this conversation: - NDR is over 500% on an annualized basis - Anthropic's first dollar of revenue came in March of 2023 - Over 90% of code inside Anthropic is written by Claude Code - The head of tax is the heaviest token user on the finance team - Run rate revenue went from $9B to $30B in one quarter (reportedly, on pace for $50B by end of next month) - Cowork is growing faster than Claude Code did at the same point in its life - Signed two double-digit million-dollar commits in a 20-minute Uber ride to the podcast - Krishna spends 30-40% of his time on compute decisions - 9 of the Fortune 10 are customers - $100B combined commitment of compute - 30 different product and feature releases in January alone - A prior model found 22 security vulnerabilities in an open source codebase; Mythos found 250 - The finance team has built a library of over 70 Claude skills specific to finance - Anthropic raised $75 billion since Krishna joined two years ago - The day of the Series E first close was the day the DeepSeek news broke - When Meta and others made huge poaching offers, Anthropic lost two people on the technical side while other labs lost dozens - All seven co-founders are still at the company - The company is just over five years old
Patrick OShaughnessy@patrick_oshag

Krishna Rao is the CFO of Anthropic, and this is his first podcast appearance. He joined the company two years ago when run-rate revenue was about $250M. Today it is $30B. He has helped raise ~$75B and is responsible for the procurement and allocation of compute. I feel lucky we get to hear what it is like to sit inside a company this consequential at a moment this pivotal. We discuss: - The cone of uncertainty - How he allocates compute across Trainium, TPUs, and GPUs - What investors misunderstand about model companies - Why the returns to frontier intelligence keep rising - Platform vs application and where Anthropic builds its own products - How Anthropic uses Claude internally I have asked my closing question about the kindest thing more than 500 times. Krishna's answer is one I have never heard before. Enjoy! Timestamps: 0:00 Intro 2:38 The Compute Canvas 6:51 The "Cone of Uncertainty" 11:58 Why the Returns to Frontier Intelligence Are So High 16:45 Recursive Self-Improvement 20:20 Scaling Laws 23:30 Sourcing $100 Billion in Compute 28:05 Platform vs. Application Strategy 32:52 Pricing Dynamics 38:48 How Anthropic’s Finance Team Uses Claude 43:24 Raising Capital & Overcoming Investor Skepticism 52:32 Public Perception, Risks, and Government Regulation 57:25 Mythos Release 1:12:33 What Could Derail the AI Revolution? 1:13:47 Biotech and Healthcare 1:15:31 The Kindest Thing

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Dante
Dante@DanteTheDon·
Was not ready for Eric Church to deliver the best commencement speech I’ve ever heard. Six guitar strings. Six pillars of a life. Faith. Family. Spouse. Ambition. Community. You. Tune them when you’re whole, not just when you’re broken. Watch the whole thing.
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Alex Lieberman
Alex Lieberman@businessbarista·
AI transformation is just a cooler way of saying data engineering, process mapping, change management and intelligent automation.
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Ryan Carson
Ryan Carson@ryancarson·
GitLab announced a layoff today. Please take this seriously. There will be many, many more. Your assignment is clear: Get skilled with agents and practice shipping to prod. It doesn't matter if you're HR, eng, infra, customer success, admin, ops, sales, whatever. As a Founder/CEO, I can tell you that I won't be hiring any employees who aren't really skilled with agents and able to ship to prod. I'm not alone in this. There is no 'engineering' org in the future.
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Luke Pierce
Luke Pierce@lukepierceops·
Yesterday Anthropic and OpenAI both launched consulting arms. I made a post about this on March 20th, and they just officially launched yesterday. Anthropic teamed up with Blackstone, Goldman, and Hellman & Friedman to launch a $1.5B firm that embeds engineers inside PE-owned mid-market companies to redesign workflows and integrate Claude into core operations. OpenAI launched The Development Company. $4B raised at a $10B valuation. Partnered with TPG, Bain, Brookfield, and Advent. Pretty much this same model. Forward-deployed engineers sitting inside mid-sized businesses, mapping operations, building the systems. Goldman's global head of asset management: "There's a big shortage of people who know how to apply these tools into businesses and then transform them." This is exactly it, and I've been talking about this for months because I have been living it. Stop overthinking how to make money with AI. It's consulting and implementation. It's the exact thing the labs just bet billions on. Every company doing $2M to $50M a year has the same problem. They have access to the best AI ever built and zero clue what to do with it. Their team is drowning... their ops are held together with duct tape. Their data lives in 7 places. They don't need another Ai SaaS tool. They need someone who can walk in, map the operation, find the bottlenecks, and build the system that actually moves the needle. This is the highest leverage skill you can build in 2026. IMPLEMENTATION. Real systems inside real businesses with real ROI attached. The window is open right now and it won't be open forever. Once the labs scale their consulting arms and the Big 4 fully pivot, the easy money on the operator side gets harder. If you've been waiting for a sign that this is the path, this is it. Anthropic and OpenAI just told you where the money is. Don't overthink it. Go build.
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Jordan Olivero
Jordan Olivero@JordanOlivero1·
@jondalgir @mcuban Agreed! @trydatabook is all of the above - focused on a domain-specific environment: constrained workflows, clear rules, known data, validation layers, and human judgment where it matters.
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Jon Dalgir
Jon Dalgir@jondalgir·
Stronger AI systems may not come from one model knowing everything. They may come from smaller domain-specific environments: constrained workflows, clear rules, known data, validation layers, and human judgment where it matters. That is probably the foundation for more reliable enterprise AI.
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Mark Cuban
Mark Cuban@mcuban·
I’m coming to the conclusion that the biggest challenge for Enterprise AI, and AI in general , as of now, is that it’s still impossible to make sure that everyone gets the same answer to the same question, every time. Which is a great response to the doomers. AI doesn’t know the consequences of its output. Judgement and the ability to challenge AI output is becoming increasingly necessary, and valuable. Which makes domain knowledge more valuable by the second. Am I wrong ?
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Collin Rugg
Collin Rugg@CollinRugg·
NEW: Astronaut Reid Wiseman shares a video of ‘Earthset’ that was taken with his iPhone “This is uncropped, uncut with 8x zoom which is quite comparable to the view of the human eye…” Wiseman said. This has to be the greatest iPhone video of all time.
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Luke Pierce
Luke Pierce@lukepierceops·
Late 2024: AI agencies sold n8n workflows for $2-$5K. Mid 2025: they pivoted to AI agents for $5-15K. Today: Claude Code ships in hours what used to take weeks, and most agencies are still pitching 2024's playbook. I spent 2 months rewriting mine for where we actually are in April 2026. Inside: → The offer closing $25K-$60K projects right now → Top 5 industries worth selling to this quarter → Content schedule generating my inbound (exact post types + cadence) → LinkedIn + cold email sequences booking calls today → My 4-call sales process from first touch to signed → The strategy doc + proposal template I'm using to close → 3 live client builds my team is shipping this quarter BONUS: First 100 people also get 2 discovery call recordings from my own sales process. Like + RT + reply PLAYBOOK and I'll DM you the link. Make sure to follow me so I can DM you.
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Fixing Education
Fixing Education@FixingEducation·
A simple visual for kids (and adults) to understand delayed gratification.
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Guillermo Flor
Guillermo Flor@guilleflorvs·
Sequoia's thesis that the next $1T company will sell work, not software, is the most important reframe in AI right now. The argument: if you sell a copilot, you're competing with every new model release. But if you sell the outcome — books closed, contracts reviewed, claims handled — every AI improvement makes your margins better, not your product obsolete. The key insight most people miss: for every $1 spent on software, ~$6 is spent on services. The entire SaaS playbook was about capturing the software dollar. The AI playbook is about capturing the services dollar — at software margins. Not "AI for accountants." The AI accounting firm. Not "AI for lawyers." The AI law firm. The companies that figure this out won't look like SaaS companies. They'll look like services firms rebuilt on software infrastructure. That's a fundamentally different company to build, fund, and scale. And most founders are still building copilots.
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Chris Bakke
Chris Bakke@ChrisJBakke·
Running a company: 2020: can you survive a pandemic? 2021: still here? we’re going to give all of your competitors $100m series A rounds. 2022: wow, you made it? okay, all engineers cost $600,000/year now. 2023: nice job! okay, SVB failed and we’re going to take away your bank account. 2024: a survivor I see. but can you pivot from ai to crypto to defense tech back to ai-enabled defense tech in a 12 month period to stay relevant? 2025: unfortunately all of your competitors have raised $2b series B rounds. oh and only 500 engineers are relevant and they cost $100m/yr each. 2026: well, well, well. you’re still in business? let’s deploy the thunderclap of godlike LLMs from the heavens so all of your customers can rebuild your app in 2 hours. can you survive?
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Claude
Claude@claudeai·
Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform.
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Luke Pierce
Luke Pierce@lukepierceops·
I’ve already been doing this for 4+ years with 80+ clients. Now I’m showing others how to do the same. 23 people already in. DM me “INNER CIRCLE” if you want in.
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Luke Pierce
Luke Pierce@lukepierceops·
Grant Cardone nails the opportunity here. Every single company needs AI implementation. The TAM is just too massive to ignore. I’d push back a bit with leading with a “consulting fee”. Unless you have extreme authority in that vertical, you’re leaving money on the table. Consulting fees and retainers should be sold on the back end. Lead with audit and discovery. Show them what’s broken, what’s possible, and what the ROI looks like. Then price by impact. You’ll make way more money. And you’ll actually retain clients because they see results before they see invoices. Either way, the core message is right. If you’re going to start something right now, start here. Every company needs an AI team (you) and almost none of them have one yet.
The Iced Coffee Hour@TheICHpodcast

Grant Cardone reveals how to make $80,000/month with AI consulting👀 “I’d make $1,000,000 in year 1”

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Luke Pierce
Luke Pierce@lukepierceops·
I mapped every AI automation opportunity across 25 industries. 10-15 pain points each. With the exact positioning, pricing range, and who to sell to. This took me 4 years and 80+ client engagements to figure out. A lot of AI agencies pick a niche and pray. They don't know the actual pain points. They don't know who the buyer is. They don't know what these companies are already paying for broken solutions. They don't know what the realistic project size is. So they end up competing on price for generic "AI automation" gigs. I've worked with marketing agencies, recruiting firms, e-commerce brands, law firms, real estate companies, healthcare practices, financial services, SaaS companies, manufacturing, construction, logistics, and more. Every single one has 10-15 processes that are bleeding money because they're still done manually. Here's what the guide covers for each industry: → The top 10-15 automation pain points (ranked by ROI) → Who the actual buyer is (CEO, COO, ops manager, etc.) → What they're currently paying for manual labor or broken SaaS → Realistic project pricing ($5K-$60K+ depending on scope) → The discovery questions that unlock the deal → How to position yourself as the expert even if you've never worked in that industry → Red flags to avoid (industries and company sizes that aren't worth it) 25 industries and 300+ specific automation opportunities. This is the cheat code for picking your niche and knowing exactly what to sell before you ever get on a call. Like + RT + reply "NICHE" and I'll send you the full guide (Must be following so I can DM)
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Jordan Ross
Jordan Ross@jordan_ross_8F·
The founders who figure out OpenClaw in the next 90 days are going to look like geniuses in 2027. The problem is most agency owners don't have time to figure out the install, the security risks, where to start, or what to actually hand it first. So my team built a 48-page beginner's guide that does it for you. Inside: — The exact prompts to hand it on day one — Plain English setup for Mac and Windows — How to secure it so it doesn't burn your business down — 42 copy-paste workflows across sales, marketing, ops, and finance Your competitors are sleeping on this. Comment OPENCLAW and I'll send it.
The Startup Ideas Podcast (SIP) 🧃@startupideaspod

"OpenClaw is the new computer." — Jensen Huang This is the early PC era all over again. A few power users see it. Everyone else hasn't even started. "It's the most popular open source project in the history of humanity, and it did so in just a few weeks. It exceeded what Linux did in 30 years." A solo founder with OpenClaw can now build what used to take a 50-person team. The leverage is absurd.

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