Nav Rao

I was going through a report from Menlo Ventures released on Thursday. Anthropic just overtook OpenAI in enterprise usage: 32% vs 25% market share. That's a complete reversal from 2023 when OpenAI dominated with 50%. Anthropic jumped from $1B to $4B in revenue in just 6 months, while targeting a smaller, higher-value enterprise segment. Both companies' flagship models are essentially at parity in 2025. It's about Claude's focus on logic, structure, and regulatory compliance, priorities that enterprise buyers actually pay premium for. AI spending is exploding 44% year-over-year, hitting $371B in 2025. And, nearly half of all AI spending is shifting from training to inference by 2032. While everyone's obsessing over model benchmarks, you should push your team to focus on: ➤ Code generation (already a $1.9B market) ➤ Agent-first architectures ➤ Production inference optimization ➤ Enterprise tool integration Model costs dropped 280x from 2022 to 2024, but enterprise spending keeps growing. Why? Because enterprises pay for outcomes, not tokens. They want AI that integrates with their systems, understands their data, and solves their specific problems. What I can say from all of this: stop chasing the latest model release. Start building AI infrastructure that enterprises actually want to buy. The companies winning aren't the ones with the best models. They're the ones solving real business problems at scale. #AI #Consulting #Strategy #Startups

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The most expensive phrase in a PE-backed CTO’s playbook? “Let’s just build it ourselves.” At first glance, building in-house feels smart. More control. Maybe cheaper. But in Private Equity, time to market is everything. Every extra sprint spent coding = value left on the table. Building in house masks hidden costs: - Months of delayed revenue - Engineering focus pulled from value-add projects - Ongoing maintenance & support - Integration headaches - Mounting technical debt Meanwhile, proven solutions are: - Battle-tested at scale - Updated continuously - Backed by strong communities - Predictable in cost & performance The best PE-backed CTOs know the rule of thumb: 👉 Buy for speed, build for differentiation. If it doesn’t create unique competitive edge, don’t reinvent it. Because in PE, growth isn’t measured in lines of code. It’s measured in market share, EBITDA, and exit multiples. Build what makes you special. Buy everything else. Agree? Disagree? What did I miss?

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Add 100k installs for productivity apps in less than 90 days through 4 steps: 1. Master the Efficiency Transformation Hook Productivity apps die because they focus on features instead of transformational outcomes Here’s some winning hooks for productivity apps that focus on outcomes > Time-Saving: "Wdym there’s an app for this??? > Organization: "Um guys, if you’re [doing this], just listen to me..." > Specific Use: "One [relatable situation] and I just found this???" 2. Create These 4 Content Types Only Stop making random content when you can just use these proven formats > Quick Workflow Demos (15-30 seconds showing specific productivity hacks) > Before/After Organization (visual chaos to organized transformation) > Day-in-the-Life (real users integrating app into daily routines) > Pain Point Solutions (specific productivity problems solved) 3. Deploy Creator Teams Specifically Recruit 4-6 micro-creators targeting specific avatars > students > entrepreneurs > busy parents > remote workers Test different hooks with each creator to identify winning angles for each demographic. 4. Use Psychology-Driven CTAs Generic CTAs kill conversions. Focus on using CTAs that are specific to your audience: > "Name of the app is XYZ btw" > "I found him on XYZ app btw" > "The app that saved my life was ABC in case you wanted to know" > Focus on dream outcomes, not features Instead of showcasing ALL features, Demonstrate ONE specific use case that solved your dream customer's time, efficiency, and productivity problems.

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It’s a bit surprising to see people in my feed this morning say OpenAI’s AgentKit “kills” a bunch of construction tech startups. It's incomplete and doesn't capture the whole picture. Yes, generic, horizontal-wrapper startups that just sit on top of foundation models are in trouble. But industry AI agents built for very specific, complex workflows, who understand and care about their customers are going to thrive as AI adoption accelerates. I played with Agent Kit yesterday for a simple task and here’s my take: 1. Agent kits are impressive but shallow without depth. To make them truly useful, you still need a quasi-technical team to build, monitor, and maintain them. In industries like construction, where margins are razor-thin and overhead has to stay lean, that’s a tough sell. Most GCs and subcontractors aren’t about to staff up a new AI department while trying to keep projects on budget. 2. Vertical Industries are highly complex and for good reason.  In construction for example: Permitting, liability coverage, inspections, safety, pay schedule and regulatory compliance all add layers of checks and balances. These workflows exist to protect businesses and keep projects safe and sustainably capitalized. You can make AgentKit work for you, you're just going to need people to do it.  And every construction company I have ever worked with wants to hire more builders and field professionals, experts in their domain, not more people in the back office. 3. Deep, domain specific challenges are where the real value is. The vertical winners won’t just bolt agents onto a foundation model, they’ll build depth with industry specific workflows. Nobody wants vendor lock-in, so they’ll be model agnostic, swapping in and out whichever LLM performs best for the task. They’ll integrate seamlessly with legacy players and every other tool the industry already depends on. They’ll own the ETL process, cleaning and structuring messy, fragmented data. They’ll capture proprietary workflows and the tribal knowledge unique to the vertical. And they’ll deliver enterprise grade privacy and transparency. 4. The organizational behaviors matter. In construction and many other verticals, middle managers often optimize for their resume and their next job while leadership optimizes for profit margins and retention. Sometimes it can be in direct tension of each other, one group wants experimental automation engineers, the other wants tools to drive productivity without new headcount. For example, software in general doesn't require additional health insurance. So yes, AgentKit and whatever comes next will eat the easy stuff like document search or report templates. But the real, hard, high value work will always go to the companies who know their vertical inside out and build for it.  Foundational models are democratizing access. Vertical AI is where the winners get created.  We’ve seen this in past technological shifts. 

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𝗢𝗽𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝘁𝗵𝗿𝗲𝘄 𝗮 𝗯𝗼𝗺𝗯 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘄𝗼𝗿𝗹𝗱 Yesterday, at DevDay 2025, they unveiled AgentKit. A visual builder for workflows and agents. Think Zapier. Think n8n. Think Lindy. Now imagine all that... plugged straight into OpenAI’s ecosystem. Will it kill the incumbents? Not yet. Zapier’s had years to perfect integrations and build community trust. n8n has an open source cult following. And Lindy is still finding its footing. But .... OpenAI doesn’t need to win the ecosystem war right away. They’ve got something far more dangerous: 📈 Distribution. 💰 Deep, very deep pockets. n8n just raised at a $1.5B valuation. OpenAI burns through that… every few weeks. OpenAI also has 800M users. That scale difference? It matters. This move isn’t about dominance today: it’s about owning the workflow layer of the AI economy tomorrow. Who wins short term? 👉 You. The customer. Because when companies fight, prices fall, and innovation soars. #openai #n8n #lindy #zapier ---- 👉 Follow me, Robin Jose for daily insights on AI, leadership, and technology. ❤️ Like and share if you found this valuable!

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