Mikhail Asavkin 🇦🇺

Last Thursday, Figma IPO'd at $50B with a 158% first-day pop. Dylan Field still owns majority control after 13 years. In an era where many founders treat startups as a means to an end, this feels like watching someone see their vision through - no matter how long it takes. Some background: It was September 2022. Adobe announces they're buying Figma for $20B. $20 billion. For a 10-year-old design tool. Everyone assumed it was done. 15 months later, regulators kill the deal. Figma gets a $1B breakup fee. Many companies would've played it safe with that windfall. He quadruples R&D to $751M. Ships Make, Sites, and Dev Mode. Goes all-in on product while everyone else battens down the hatches. Takes the company public at $50 billion (2.5x Adobe's offer). That's what happens when you refuse to abandon your mission. (13 years of refusing) I keep thinking about what made Field stick with it for 13 years. It's not (just) intelligence or opportunity. It's something I saw in his IPO letter: "Expect us to take big swings. We will bias toward long-term compounding over quarterly share-price appreciation." This from the guy who just delivered a $50B IPO after regulators killed his $20B exit. Look at the 5 moves Field made after the Adobe deal collapsed: 1. He ate the rejection like breakfast. Regulators kill the Adobe deal? Use their $1B to build something worth $50B. Turn a forced “no” into the best “yes” of your career. 2. He kept his team when everyone said cut. After Adobe collapsed, conventional wisdom would say downsize, especially in the hiring environment at the time. But Field avoided forced layoffs. Most stayed. This means the same core team that built a $20B company built a $50B one. 3. He treated the breakup fee like jet fuel. Many companies would've hoarded that $1B. Figma quadrupled R&D to $751M. Shipped Make, Sites, and Dev Mode while everyone thought they were damaged goods. 4. He built AI into the product roadmap. 2023-2024 was peak "pivot to AI or die" hysteria. Field shipped AI-native products (Make, Sites, Dev Mode) that actually solve design problems, not just chase buzzwords. 5. He built the company to be un-acquirable from day one. Dual-class shares. Majority voting control. The legal infrastructure to say no. When Adobe finally got through, it was because Field chose to let them. — Adobe offered Figma a dream acquisition of $20B. Regulators said "no." So he bet their $1B consolation prize on going bigger - and built something worth $50B instead. $20B in 2022. $50B in 2025. The "setback" was actually a massive gift.

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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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Lots of people made jokes when Atlassian bought The Browser Company a week ago. But I think there are some legit product strategy reasons... I'm a little late to the party on this. Tech Twitter had a field day with this news. It was highly entertaining, but mostly poked fun at the deal. Atlassian is a legendary SaaS company, so I analyzed this deal under the assumption that the people making it are smart, thoughtful, and deliberate. I think there are 5 legit possible reasons for the deal: Agents Are Existential To Atlassian's Core Business JIRA and Confluence make up the majority of the revenue. One is workflow management, the other is content management. Both are in the crosshairs of agents. There Are New "Entry Points" For AI Products ChatGPT, Claude, Cursor, etc have established new entry points for workers. They are starting their work more and more in those tools vs traditional SaaS tools. The browser is another potential entry point with no clear winner in the AI era yet. B2B Browsers May Make Sense In An AI World We mostly use a desktop browser for work. A lot of our personal consumption has moved to mobile. Existing desktop browsers were not built for work. AI may create the shift and opportunity to create this. Atlassian Needs Moonshots To Keep Growing Atlassian's historical playbook has been to build/buy and then sell adjacent products to JIRA and Confluence. That strategy may be running out of fumes. What adjacencies exist that could maintain growth when the baseline is $4B. They may need a new S-curve. They're Losing The Next Generation Of Companies First pointed out by Varadh Jain, the next generation of companies don't start with Atlassian products. They are choosing Linear over JIRA, Notion over Confluence, Slack over Hipchat (RIP). Dia and Arc are both products that had followings of the next generation. All of this is interesting to me as product strategy has shifted in the world of AI. Full Post Here -> https://lnkd.in/gknesaZB

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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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The latest AI Product Kitchen is live - with Nan Yu, Head of Product at Linear!! Linear just closed their Series C ($134M total), are taking on Jira, and power the world's fastest-growing tech teams: Cursor, OpenAI, Vercel, Notion, and Replit. We cover: - Why Linear is building AI agents as teammates - Why backlogs should be deleted - How Linear wins with fewer features -- Here's 6 of my fave spicy takeaways -- 1. Linear wins by doing less, better They don’t try to match Jira. They do the opposite - focused, opinionated, solving the right problems, beautifully. Less surface area = more velocity 2. Every product org is a series of bets You’re not shipping a roadmap - you’re placing wagers. Linear treats every decision as a hypothesis: test quickly and adjust fast. Speed > perfection 3. Backlogs are where good ideas go to die Most teams hoard tickets “just in case.” Linear isn’t afraid to delete them. If it’s not clear and actionable, it doesn’t stay in the system. Clarity > clutter 4. If your system doesn’t help future-you, it’s useless Linear designs for retrieval, not perfection. They ask: "Will future me understand this?" If not, it gets rewritten or tossed. Build for context that’s long gone 5. Good PMs ship features. Great PMs ship clarity Anyone can ship a feature. The real value is aligning people, defining the “why,” and reducing ambiguity. Clarity scales. Confusion compounds 6. Define ownership before outcomes Linear bakes ownership into the system. Without clear responsibility, even great strategies fall apart. Every initiative starts with “Who owns this?” If you care about speed, clarity and building impactful product orgs - this ep's for you. See the full ep: "Agents as Teammates: Linear's AI Vision" on YouTube or links in comments below

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We’ve been jamming with Y Combinator’s Summer 25 batch, and have been blown away by the quality and pace of the teams. What jumps out: -Infra > interface: some excellent teams are building in the execution fabric, trust rails, or evaluation loops. They’re tackling latency budgets, anti-bot arms-races and SOC-2 checklists: the plumbing that lets agents graduate from demos to enterprise workloads. -Data moats by design: the strongest vertical plays lead with proprietary claims corpora or HIPAA-grade call graphs. -Hard-tech is back: Flywheel’s tele-ops excavators and Verne's one-hour-training vision stack prove YC is funding robots again, but with a software-first margin profile. -Founders who’ve lived the pain: Ex-Cleartax PMs fixing GST reconciliation. Ex-Goldman post-trade leads building zero-trust for agents. Wet-lab scientists automating plate handling. Why we’re excited: -These teams aren’t asking whether agents will work; they’re solving the reliability, audit, and compliance gaps so agents can be boringly dependable. -Several are already in production with paying customers pre-Demo Day. -Defensibility comes from data moats, regulatory grounding and real infrastrucutre. You can see the agentic web moving from prototype to platform. Looking forward to seeing some of you tomorrow at Demo Day.

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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