Aram Shatakhtsyan

The big labs are expansive in their product ambition, especially since foundation models have largely improved in lockstep - in order to compete with them you have to do things they won’t which are: - building a very rich software ecosystem around a primitive - orchestration across multiple models - going insanely deep on product and growth for a narrow vertical domain

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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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Found this pattern again last week: Startup builds MVP fast → raises seed round → tries to scale → discovers massive tech debt from vibe-coded Firebase/Supabase. The fix usually costs 2–3× more than if they’d built production-grade from day one. Question for founders: When do you usually bring in a senior engineer — before the pain hits or after?

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GTM goals for early stage technical companies: 1. Be referenced often in internal meetings of your prospects (exploratory phase of adoption) 2. Be referenced often in externally facing architecture diagrams of the top companies in your market (first path to production + PMF) 3. Be endorsed often in communities as experts/influencers see 1 + 2 appear without your promotion (your real ARR + NRR story happens here) 4. Be endorsed as the gold standard by communities (moat, hang on for dear life and keep shipping and making money) #2 is v hard as things shift often, but Founder time should be with customers to make this occur as often as possible. #3 + #4 become your roadshow focus over time so you can scale your company.

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🚀 The AI-Native Revolution: Why Re-engineering SaaS Won't Cut It in the Enterprise 5.0 Era After decades of SaaS dominance, we're witnessing a fundamental shift that's reshaping how businesses operate and compete. This isn't just another tech upgrade—it's a paradigm change from traditional software to AI-native architectures. 💡 The Dawn of Enterprise 5.0 We're entering Enterprise 5.0, where human ingenuity combines with intelligent machines to create autonomous workflows. Unlike previous eras, employees become orchestrators of intelligent workflows rather than manual processors of information. 🏗️ Architecture is Destiny The difference between AI-native and traditional SaaS isn't in surface features—it's in architectural DNA: Traditional SaaS: Static workflows, bolt-on AI features, fragmented data AI-Native:Intelligence embedded from the ground up, continuous learning, unified data flows Adding AI features to legacy SaaS is like attaching a jet engine to a horse-drawn carriage. You might go faster, but you haven't built an airplane. ❌ Why SaaS Re-engineering Fails Three critical failure modes: 1. Fragmented Data Access - AI features only see siloed data, missing the full picture 2. Broken Learning Loops - Insights don't flow across systems, limiting compound intelligence 3. Task vs Process Automation - Bolt-on solutions automate tasks, not entire workflows 🎯 The Real Challenge Building AI-native solutions faces unique hurdles: • 85% of AI projects fail due to data quality issues • Only 12% of organizations have AI-ready data • 67% of employees show low-to-moderate AI acceptance • Requires entirely different tech stacks and organizational transformation 📈 The Market is Moving VCs have invested $2+ billion in agentic AI startups in the past two years. 25% of companies using generative AI will launch agentic AI pilots in 2025, growing to 50% by 2027. 🔮 The Path Forward Success requires: ✅ Full-stack AI rearchitecture ✅ Human-centric design that amplifies capability ✅ Different economic models (higher initial investment, lower marginal costs) ✅ Continuous learning architectures The Bottom Line:Just as budgets moved from TV to digital and software migrated to cloud, this transition will be swift and decisive. Companies retrofitting AI onto legacy architectures will be outpaced by AI-native competitors built for this new era. The future belongs to those who understand that we're not just adding intelligence to existing software—we're fundamentally reimagining how software and services work. At BGV, we're backing founders building these human-centric, AI-native solutions that combine the best of human ingenuity with intelligent machines. This is Enterprise 5.0, and architecture is destiny. #AI #Enterprise #Startups #VentureCapital #AINative #Enterprise50 #BGV #FutureOfWork --- What's your take?

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San Francisco, 2013. Sid Sijbrandij paid for office space after Y Combinator insisted remote wouldn't scale. His employees came in for three days. Then stopped showing up. They were shipping faster from home. Since 2012, GitLab had been forced to write everything down. Employees distributed across Europe and US meant every decision required written reasoning others could follow. This constraint would solve enterprise software's biggest problem: how to trust a startup with your data. Today GitLab is worth $8B. Not because they built better DevOps tools. Because they built a software company that runs like software itself, inspectable and auditable. They didn't eliminate trust. They relocated it from salespeople to source code. The Trust Tax Enterprises ask one question: Can we risk our business on this? Traditional vendors answer with: → Discovery calls and executive briefings → Reference calls with other CIOs → Proof-of-concepts and pilot programs → 18-month sales cycles GitLab asked: What if customers could verify everything themselves? Radical Transparency Their open-core model meant customers could inspect the code and run it on their servers. They went further by publishing their 2,000+ page operating handbook, actual playbooks for decision-making and governance. But the real innovation was structuring everything around the merge request. The MR became their trust architecture. Every change flowed through this single point because remote work demanded it. Proof in Practice CACI consolidated 7 DevOps tools into GitLab, cutting administrative overhead by 90%. Not because GitLab promised it. Because they could trace every decision through MR history. Compliance audits that took months now take hours. The trust tax disappears. Nothing left to trust, only verify. The Uncopyable Moat GitLab's flywheel: Security-conscious enterprises adopt → Require compliance features → Contribute improvements → Become social proof → More regulated industries join → Platform strengthens You can't retrofit radical transparency. You either give customers control or you don't. The moat isn't code. It's collective investment from thousands of enterprises. The NewCo Trust Infrastructure Checklist: → Pick your transparency vector → Make commitments permanent → Build audit trails by default → Give customers their data → Eliminate gatekeepers Replace places where customers must "trust you" with places they can verify themselves. GitLab deliberately used transparency as their enterprise wedge. Your advantage is learning from their playbook. Companies that win won't have the best technology. They'll relocate trust from promises to proof. (Full case study sent to subscribers)

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AGI lab bootstrapped to $1,000,000,000 Revenue and $30 Billion valuation in 5 years Surge AI by Edwin Chen Great lesson at South Park Commons SF yesterday on focusing on building, the problem and the customer. Only now are they raising their first VC round of $1 billion. #AGI #SF

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Many YC founders go to Demo Day expecting to fill their round and get back to work. But then reality hits: Lots of them don't raise a dollar. You just don't hear about these stories. You only hear about the successes. YC partners are clear about this: "Don't think that just because you're YC, you'll lift a finger and raise your round." The badge gets you in the room. But you're still competing against 50-100 other startups for maybe 5-10 investment spots per fund. If I were to do YC again, here’s how I’d approach it: Use the $500K to create a 3-year runway and build something people want with a team of 2 in total. I won’t show up to demo day. Just focus on building. I’d forget about: Growth targets. Fundraising decks. Creating a one-liner. 100% focus on profitability. That's how you become default alive and don’t need to beg for money.

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