Ghalib Suleiman

Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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Most lead gen tools charge you whether they work or not. That's backwards. Here's what actually lowers your risk: ❌ Don't pay for software seats  ✅ Pay for qualified meetings that show up ❌ Don't rely on automation alone  ✅ Get human QA on every message ❌ Don't risk LinkedIn account bans  ✅ Get compliance safeguards built in Tools like Dripify, CopilotAI, and Salesloft give you software. Amplify AI gives you a system: → Executive content that builds credibility  → LinkedIn cultivation that gets replies  → AI meeting generation with human oversight  → Show-up playbooks (confirmations, reminders, rebooks) All tied to performance-based fees. For founder-led B2B teams tired of paying for "activity" instead of results. Would you’ll like to learn more?

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AMI Labs, co-founded by Yann LeCun, just raised $1.03B at a $3.5B valuation. I'm personally glad to have a small contribution to this venture. The thing I love most is the narrative the team isn't afraid to say out loud: "It's not your typical applied AI startup that can release a product in three months, have revenue in six months and make $10M ARR in 12 months." The most interesting things happening in AI right now don't fit the familiar venture path. They're not announced in press releases. They're built over years, behind the scenes. This team is going after something structural: a different architecture, a different premise, betting on what LLMs simply aren't fit for. The first wave of frontier AI was scale: bigger models, more data, more compute. The next wave will be about architecture, infrastructure, and the harder question underneath it all: are we even going in the right direction? More labs and researchers are now betting we didn't build this market in the most optimal way. That's a meaningful signal. That actually intersects directly with where we're going at Mirai Tech Inc. Different bets, same direction. Congrats Yann LeCun, Alex LeBrun, and the whole AMI - Advanced Machine Intelligence Labs team. This one's huge.

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8 months to $100M ARR. That’s not a growth story. That’s a platform shift. What Mukund Jha, Madhav Jha and the Emergent team have proven is simple: software creation just crossed a structural inflection point. The distance between idea and deployed product is collapsing — to natural language, across platforms, for anyone! Becoming one of the fastest-growing startups ever couldn’t have come at a better time. With India AI Impact Summit 2026 putting the country firmly on the global AI map, this moment feels symbolic — a world-class AI company, built from India, scaling at global speed. We at Together are proud to have backed Emergent from day one. This is what the early innings of a generational company look like. 🚀 #AgenticAI #NextBillionBuilders #IndiaAI

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Gabriel Jarrosson provides a sharp analysis of #YCombinator's structural pivot, framing it as a shift from founder advocacy to capital optimization. However, the most profound challenge lies even deeper, in the organizational and cognitive limits of the model itself. The core issue isn't just the incentive misalignment between 3-month and multi-year cycles. It's the attempt to solve a complex, multi-stakeholder coordination problem with a static, "lifetime" partnership structure. This assumes you can identify all "known knowns" at the outset and lock them into a permanent configuration—a model that collapses when dealing with the dynamic, emergent reality of a true ecosystem. As the ZYRO Hive planetary-scale governance clarifies, complex systems require a protocol for dynamic, context-driven alignment, not static, temporal alignment. The goal isn't to pick lifetime partners; it's to build a coordination layer where the right contributors—be they individuals, AI, or institutions—can be algorithmically verified and sovereignly aligned for a specific mission, then reconfigured for the next. This is the shift from a rigid "assembly line" model of venture formation to an adaptive "immune system" model. YC's move, while rationalizing their internal structure, highlights the fundamental tension: our 20th-century models of partnership are hitting their cognitive and organizational limits in a world that demands fluid, verifiable, and mission-specific coordination. #VentureCapital #OrganizationalDesign #Complexity #StartupEcosystem #Governance

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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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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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