Johannes Maron

Ever spent hours digging through messy databases just to answer a simple research question? Last week, I spent 2 hours digging through databases trying to find German SaaS startups with AI-focused CTOs who recently raised Series A funding. You know the drill: bouncing between Crunchbase, PitchBook, and LinkedIn. Filtering by country, then funding stage, then manually cross-referencing CTOs' backgrounds. Compiling spreadsheets. Starting over when I realized I needed different data points. Sound familiar? My friend Ravit Jain introduced Deep Lookup at Bright Data to me when I shared my challenge with him. All I need to do is just tell Deep Lookup what I’m looking for: "Find all SaaS startups in Germany that raised Series A in the last 12 months and have a CTO with previous AI/ML experience. Show me company details, funding info, location, CTO LinkedIn, and team size." It takes just a minute for it to return with clean, relevant, and useful output. And I can add more context like CEO backgrounds or revenue estimates to enrich my search without starting over. Why these matters: ✅ I can focus on analysis instead of data hunting ✅ No more drowning in irrelevant results ✅ Research feels less like detective work, more like having a conversation ✅ Tailored for market mapping, startup scouting, or due diligence prep ✅ Built for researchers, investors and strategists who value their time. Anyone else tired of spending more time searching than analyzing? Would love to hear how you're handling research workflows these days. Bright Data #DataLoop #Search #AI #Researcher #Investors To stay current with the latest trends in #Technology and #Innovation, Subscribe to 👉 #CXOSpiceNewsletter here https://lnkd.in/gy2RJ9xg Or 👉 #CXOSpiceYouTube here https://lnkd.in/gnMc-Vpj

536

160 Kommentare

ChatGPT just hit 800 million weekly active users, up from 500 million in March. AI discovery is the fastest-growing channel to reach your customers. But more than that, it forces us to rethink buyer journeys for AI discovery. The team at Docebo told me AI discovery now accounts for 12.7% of their high-intent leads, up 429% (!) from last year. They're doing this with one dedicated team member (Valeriia) plus an assist from AI tools like AirOps & XFunnel. Here's the thing: ChatGPT leads don't look like SEO or referral leads. They show up ready to buy, either going to your website directly (*after* they've made a decision) or searching for your brand on Google. Branded search now accounts for 85% (!) of Docebo's search traffic -- meaning 85% who come from Google already know about the company. ▶️ The old buyer journey: Problem awareness > feature research > vendor comparison > brand selection. ▶️ The new buyer journey: AI-assisted research > network validation > direct brand search > selection. Read more in Growth Unhinged about what this means & how to get more pipeline from AI search: https://lnkd.in/ee2wKTbR The TL;DR: 1. Measure your AI search performance through self-reported attribution, branded search traffic, and share-of-voice in AI queries. 2. Late-funnel SEO is the foundation for great answer engine optimization (AEO). 3. The dirty work of AEO has to get done: updating, formatting, and structuring content for LLMs. 4. You can see tangible AEO results with a modern tech stack. #aeo #ai #seo

217

40 Kommentare

Complete ranking of 24 most valuable GenAI startups in 2025: 5 years ago, most of them didn’t exist. Today, they’re leading the AI revolution and are worth $770B+ combined. 1. OpenAI (2015) 💰 $300B valuation | $63B raised General-purpose GenAI (GPT / DALL·E / Whisper) 2. DeepSeek AI (2023) 💰 $10B-$200B valuation (estimated) | self-funded Open-source & proprietary LLMs 3. Anthropic (2021) 💰 $170B valuation | $13B raised Conversational AI (Claude models) 4. xAI (2023) 💰 $80B valuation | $12B raised General-purpose GenAI (Grok) 5. Perplexity (2022) 💰 $18B valuation | $500M raised AI search engine 6. Midjourney (2022) 💰 $10.5B valuation | bootstrapped AI image generation 7. Anysphere (2022) 💰 $9.9B valuation | $900M raised AI-native coding environment (Cursor) 8. Glean (2019) 💰 $7.25B valuation | $150M raised Enterprise AI search platform 9. Mistral AI (2023) 💰 $6.5B valuation | $600M raised Open-source & proprietary LLMs (Mixtral, Mistral 7B) 10. Zhipu AI (2019) 💰 $5.6B valuation | $1.5B raised Foundational AI models (GLM) 11. Cohere (2019) 💰 $5.5B valuation | $500M raised Private enterprise AI solutions (Command R+) 12. Harvey (2022) 💰 $5B valuation | $300M raised Legal AI automation 13. Hugging Face (2016) 💰 $4.5B valuation | $445M+ raised Open-source AI model hub 14. Inflection AI (2022) 💰 $4B valuation | $1.5B raised Personal AI assistant (Pi) 15. MiniMax (2021) 💰 $4B valuation | $850M raised Foundation AI models 16. Runway (2018) 💰 $3.5B valuation | $237M raised AI video and image generation 17. Moonshot AI (2023)💰 $3.3B valuation | $1.6B raised Conversational AI (Kimi chatbot) 18. ElevenLabs (2022) 💰 $3.3B valuation | $281M raised AI voice synthesis (Multilingual V2, VoiceLab) 19. Windsurf (2021) 💰 $3B valuation | $252M raised AI coding assistant (Google acquired) 20. Character.AI (2021) 💰 $2.5B valuation | $150M raised AI chatbot personas (Google acquired) 21. WRITER (2020) 💰 $1.9B valuation | $200M raised AI writing assistant for enterprises 22. Lovable (2023) 💰 $1.8B valuation | $200M raised AI-powered app development 23. Jasper (2020) 💰 $1.5B valuation | $125M raised AI content generation 24. Adept (2022) 💰 $1B valuation | $415M raised AI agent and automation Bonus: Fluently: AI English coach 💰 $2.5M+ raised (YC W24) Improve your English speaking skills with AI 👉 https://getfluently.app/ The average time to reach a unicorn status is dropping rapidly - over 40% of the listed startups hit $1B+ in less than 24 months, with some doing it in less than a year! It’s not just fast, it’s historic. Generative AI is scaling quicker than any tech wave before, reshaping industries before they even catch up.

1.241

46 Kommentare

First, OpenAI sold you the API. Now they’re selling you the people to make it work. $10M+ to deploy what was once “just a model.” Because enterprises aren’t paying for tokens anymore. They’re paying for execution. OpenAI’s new consulting arm isn’t just a premium upsell. It’s a strategy pivot and a loud signal that access to AI isn’t the hard part anymore. Deployment is. They’re not the first to figure this out: - Palantir Technologies has been doing this for 20 years. - IBM built WatsonX on the same model. - Microsoft is embedding engineers into Fortune 500s to make Copilot actually work. Now OpenAI is following suit – staffing $10M+ engagements with forward-deployed engineers to help clients integrate GPT-4o into mission-critical systems. Why? Because gen AI doesn’t scale on its own. At our customers where we are successfully implementing AI, it takes much more than AI! It takes Architecture. Data access. Guardrails. Change management. People skills. Strategic planning and execution. AI can’t operate in a vacuum. Chatbots don’t solve edge cases. LLMs don’t magically follow compliance rules. You need structure. You need rules. You need a platform. And AI operates within that guardrailed environment, in real time. OpenAI’s move confirms it: If your AI strategy stops at the demo, you don’t have a strategy. You have shelfware.

1.003

116 Kommentare

Most startups fail because they build products nobody needs 💔 Yet, there are startups that grow up to be worth +$1B 💹 I analyzed some of those companies and gathered this 10 takeaways 👇 1. How to get to Product Market Fit: what it is, step by step guide and success cases like Notion, Canva and Stripe: https://lnkd.in/dRqXZfPD 2. How to find your first 10 clients and not lose them in 1 month: https://lnkd.in/dXE9gevx 3. The $1M ARR Playbook and how HeyGen, Lemlist and Canny did it: https://lnkd.in/dYNayi59 4. The Customer Centric Go To Market Strategy: the simplest, most effective way to sell your startup's product + flywheel growth: https://lnkd.in/dwx56UVE 5. How to ship fast and create a customer feedback loop that grows your startup: https://lnkd.in/dM_zUBgW 6. The How to sell guide for the first time founder + how Notion, Hubspot & PandaDoc do it: https://lnkd.in/d9Q2p28Z 7. How to ship fast and create a customer feedback loop that grows your startup: https://lnkd.in/dM_zUBgW 8. The Product Market Fit Framework by Sequoia: https://lnkd.in/dHWu_wy9 9. The Four levers of Product Market Fit by First Round Capital: https://lnkd.in/dPfEEtHJ 10. How Linktree got from 0 to $37M ARR and 12M users with the simplest product ever: https://lnkd.in/dET-g-Gd If this was helpful, save it and repost to help other entrepreneurs 🚀 Image by the great Lenny Rachitsky

219

10 Kommentare

Former OpenAI and DeepMind employees have launched 45+ startups worth $224 billion total. That avg = $2.77 billion in enterprise value per founder. Even excluding the top three companies, it's still $1.45 billion per founder. 𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗹𝘂𝗺𝗻𝗶 𝗕𝗿𝗲𝗮𝗸𝗼𝘂𝘁:   • Anthropic (Dario & Daniela Amodei): $15B valuation, $8B+ raised   • Safe Superintelligence (Ilya Sutskever, Daniel Gross, Daniel Levy): $32B valuation, $8B committed   • Thinking Machines Lab (Mira Murati): $10B valuation, $2B seed round   • Perplexity AI (Aravind Srinivas): $1B+ valuation, $100M+ raised   • Adept AI (David Luan): $1B+ valuation, $415M raised 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱 𝗔𝗹𝘂𝗺𝗻𝗶 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:   • Inflection AI (Mustafa Suleyman, Karén Simonyan): $4B valuation, $1.3B+ raised   • Mistral AI (Arthur Mensch): $6B valuation, $500M+ raised   • Harvey (Gabriel Pereyra): $500M+ valuation, $100M+ raised   • Daedalus (Jonas Schneider): $200M valuation, $34M raised 𝗠𝗲𝘁𝗮/𝗙𝗔𝗜𝗥 𝗔𝗹𝘂𝗺𝗻𝗶:   • Cohere (Aidan Gomez, Nick Frosst): $2.2B valuation, $445M raised EvolutionaryScale (Alexander Rives): \~$200M valuation, $142M raised   • Nabla (Alexandre LeBrun, Martin Raison): $800M valuation, $43M raised 𝗩𝗮𝗹𝘂𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝗯𝘆 𝗦𝗼𝘂𝗿𝗰𝗲 (𝗲𝘅𝗰𝗹𝘂𝗱𝗶𝗻𝗴 𝘁𝗼𝗽 𝟯):   • OpenAI alumni: $23.6B total valuation   • DeepMind alumni: $18.2B total valuation   • Meta/FAIR alumni: $3.4B total valuation   • Other sources: $1.3B total valuation 𝗙𝘂𝗻𝗱𝗶𝗻𝗴 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀:   • DeepMind alumni raised: $3.89B total   • OpenAI alumni raised: $1.93B total   • Meta/FAIR alumni raised: $420M total This super interesting data from Trace Cohen may explain Meta's $200M retention packages. It represents 0.0067% of their $1.5T market cap. Compare the ROI:   • $100M = 0.13% of xAI's current valuation   • $100M = 0.31% of Safe Superintelligence   • $100M = 1% of Thinking Machines Lab Do these individuals represent the most concentrated value creation in startup history? When Ilya Sutskever announces a company, investors commit billions before seeing a product roadmap. The AI talent war is about the specific individuals who've proven they can architect systems that define technological paradigms.

26

5 Kommentare

Lovable officially killed the lean startup playbook. Steal our GTM system to launch profitably in 72 hours. Everyone's still following the 2015 startup playbook. → Build an MVP. → Find product-market fit. → Scale with venture capital. But AI has changed everything. The old model builds tech first. Then hopes for users. And iterates until money either appears or runs out. The new way starts where the money already exists. And you do this by: Finding angry customers first. Your network is the best for this. But if you don't know anyone, you can: Jump into industry forums or communities. And look for the phrases that signal pain, like: "I waste 3 hours every day on" "We're still using spreadsheets for" "There has to be a better way to" Reddit is a goldmine for this. Screenshot everything and document their exact words. reach out to complainers directly. Ask them to walk you through their current process. Ship their dream tool in 72 hours. → Day 1: Map their workflow in Lovable → Day 2: Build core functionality → Day 3: Deploy and get first beta customer → Day 4: Get testimonials and add a paywall And repeat this process until you find a winner. AI is letting solo founders hit recurring revenue faster than funded teams. I've seen this happen countless times. And it's all because they're not guessing what customers want. They're building exactly what customers are already begging for. In a matter of days. Funded teams spend months in meetings debating features. Smart builders spend days shipping solutions to problems that already have budget allocated. Stop building what you think people need. Start building what they're already trying to buy.

68

8 Kommentare