The AI CEO

You know what I'm most excited about with GPT-4.5? The PS at the bottom of their email. Seriously, folks -- it's time we normalized AI-augmentation. This is something Asana does particularly well, as Henrik Werdelin and I learned on Beyond the Prompt AI. Link to show and a blog post it inspired in the comments below...

Bolt and Lovable just went from zero to $15M ARR in 2 months with 15 employees. Media calls them "AI miracles". Yet everyone missed the real story: We're witnessing the first wave of autonomous businesses in history. While the TechCrunch headlines are obsessing over their AI, that's not what makes them special. These companies aren't winning because of their AI. They're winning because they've cracked a completely new operating system for building companies. The math tells the story: Traditional SaaS: $150K-250K revenue per employee Autonomous businesses: $2M-3M+ per employee And the spoiler: It's not about replacing humans with AI agents.? It's about turning your existing talent into superhumans - empowering a small team to operate at enterprise scale. These companies didn't automate their humans away. They fundamentally reimagined how humans and AI work together. Where traditional companies build armies of specialists, autonomous businesses build small teams of strategic orchestrators. They're proving that the old playbook is dying: Hire 50+ engineers to "figure out" what to build Build a 100-person sales team to brute force growth Burn $2M+ on ads hoping to find what works Here's what the autonomous playbook actually looks like: Demand Engine: - AI: Qualifying 1000s of leads in real-time - Humans: Small team of strategists owning $100K+ opportunities - Result: 3-person team >> 50-person sales org Product Development: - AI: Handling 80% of code execution - Humans: Product leaders owning vision & strategy - Result: 5 builders shipping faster than 50 engineers Customer Success: - AI: Managing 90% of support queries - Humans: Strategic CSMs crafting expansion plays - Result: 2 humans driving same NRR as 20-person teams The pattern becomes clear: While traditional companies spread themselves thin, autonomous businesses concentrate human firepower where it matters most. At Swan, we're not just talking about this future - we're building it: 3 founders $0 spent on ads 30+ AI agents And a goal to hit $30M ARR without hiring a single employee. Because building an autonomous business isn't about replacing humans. It's about empowering small teams to achieve impossible scale. The era of bloated teams is over. The age of the autonomous business has begun. Building lean in 2025? Drop your story below ?? (Or tag a founder who needs to see this) #BuildASwan #HumanFirstSales #HumanEmpowerment

Struggling to balance the fast changing nature of generative AI with your overall technology portfolio strategy? Thoughts on the value of effective leadership and practical approaches to managing change while keeping ethics and stability of your ecosystem.

Sorry tech startup founders, but it's time for a bit of truth-telling. I'm speaking to those at the idea stage or finding first customers: Investment is wrong and you should not be doing it. Raising money is wasting your time. This is especially true in the current economic environment. Every ounce of energy lost while chasing investors who are either too f*cking rude to reply (VCs) or are great people (angels) but sadly burnt with all their money tied up in the last batch of now zombified startups, is time YOU could spend actually building the product, yourself. And knocking on client doors, yourself. Yes. YOU. ?? You don't need investment to pay yourself a salary. Salaries come out of revenues. If you have no sales, you need to live off your 9-5 salary or that of your partner; or be lucky enough to have some savings. ?? You don't need investment to pay developers. Building MVPs has never been easier or cheaper for 'anyone' to do. Something simple to offer early customers a bit of value and a vision of what else is possible. ?? You don't need investment to recruit salespeople and iPads for them all and have them go and be the first people to actually try to make a sale. Every startup must have the founder make the first sales. Raising investment nowadays is a minimum 6-12 months hard slog. It is painful. Most won't get a single penny even after all that time. If you're a female founder you'll also have to endure the "sorry princess, you're gonna need a male co-founder" crap too. And during that time, someone else in another town or on the other side of the world will be doing your idea and making real headway with your clients, while you churn out version 34 of your just-another-pitch-deck just so it can sit in an investor's in-tray 'til the end of time. But, imagine the other way, with 6-12 months to focus on actual building and selling stuff, rather than chasing rainbows... ?? Imagine all the low-code/no-code solutions YOU can master to build your own basic website and app. ??Imagine all the posts YOU can write on LinkedIn to build your social presence amongst your target audience. ??Imagine all the sales calls YOU can make to close deals and get actual cash coming into the business. Yes, into the "business", because it is no longer just an idea. And do you know the additional benefit of this approach? Investors will hear about you - trust me. And you may even get one or two reach out. Now who's the boss!? Time to get creative. ---------------------------- ?? Steve Procter ? Tech Entrepreneur for 29yrs ?? Helping the Next Generation of Founders ?? ?????????????? ???????? ???? ?????? ???? 24?????? – ?????? ???? ?????? ----------------------------

Which LLMs work best for AI agents? Introducing the Agent Leaderboard by Galileo??! It was built to evaluate LLMs on real-world tool-calling tasks—crucial for building AI agents that interact with external tools & APIs. After evaluating 17 leading LLMs across 14 diverse datasets, here are the key findings: 1?? Leader -- Google's ????????????-??.??-?????????? leads with a 0.94 score at a remarkably low cost. 2?? Pricing -- The top 3 models span a 10x price difference with only 4% performance gap. Many of you might be overpaying. 3?? Open-source -- Mistral AI's mistral-small-2501 leads open-source options, matching GPT-4o-mini at 0.83. 4?? Reasoning models -- While?reasoning models like o1?and?o3-mini?demonstrated excellent integration with function calling capabilities, DeepSeek-R1 didn't make the rankings due to limited function calling support. 5?? Edge cases -- Claude-sonnet?achieves standout performance in tool miss detection (0.92). In general, current models still struggle with edge cases. 6?? Architecture trade-offs -- Long context vs. parallel execution shows architectural limits: o1 leads long context (0.98) but fails parallel tasks (0.43), while GPT-4o shows the opposite pattern. (leaderboard and more details in the comments)