Alex Lieberman

Jamming with an LLM via voice mode is one of my favorite activities while on-the-go. What an insane gift it is to meander through professional/personal rabbit holes with a 24/7/365 sparring partner that has encyclopedic knowledge. Have used it to: - Critique a mental model of how to think about AI ROI in enterprise - Explore research on psychopathy and how much is nature vs. nurture - Brainstorm improvements to my startup IG show to maximize watch time - Beat up my POV on the best way to compensate internet creators - Understand the nitty gritty of 529 plans and strategies behind superfunding them Quickly realizing this post-AI era is a gift for everyone, but especially verbal processors.

Vibecoding at most companies is the wild west. Here are 6-steps for letting employees vibecode useful apps, responsibly 👇

We’re hiring like crazy at Tenex. Launched 15 months ago and on track to end the year at Series C size. Few key roles: 1) DevRel: If you work in DevRel and want to join a hypergrowth applied AI company transforming fortune 500s & incubating ai products, shoot me a DM. 2) Long form writer: you’d own our newsletter, longform site content, applied AI playbooks, research reports, etc. shoot me a DM if you think you’re a good fit.

There have historically been two career paths for engineers: ic & manager. I'm offering a third path: creator. The job is... - 1/3 ai engineer - 1/3 ai creator - 1/3 ai educator Hot take: being a creator is one of the highest status jobs in a sea of abundance, where earning & nurturing trusted distribution has never been more important. But many of the best creators don't just want to create content. They want to practice what they preach, while using the learnings from their craft as the foundation for the content they create. Which is why we've created & hiring for the Applied AI, Engineer & Creator at Tenex. You'll solve complex problems with AI for some of the biggest companies in the world, while leveraging those learnings to create content online & lead AI workshops for F2000 enterprises. Must-haves for the role: - Cracked 10x engineer - Strong communication skills Nice-to-haves: - A student of X - Experience creating content online - A small, but mighty following online And yes, I'm looking for a unicorn. Apply here: https://lnkd.in/gRvr86Vc Tag anyone below you think could be a good fit.

My co-founder Arman Hezarkhani broke down Kimi K3 (new 2.8T parameter Chinese model) on Fox Business Network today with Charles Payne. We had our engineers testing it all day before he went on air. Here's the gist: 1) K3 is very good. As good, if not better, than many of the American frontier models. 2) The cost story is being misread. Everyone expects a Chinese model to be the cheap one. Per token, K3 is competitive. Per run (the actual job you outsource to the model), it's as expensive as the frontier models. But it's supposedly open source, so developers will attack the cost curve. Give it weeks, not quarters. 3) The playbook should look familiar. It's the same one China ran on solar panels & EVs: flood the market with cheap supply, wait for the addiction, then move the price. 4) The internet & the space race were funded by the US government, and innovators competed on top of the platform. AI got funded by private markets, and now the same companies that spent those trillions are getting hamstrung right as China gives its models away. 5) On guardrails: the bad guys will have completely unconstrained tools no matter what we do. Foreign adversaries, and bad actors here at home. If the good guys don't have equally powerful tools, only one side is armed. 6) Commoditized models are actually good news for the hyperscalers. All that open source intelligence has to run somewhere, and Google & AWS will get paid a lot of money to run it. 7) The labs saw this coming too. It's why Anthropic & OpenAI are racing up the application layer instead of just selling tokens.

One of the most common AI questions we get from execs: "How do we let non-engineers build vibe-coded apps that become useful and aren’t a nightmare for IT?" The answer: A Citizen Developer SDLC A six-stage lifecycle that takes software built by non-technical employees with AI from personal prototype to governed production: Stage 1 (Idea): - Input: Plain-language memo, AI-assisted brief, owner, users, data sources. - Output: A durable brief that downstream checks and decisions can reference. Stage 2 (Front Door): - Input: The brief becomes the request, IT ticket, and permanent record. - Output: Every app enters triage on the record before any build work begins. Stage 3 (Triage): - Input: App shape, blast radius (reach, reversibility, exposure, data sensitivity), overlap with existing apps. - Output: Approved, reused, or escalated with full request context and a named shape. Stage 4 (Provisioning): - Input: One approval, the paved road for the app’s shape, app metadata and owner. - Output: An isolated app shell with auditability and constraints from day zero. Stage 5 (Build): - Input: Controlled cloud workspace, AI coding agent, generated contracts and test data. - Output: Routine changes move at CI speed. Consequential exceptions reach people. Stage 6 (Run and change): - Input: Production usage, change requests, audit events, ownership and access data. - Output: Promote what people use, archive what they do not, send changes back to Stage 1. One principle runs through every stage: AI does the labor, deterministic code sets the guardrails, and humans handle the exceptions. It exists because AI collapsed the cost of writing code to near zero and moved the bottleneck downstream, to making sure what got built is sound and keeping a growing fleet of it governed once it is live.

Kimi K3, the 2.8 trillion parameter open-weight Chinese model, owned the internet today. I read tons of content about it & observed 10 key patterns: 1) The open source-to-frontier gap went from a year+ behind to 6 months to 6 days, all within the last 12 months. 2) An open model debuted ahead of a flagship US model for the first time ever. Artificial Analysis scored K3 at 57. Opus 4.8 sits at ~56, GPT-5.6 Terra at 55. It's still behind Fable 5 and GPT 5.6 Sol. 3) K3 helped build itself. An early version of K3 did the majority of Moonshot's own kernel optimization work during development. One 15-hour unattended run made a core operation 2.5x faster. 4) It's cheap per token, not cheap per answer. Sticker price is 1/3 of Fable. But it only runs at max thinking effort and burns ~2x the tokens per response. Example: 13,241 reasoning tokens to write a 3,417 token answer. 5) The era of dirt-cheap Chinese AI is ending. $3/$15 per million tokens. Hacker News called it "extremely high for a Chinese open-weight model." 6) Weights don't drop until July 27. Mentions of "open" quietly disappeared from the docs an hour after launch. 7) Even when the weights drop, you can't run them. 2.8 trillion parameters. Top Reddit joke: "2TB VRAM Is All You Need." Open weights increasingly means auditable by companies with GPU clusters, not runnable by you. 8) The "they just distill/copy" argument is dying in public. One of the most upvoted comments: you'd have to be "a complete ignorant or a complete bigot" to believe Chinese labs aren't legit at this point. 9) Day one user verdict: fast, but less accurate. "Faster than Claude, but less accurate. On par with GPT 5.5 perhaps, but not 5.6 or Fable." 10) The one thing everyone agrees on: competition is wonderful. Even the skeptics: "Say what you want about these Chinese models but they sure create competition and urgency in the space."

Despite being a “successful” founder, I really am poor at several things. These aren’t fake weaknesses that one would share in job interviews. They are things I genuinely stink at and wish I stunk less. I’m disorganized. I’m impulsive. I procrastinate. I’m highly distractible. I have trouble sticking to a timeline. So how have I had one 9-figure outcome & another hypergrowth startup, in spite of my deficiencies? 1. I embrace the cringe. I make audacious asks to customers. I’ll look like an idiot on social media. I’ll push through the discomfort of asking innumerable questions and keep going until I actually understand something. I’ll make an ass out of myself if it means getting more exposure for my business. 2. I’m a one-trick pony. And I don’t feel bad about it. There are a few things I’m world class at. Building organic distribution. Relationship building with execs. And telling a compelling story. I spend 90% of my time sharpening these tools while letting others around me cook in their zones of genius. 3. I’m like a kid. If there’s one thing I’m good at it’s honoring my 5-year-old self. I’m insatiably curious. I probably ask 100+ questions/day to employees, customers, and people in my network. I’m goofy and playful. I can be serious, but most of the time I’m not, because most of what we do in knowledge work, just isn’t that serious. You can find me shooting nerf darts across the office or building a Lego set in between calls. Why do I share all of this? Because first time founder Alex wanted to be great at everything and enjoy every part of building a business. And if he didn’t, he thought something was wrong with him. Nothing was wrong with him. He was exactly what he needed to be and where he needed to be. He just didn’t believe it yet.

My team spends all day talking AI with enterprise execs. I asked them to share the most common questions they get. Here's what we're hearing from the field: • How to properly build a UAT suite that can test not only the new software we are building but also account for the AI features in some benchmark eval suite? • How can I provide guardrails and direction to enable the front line to develop useful tools and applications for the business? • How do you deal with fragmented data? What is your process to unify without a massive overhaul of our orgs data architecture? • How do I stand up the internal motion to drive AI tools and workflows when everyone also has their day job? • How do we know an agent that we create is actually "good"? How do we measure that? How can we improve it? • How can I give employees full agency to create without impacting mission-critical systems/workflows/etc.? • How do I uniformly transform a multi-thousand person org to adopt ai? How do we not leave anyone behind? • What does the operating model have to look like with my direct reports as well as the org with AI? • How do I start controlling token spend and how do I think about attributing value to a token? • How do we develop a central company brain to capture embedded organizational tacit knowledge? • What are the best ways to be multi-model and have a multi threaded approach to partnerships? • How can non technical people access, change, and iterate on apps they did not build? • When an agent does eight hours of work, how does a human check it in eight minutes? • What are the big investments I need to make in my data to make AI effective? • How do I protect my data while still having the harness of cowork and code? • How do employees in different business units edit, manage their own skills? • How do we adopt AI so that we aren't vendor locked with one frontier lab? • How do we ensure our AI usage is safe (infra & security controls)? • What data is safe to put in (especially sensitive functions)? • Whats the path from AI Literate, to AI Enabled, to AI First? • How do we get people excited vs scared to lose their jobs? • How can AI apply when I'm in a highly regulated industry? • As a CEO, what do I need to know about AI to run my org? • Should I hire a team vs. work with an external partner? • When processes are the problem, where do I start? • How do we let people access internal data safely? • Should I allow Skill creation? Artifact creation? • Who should I give access to Claude Code or Codex? • What does the cutting edge of AI SDLC look like? • How do I "sell" AI internally within my company • How do I know which models are actually good? • Who owns a build after it's deployed? • How to capture the full scope of ROI? • How do we prioritize use-cases? • What are other companies doing? • Who should own AI internally? • How do we distribute skills? • What is an agent harness? • How do we govern AI? • Are we behind?