It feels like the internet is breaking. AI has created an entirely new paradigm in cybersecurity and I want to learn as much as I can about it. So I found a world-class cyber expert to give me a 60-minute cyber primer on Friday at 12pm ET. And you're invited (link to register below). David Campbell (@dcam_ai) is former Head of AI Security Research at Scale AI, where he architected and lead some of the most advanced AI red teaming and resilience programs in the world. Here's what we'll talk about & the questions I'll ask: 1) What cybersecurity has historically meant - Explain cybersecurity to me like I'm a smart CEO who's never had to think about cyber. - Give me the cast: nation-states, ransomware gangs, insiders, hacktivists. Who actually costs companies the most money? - Walk me through the eras of cyber security: castle-and-moat firewalls, then cloud and "identity is the new perimeter," then zero trust. What changed each time? - How was a security team traditionally organized? - What are the key terms/language you need to know to be cyber-literate? Translate SOC, red team, blue team, pen test and CISO for me. 2) How AI changed it - What's the single biggest thing AI changed about cyber, in one sentence? - What did AI do to the types of attacks that are done and the manner in which they’re done? - What did AI do to the cheap, everyday attacks: phishing, deepfake voice calls to finance, fake vendors? - Did AI change who the attacker is? Can a teenager now do what took a nation-state in 2015? 3) What AI-native cybersecurity looks like - You wrote that AI security isn't bullshit, but we're securing the wrong thing, and that prompt injection isn't the real risk surface. Explain. - Companies are handing AI agents keys to email, code, CRM and payments. What new attack surface does that create? - What is AI red teaming, how is it different from a classic pen test, and what does a good engagement look like? - If you were building a security company from scratch today, what would be AI-native about it that incumbents can't copy? 4) What being great at cyber looks like for a business today - I'm a CEO of a 1,000-person company. Give me the five things I must have in place in 2026. - Before I roll out AI agents across the company, what's the security checklist? - How should a board measure security now? What are the 2–3 metrics that matter? - Build, buy or partner? What should a mid-market company never try to do in-house? Link to register here:

Just spoke to the Head of AI at a huge company. The #1# thing they're struggling with is... what do I do with the AI laggards at our company? Like most companies, they’ve found that a Super Pareto Principle is true within their org. 10% of their employees are responsible for 90% of the uplift in productivity/token usage from software engineering to non-technical knowledge work. They’ve spent a lot of time & resources on AI enablement from LLM foundations to function-specific trainings and skill building. And yet, the power law of productivity increases hasn’t budged. So now they’re left with a few sobering thoughts and hard questions: 1) 90% of people are overpaid and 10% are underpaid 2) We’d be better off allocating tokens proportionally to the power law, but how do we do that without killing culture? 3) Significant layoffs are likely warranted and wouldn’t impact overall output, but cultural backlash is a big concern? 4) How do we hire more power law people as a non technology company? 5) Is all of our efforts on training and enablement ACTUALLY worth it? 6) How do we not experience a brain drain where the top 10% all leave because they want to be surrounded by other AI-native 10x’ers?

AI Agent Sprints have blown up since we launched them a few weeks ago. Have received interest from hundreds of enterprises including: - Tesla - Airbus - SharkNinja - Amazon - Palo Alto Networks - Samsung We have a few spots left, before we close the October cohort. These 5-day, free intensive bootcamps for enterprise leaders are the perfect way to FINALLY get agents to production. At @tenex_labs, we've helped dozens of midmarket cos, F2000 enterprises, and PE portcos deploy agents into production. With bootcamps, we're lowering the friction of experiencing the power of an agent within your company's workflows to ~zero. 3 phases of the sprint: 1) Find Opportunities - We interview the people closest to the work to identify where agents could have the biggest impact. 2) Map Workflows - We map workflows step by step, including the systems involved and expected business impact. 3) Prototype Agents - We build prototypes on your data, demonstrate the results, and recommend what to take live first. Sprints are led by our world-class FDEs & strategists, on the ground, working with your teams and systems. What do you get? 1) A clear AI roadmap with ROI per opportunity 2) Up to 8 mapped opportunities 3) Up to 4 working prototypes Who are agent bootcamps for & how much do they cost? VP-level & up decision makers at midmarket & enterprise companies doing >$100m in revenue or funding. Bootcamps are free. They're an opportunity to see what AI agents can do for you before committing. Why are we launching this now? Many companies are in the messy middle of AI transformation. They've rolled out LLMs companywide, perhaps done some AI enablement work, coding agents are being used widely in engineering, and that's it. The next obvious step in the AI journey is bringing agents to the workflows of an organization, but most companies are getting stuck. Taking agents to production is complex and nuanced and we've built a repeatable process for handling the final mile of applied AI, which allows us to take agents into production in ~2 weeks. How do I sign up? Join the Agent Sprint waitlist here:

I'm cohosting "Master @Meta's Muse in 30 Minutes" tomorrow at 1pm ET. @JJEnglert, an AI trainer & builder, has spent hundreds of hours experimenting with Muse, since it was released. On this episode, he will teach me how to use Muse from Easy Mode to God Mode. He'll speedrun the proper setup to get the most out of your Muse & walk us through his most cracked workflows like voice calling, connecting Muse to hardware, and other cool stuff. RSVP for the event here: P.S. What are the odds @alexandr_wang makes a cameo an Muse Mogs us?

I'm hosting a "How to Muse Mog your Friends" event tomorrow at 1pm ET. Will be interviewing @JJEnglert, an AI trainer & builder, who has spent hundreds of hours experimenting with Meta's new personal agent, Muse, since it was released. On this episode, he will teach me how to use Muse from Easy Mode to God Mode. He'll speedrun the proper setup to get the most out of your Muse & walk us through his most cracked workflows & ways he's making Muse do cool sh*t for him. RSVP here: P.S. what are the odds @alexandr_wang makes a cameo an muse mogs us?

Demand for these Executive AI Agent Bootcamps has been insane. 18 hours in and 200+ companies have joined the waitlist. Instant market pull from execs looking to get agents to production in their companies. If you’re VP-level up at a $100m+ company & want to join the waitlist for our free 5-day, Exec AI Agent Bootcamps, fill out the form below. We’ll be prioritizing your applications.

The real winner of this AI race is capitalism. The speed by which labs are shipping. The downward pressure on model prices. The historic infrastructure build out. The money flowing through the veins of venture. Capitalism is doing its thing and it’s beautiful.

Demand for these Executive AI Agent Bootcamps has been insane. 18 hours in and 200+ companies have joined the waitlist. Instant market pull from execs looking to get agents to production in their companies. If you’re VP-level up at a $100m+ company & want to join the waitlist for our free 5-day, Exec AI Agent Bootcamps, fill out the form below. We’ll be prioritizing your applications.

The year after selling my business was one of the hardest periods of my life. People are usually shocked to hear this & can't make it make sense. "You accomplished what every entrepreneur dreams of." "You made life changing money, what could be so bad?" "You were given the gift of time, which is the greatest gift in the world." What you're wondering is true & valid...and that's partially what made it so painful. The journey of exiting @MorningBrew was a paradox of the highest order. The outcome was beautiful in so many ways... - A small idea that my cofounder and I dreamt in our dorm rooms at college blossomed into a media company that impacted millions of lives & reached a size that pushed our wildest imagination and ambition. - A promise fulfilled. My dad passed away when I was a junior in college. In my last conversation with him, I promised him that I'd do everything in my power to take care of the Lieberman household so no on had to worry about money. - Financial security that left me & my wife with a sense of security and optionality as we looked forward to growing our family. - A new home for our business that could allow us to dream bigger & take bigger swings with the opportunity for greater scale, imagination, and impact. It was also painful in nearly as many ways... - My company ate my identity. I spent most of my young adult years building this business and being validated as "CEO of Morning Brew" which felt good, appealed to my ego, and continued this cycle of have an idea --> do good work --> get celebrated for it. - My 12/10 motivation disappeared overnight. Fear-based motivators were the story of what drove me during this chapter of my life. I had a chip on my shoulder to prove those who bullied me (earlier in life) wrong & I felt financial anxiety to create safety for my family after my dad's death. Once we sold, the motivators that served me in the previous 10 years would not serve me for the next 10. - The aftermath was lonely. My "struggles" post-exit felt like a champagne problem people would kill for. Because of that I rarely shared the journey. And because I rarely shared the journey, I bottled up many of these emotions. - Time is a gift and a curse. Once I stepped out of the CEO role, I was given the "gift" of time. But unless you know how to productively direct time, it can feel like a prison. For a year straight, I marinated in my thoughts around being lost, no longer being motivated, and needing to re-find purpose. While the 1-2 years post-exit were challenging, I now very much view it as a period of getting lost to better find myself. I emerged from this chapter of my life with clarity around a few things: 1) Fear-based motivation is incredibly powerful but short-lived. Once you've nurtured your ego, materialistic needs, and childhood traumas, the fire under your ass quickly turns into fizzling coals. Today, I try my best to listen to growth-based motivators that feed 3-year-old Alex's desires: curiosity, growth and play. This type of motivation is the energy equivalent of an ultramarathon vs. a 100-yd dash. 2) Freedom of time ≠ happiness. It is true that time is finite & having the privilege of choosing how you spend your time is exactly that...a privilege. But being gifted with more time does not mean you'll automatically feel more fulfilled. Why? Our job as humans is to create. To create families. To create communities. To create companies. When we are not in the act of creation, or at the very least have clarity around what we're working towards creating, we can feel lost. 3) Deeply understanding your zone of genius & your values is the gift that keeps on giving. During my post-exit journey I got clear on two things: - The 1-2 superpowers I bring to the world & how they can show up in my professional & personal life. For me: those are relentless curiosity & a fervor for storytelling. - How money can be translated into happiness & at what point more money doesn't convert into more happiness. Money feels most valuable to me when it's used in 3 ways: not having to worry about it, giving to my family, and spending on experiences with the people i love. The most helpful tool that helped me get clear is something called The Best Stuff Exercise (by Conscious Leadership Group), which I couldn't recommend enough. This was a long-winded way of saying that everyone's journey has nuance and periods of wandering are uniquely human, profound & beautiful, but also complicated and painful. Hope this helps!

The real winner of this AI race is capitalism. The speed by which labs are shipping. The downward pressure on model prices. The historic infrastructure build out. The money flowing through the veins of venture. Capitalism is doing its thing and it’s beautiful.

We are launching AI Agent Bootcamps at @tenex_labs! What's an agent bootcamp? A 5-day, free intensive to find & build high-impact agent opportunities with your real systems & data. We've helped dozens of midmarket cos, F2000 enterprises, and PE portcos deploy agents into production. With bootcamps, we're lowering the friction of experiencing the power of an agent within your company's workflows to ~zero. 3 phases of the bootcamp: 1) Find Opportunities - We interview the people closest to the work to identify where agents could have the biggest impact. 2) Map Workflows - We map workflows step by step, including the systems involved and expected business impact. 3) Prototype Agents - We build prototypes on your data, demonstrate the results, and recommend what to take live first. Bootcamps are led by our world-class FDEs & strategists, on the ground, working with your teams and systems. What do you get? 1) A clear AI roadmap with ROI per opportunity 2) Up to 8 mapped opportunities 3) Up to 4 working prototypes Who are agent bootcamps for & how much do they cost? Midmarket & enterprise companies doing >$100m in revenue or funding. Bootcamps are free. They're an opportunity to see what AI agents can do for you before committing. Why are we launching this now? Many companies are in the messy middle of AI transformation. They've rolled out LLMs companywide, perhaps done some AI enablement work, coding agents are being used widely in engineering, and that's it. The next obvious step in the AI journey is bringing agents to the workflows of an organization, but most companies are getting stuck. Taking agents to production is complex and nuanced and we've built a repeatable process for handling the final mile of applied AI, which allows us to take agents into production in ~2 weeks. How do I sign up? Join the Agent Bootcamp waitlist here:

Better models are becoming less and less important for most people. It's like going from a 110 IQ employee to a 120 IQ employee at your company. The relative performance of these two people likely has nothing to do with IQ and everything to do with all the things surrounding their IQ (empathy, work ethic, agency, etc). Same thing goes for AI. Every marginal minute that a knowledge worker spends with the technology right now should be spent on context management, evals, and change management. And JJ's video (below) is a perfect starting point to chip away at the context management piece...

The year after selling my business was one of the hardest periods of my life. People are usually shocked to hear this & can't make it make sense. "You accomplished what every entrepreneur dreams of." "You made life changing money, what could be so bad?" "You were given the gift of time, which is the greatest gift in the world." What you're wondering is true & valid...and that's partially what made it so painful. The journey of exiting @MorningBrew was a paradox of the highest order. The outcome was beautiful in so many ways... - A small idea that my cofounder and I dreamt in our dorm rooms at college blossomed into a media company that impacted millions of lives & reached a size that pushed our wildest imagination and ambition. - A promise fulfilled. My dad passed away when I was a junior in college. In my last conversation with him, I promised him that I'd do everything in my power to take care of the Lieberman household so no on had to worry about money. - Financial security that left me & my wife with a sense of security and optionality as we looked forward to growing our family. - A new home for our business that could allow us to dream bigger & take bigger swings with the opportunity for greater scale, imagination, and impact. It was also painful in nearly as many ways... - My company ate my identity. I spent most of my young adult years building this business and being validated as "CEO of Morning Brew" which felt good, appealed to my ego, and continued this cycle of have an idea --> do good work --> get celebrated for it. - My 12/10 motivation disappeared overnight. Fear-based motivators were the story of what drove me during this chapter of my life. I had a chip on my shoulder to prove those who bullied me (earlier in life) wrong & I felt financial anxiety to create safety for my family after my dad's death. Once we sold, the motivators that served me in the previous 10 years would not serve me for the next 10. - The aftermath was lonely. My "struggles" post-exit felt like a champagne problem people would kill for. Because of that I rarely shared the journey. And because I rarely shared the journey, I bottled up many of these emotions. - Time is a gift and a curse. Once I stepped out of the CEO role, I was given the "gift" of time. But unless you know how to productively direct time, it can feel like a prison. For a year straight, I marinated in my thoughts around being lost, no longer being motivated, and needing to re-find purpose. While the 1-2 years post-exit were challenging, I now very much view it as a period of getting lost to better find myself. I emerged from this chapter of my life with clarity around a few things: 1) Fear-based motivation is incredibly powerful but short-lived. Once you've nurtured your ego, materialistic needs, and childhood traumas, the fire under your ass quickly turns into fizzling coals. Today, I try my best to listen to growth-based motivators that feed 3-year-old Alex's desires: curiosity, growth and play. This type of motivation is the energy equivalent of an ultramarathon vs. a 100-yd dash. 2) Freedom of time ≠ happiness. It is true that time is finite & having the privilege of choosing how you spend your time is exactly that...a privilege. But being gifted with more time does not mean you'll automatically feel more fulfilled. Why? Our job as humans is to create. To create families. To create communities. To create companies. When we are not in the act of creation, or at the very least have clarity around what we're working towards creating, we can feel lost. 3) Deeply understanding your zone of genius & your values is the gift that keeps on giving. During my post-exit journey I got clear on two things: - The 1-2 superpowers I bring to the world & how they can show up in my professional & personal life. For me: those are relentless curiosity & a fervor for storytelling. - How money can be translated into happiness & at what point more money doesn't convert into more happiness. Money feels most valuable to me when it's used in 3 ways: not having to worry about it, giving to my family, and spending on experiences with the people i love. The most helpful tool that helped me get clear is something called The Best Stuff Exercise (by Conscious Leadership Group), which I couldn't recommend enough. This was a long-winded way of saying that everyone's journey has nuance and periods of wandering are uniquely human, profound & beautiful, but also complicated and painful. Hope this helps!

Hot take: world-class recruiting & hr teams are more important than ever. Why? AI is a leveraged bet on people. Because of that, hiring exceptional people and maintaining talent density has never been of greater importance. Which means the competition for outlier talent will be fierce as hell. People you trust who help you build and retain human intelligence so you get the most out of artificial intelligence will command a very high premium.

I don't know if Jev will win, but I do believe the architecture & benefit is here to stay. So I found someone actually shipping with Jev's architecture and made him share best use cases. @moritzkremb (ex-PM, Prompt Warrior) took me from easy mode to god mode for a 13-minute masterclass on Jev. 1) Easy Mode: what Jev actually is - Definition: a classification / decision model. You give it input + a question. It returns probabilities (yes 96% / no 4%), not an essay. You need two building blocks: - Inputs. The thing you’re judging (an ad creative, a resume, a page state, pasted invoice text). - Checkable questions. Dozens if you want. Fire them in parallel. Get instant yes/no (with confidence) back. - Why it matters: ~20–200x faster and ~40–400x cheaper than making an LLM write you a paragraph for every judgment. Use case: Meta Ads Analyzer. For every ad, Jev runs a question pack (does the hook call out the audience? quantified result in the first sentence?) and turns those probabilities into a dashboard you can act on. 2) Hard Mode: detailed scoring across a pile of items Definition: stop asking one model for a vibe summary. Ask many precise questions across hundreds of items, then bucket the results. Rules of thumb: - Write questions like hiring criteria or ad QA, not vibes. “Has the candidate shipped and maintained a user-facing TypeScript/React app?” - Weight what matters. Not every must-have is equal. - Sort into clear buckets (Shortlist / Review / Decline) so humans only spend time on the middle. Use case: An AI recruiter that sifts hundreds of applicants in milliseconds against your must-haves. 3) God Mode: real-time products that didn’t make sense before Definition: Jev as System 1 (fast, intuitive) next to LLMs as System 2 (slow, deep reasoning). Speed + cost unlocks products that were too slow or expensive to build. What to use Jev for: - Lots of data that needs detailed checks → classification loop. - Must react while the user is still speaking or pasting → hit Jev on every word / state update. - Combine them. LLM for deep reasoning. Jev for the snap decisions inside the loop. Use case: voice-controlled browser (transcript + page state → click/scroll probabilities) and smart paste (invoice text maps into the right expense fields as you paste). Full episode:

I’m not at all surprised so many enterprise AI deployments fail. So many things have to go right for an agent to make it to production & if one cog in the machine breaks timelines are pushed back (best case scenario) or leadership’s trust is eroded (worst case scenario). Everything that has to work: 1) Scope has to be clear - One workflow with a named business owner, a baseline metric, and a dollar outcome. - Autonomy matched to blast radius: suggest, then act with approval, then act autonomously. Earn each step with data. - Kill criteria defined up front. - Clarity around the current way of working/process and what the desired future state looks like. 2) Data & system access has to configured and trustworthy - Read/write access to systems of record, legacy APIs, and unstructured sources. - Service identities, SSO, and permission models that work for a non-human actor. - Data quality and freshness. Bad source data becomes confident bad output. 3) Evals must be core part of infrastructure - Golden set built from real historical cases, including edge cases and adversarial ones. - Success measured against the human baseline, not against perfection. - Regression suite on every prompt, model, or tool change. 4) Architecture and reliability are sound - Deterministic scaffolding around the LLM. Use code for anything that can be code. - Well-designed tools, timeouts, and fallbacks. - Model abstraction so you can swap providers. -Latency and cost budgets per task, plus rate-limit handling at scale. 5) Security is buttoned up AF - Least-privilege agent identity and scoped tool permissions. - Prompt injection and exfiltration defenses, especially with untrusted inputs (emails, documents, web). - Full audit trail, tenant and data isolation, residency, and vendor DPAs. 6) Governance and risk considered from the jump - IT, legal, compliance, and finance sign-off. - Regulatory fit (SOX, HIPAA, GDPR, EU AI Act, and industry-specific rules). - Human-in-the-loop thresholds, a kill switch, and one accountable owner for agent behavior. 7) Observability and operational rigor - Step-level tracing of every decision and tool call. - Monitoring for cost, quality drift, and failure clusters. - Versioning and rollback for prompts, models, and tools. - A feedback loop from human corrections back into evals. 8) The conditions for adoption are ripe - Executive sponsor with budget authority, plus the frontline process owners involved from the start. - Agent embedded where people already work (Slack, Salesforce, Outlook), not a new destination. - Clear escalation paths and honest handling of job-displacement anxiety. - Incentives aligned so the team benefits from the agent working. 9) Economics underwritten and measured - Fully loaded cost per completed task (inference, infrastructure, review labor, maintenance) against the human cost. - ROI measured on the baseline set before launch, not estimated after. - Understood if it’s part of experimental, efficiency, innovation, or infrastructure budget Until you’ve truly spent time in the guts of an enterprise, you don’t realize just how hard meaningful change is & just how much has to happen beyond raw frontier intelligence.

The most successful people I know are either militant about their time or super liberal with it. The first group audits their time down to the minute. “Why can’t this be an email” or “This meeting doesn’t need to be recurring” is a core part of their vernacular. They view wasted time as wasted potential, so the misuse of it makes them sick. The second group treats their time based on vibes. They carve out large chunks for unstructured brainstorming, knowledge sharing and relationship building. Life without serendipity and connecting that doesn’t feel transactional isn’t a life worth living. One group isn’t better or worse, and this distinction is more so ends of a spectrum vs. a binary switch, but really interesting to observe nonetheless.

I’m not at all surprised so many enterprise AI deployments fail. So many things have to go right for an agent to make it to production & if one cog in the machine breaks timelines are pushed back (best case scenario) or leadership’s trust is eroded (worst case scenario). Everything that has to work: 1) Scope has to be clear - One workflow with a named business owner, a baseline metric, and a dollar outcome. - Autonomy matched to blast radius: suggest, then act with approval, then act autonomously. Earn each step with data. - Kill criteria defined up front. - Clarity around the current way of working/process and what the desired future state looks like. 2) Data & system access has to configured and trustworthy - Read/write access to systems of record, legacy APIs, and unstructured sources. - Service identities, SSO, and permission models that work for a non-human actor. - Data quality and freshness. Bad source data becomes confident bad output. 3) Evals must be core part of infrastructure - Golden set built from real historical cases, including edge cases and adversarial ones. - Success measured against the human baseline, not against perfection. - Regression suite on every prompt, model, or tool change. 4) Architecture and reliability are sound - Deterministic scaffolding around the LLM. Use code for anything that can be code. - Well-designed tools, timeouts, and fallbacks. - Model abstraction so you can swap providers. -Latency and cost budgets per task, plus rate-limit handling at scale. 5) Security is buttoned up AF - Least-privilege agent identity and scoped tool permissions. - Prompt injection and exfiltration defenses, especially with untrusted inputs (emails, documents, web). - Full audit trail, tenant and data isolation, residency, and vendor DPAs. 6) Governance and risk considered from the jump - IT, legal, compliance, and finance sign-off. - Regulatory fit (SOX, HIPAA, GDPR, EU AI Act, and industry-specific rules). - Human-in-the-loop thresholds, a kill switch, and one accountable owner for agent behavior. 7) Observability and operational rigor - Step-level tracing of every decision and tool call. - Monitoring for cost, quality drift, and failure clusters. - Versioning and rollback for prompts, models, and tools. - A feedback loop from human corrections back into evals. 8) The conditions for adoption are ripe - Executive sponsor with budget authority, plus the frontline process owners involved from the start. - Agent embedded where people already work (Slack, Salesforce, Outlook), not a new destination. - Clear escalation paths and honest handling of job-displacement anxiety. - Incentives aligned so the team benefits from the agent working. 9) Economics underwritten and measured - Fully loaded cost per completed task (inference, infrastructure, review labor, maintenance) against the human cost. - ROI measured on the baseline set before launch, not estimated after. - Understood if it’s part of experimental, efficiency, innovation, or infrastructure budget Until you’ve truly spent time in the guts of an enterprise, you don’t realize just how hard meaningful change is & just how much has to happen beyond raw frontier intelligence.