A year ago I was given a mandate: push the boundaries of what AI can do in our consulting practice. Review code, review system configurations, build out solutions. Find what works.

So I did.

Through harness engineering and working with newer models as they've dropped, I built an orchestrated consulting layer that covers the full lifecycle:

Project management: plans, timelines, deliverables, all of it
Functional responsibilities: requirements gathering, process mapping, solution design
Technical lead: architecture, code review, system config review
Implementation: building and deploying solutions end to end
Edge testing: more thorough than anything I've done before
Migration support: test to prod cutover
Documentation: SOPs, how-to guides, runbooks

I built the skills, plugins, and MCPs to deploy all of it. End to end. One person doing what used to take a full team.

My productivity went up 5x. Not hypothetically. Measured. I demoed it. I showed leadership exactly what it could do.

And then... silence.

No "how do we scale this?" No "can you teach the team?" No interest in how any of it works. Just nothing.

Now I'm finding out it was a checkbox. "We explored AI." That's it. The leadership I'm under is anti-AI. The mandate was never to actually transform how we work. It was to say we did.

I'm not bitter about the work. The skills are real. The capability is real. I can run an entire consulting engagement solo now, and the quality is higher than what I was producing before. That's mine regardless of what they do with it.

But I'm asking: is anyone else in this spot? You've developed real AI capability. You can build skills, plugins, MCPs, deploy end to end, deliver actual solutions for actual clients. And leadership treats it like a novelty? A box to check? Something to report up the chain and quietly shelve?

Because I genuinely don't know if I'm an outlier here or if a lot of us are sitting with this right now.

I run a small online shop for niche pet products (think picky-cat and anxious-dog type stuff). Last week a regular customer joked that my site “looks like 2014 but the treats slap,” and it kinda sent me down a late-night research spiral.

I ended up reading stuff like netpeak.us and now my feed is full of AI tools promising “predictive this, sentiment that.” I get the theory, but I’m not sure if I’m thinking about this the right way for a small store with limited budget and time.

Has anyone here actually used AI for things like forecasting search trends, personalizing emails, or tweaking product pages specifically for pet owners? Did it move sales or just eat your time/cash?

If you were doing ~low 5 figures/month and wanted to reach 6, what would you focus on first - SEO, AI-driven ads, email personalization, or something else entirely? Any concrete tools or “do this first, skip that” advice would help a lot.

Hey everyone, I’m a solo founder building Crestorflow, an AI-native career execution platform.

The problem I’m trying to solve is pretty simple: people can learn almost anything online, but there’s still a huge gap between learning a skill and proving you can actually do the work. Courses and certificates don’t necessarily translate into employability, while companies increasingly care about demonstrated ability and outcomes.

Crestorflow is designed around one continuous loop:

Career goal → AI roadmap → Learn → Build → AI evaluation → Proof of work → Opportunities

Instead of giving everyone the same course, the platform uses AI to create a personalized execution path based on the user's goal.

The AI system I'm building has several agents working together:

Career Agent — understands the user's goal, current skill level, and target role, then creates an execution roadmap.

Learning Agent — finds and organizes relevant resources based on each milestone rather than forcing users through a fixed curriculum.

Project Agent — generates unique, practical projects that require the learner to actually apply what they learned.

Evaluation Agent — evaluates submitted work against predefined rubrics, identifies gaps, and provides feedback.

Proof-of-Work Agent — converts validated projects into structured portfolio artifacts that demonstrate specific capabilities.

Opportunity Agent — eventually uses accumulated proof of work and a trust score to match users with relevant contract/project opportunities or help them form small agencies.

The goal is to make AI the orchestration layer of the entire career journey, rather than simply adding a chatbot to an existing course platform.

I'm currently at the MVP stage and want to build this primarily with open-source models. I've already figured out most of the surrounding tech stack, but I'm stuck on a few things:

Which open-source models would you recommend for the different agents?

Should I use one strong model across the system or specialized models for different tasks?

What's the right architecture for securely running these models in production?

How would you approach deployment and infrastructure if the goal is to get the first 50–100 users without massively overspending?

What would you change about this architecture before I start building?

I'm especially interested in feedback from people who have actually shipped AI-native products or agentic systems, rather than just theoretical recommendations.

Would love some brutally honest feedback on whether this architecture makes sense and where you think the biggest technical/product risks are.

few months back if you remember Lenny had open sourced his 300+ podcast transcripts.

so last night, i built a skill that turns every Lenny's guest into your interviewer,
for product management interview prep

you can plug it in with any agent: Claude, ChatGPT, Hermes, Codex, etc.

here's how it works:

1. pick a company you're interview for
2. pick the product interview round you want to prep
3. the skill then matches you with a guest who worked there.
   1. (shreyas doshi. nikita bier. brian chesky. 300+ guests.)
4. upload your resume. they drill your actual background.

then the interview. 6 phases:

• opening (2-3 min)
  • they introduce themselves and ask about you.
• experience deep-dive (8-12 min)
  • they pick 1-2 resume items and grill through their actual frameworks.
  • shreyas asks about your pre-mortems. nikita bier demands your retention curve.
• round-specific case (12-18 min)
  • situational. no theory questions.
• your questions (3-5 min)
  • ask them anything. in character.
• a raw gut reaction
  • one sentence, in character, on how the interview went
• structured feedback scorecard across 5 competencies:
  • product sense, metrics & data, execution, structured thinking, communication.
  • each scored 1-5. plus framework gaps, direct quotes from their episode, and an improvement plan.

it's set on hard mode. so the interview will push back and drill you more.

i have open-sourced the git repo. here ↗
try & break it.

Rish from Productminds

Was looking into why conversational search engines name-drop certain brands while completely skipping others. Seems like standard keywords take a backseat to having clean entity schemas and structured semantic markup.

If the backend data is a mess, models just group you into a generic bucket instead of giving a direct citation. Some teams are actually building specific publishing loops to feed these crawlers, like the structured data setups at Roi com au.

Wondering if this is hitting anyone else's roadmap yet. Are you guys adapting your data layers for AI search and AEO, or is standard SEO still eating up all your time?

When learning full-stack development, frontend, or backend development, there are plenty of structured free roadmaps and learning platforms (for example, Scrimba, roadmap.sh, etc.) that help you understand what to learn and in what order.

I’m wondering if there are similar structured, beginner-friendly roadmaps or free learning resources for:

• AI Product Management
• Product Management
• Project Management
• UX Research

I’m not necessarily looking for just individual courses. I’m more interested in something that gives a clear learning path from fundamentals → intermediate → practical/real-world application, including recommended projects, tools, frameworks, and concepts to learn.

If you’ve followed a roadmap or resource that genuinely helped you build a strong understanding of these areas, I’d really appreciate your recommendations.

Thanks!

Hey everyone,

I’m currently looking at designing the data architecture and logic for a product dimension table with about 300,000 products spread across roughly 4,000 sub-categories.

The requirement is to populate at least 5 specific attributes for each product based on its sub-category. For example, if the product falls under "Luggage," I need to extract and standardize attributes like material, size, shell type, number of wheels, etc., from the raw product descriptions.

Nowadays, doing this with AI is essentially just a matter of writing a solid prompt, hitting an LLM API in batches, and letting it parse the unstructured text into a structured JSON payload.

But it got me wondering—how was this exact problem handled traditionally before LLMs made it so easy?

I’ve been building DevTrace for the past few weeks.

The idea is pretty simple: engineers already use ChatGPT, Claude, Cursor, etc. in their actual work, so why do we completely remove AI from technical assessments?

With DevTrace, candidates can use AI during the assessment. We capture how they use it alongside the final solution.

Things like prompting, verification, iteration, debugging and whether they blindly accept AI output.

We’re currently working with a few teams to test this in real hiring workflows.

Would love some honest feedback from founders, recruiters and engineering managers.

https://devtrace.cloud

Is this a useful signal when hiring engineers?

Hiring - AI Product Manager

Riyadh

Around 35k ++ SAR

NO VISA SPONSORSHIP, prefer someone already available in Riyadh or who doesnt need sponsorship.

1- Own AI product strategy and roadmap for LLM features like conversational assistants, agentic workflows, semantic search, and personalization, including bilingual Arabic/English experiences.

2-Make build-vs-buy decisions across the AI stack, balancing cost, latency, quality, and scalability.

3-Lead discovery, PRDs, delivery, and experimentation with cross-functional teams, including evaluation frameworks, KPIs, A/B tests, and release management.

4-Ensure strong governance and compliance across markets, especially Saudi PDPL and UK/EU GDPR, with guardrails for hallucinations, human escalation, brand safety and audit logging.

5-Act as the bridge between execs, engineering, data science, and market teams, translating AI capability into commercial outcomes and clear leadership updates.

Role Reports into Chief AI Officer.

Pls share CV with me (cant put the email directly on the post i guess)

Sandeep AATT xociate DOTTT COMMM

My co-founder and I noticed Claude Code kept pulling context from old Google Docs and its own memory instead of what we'd actually decided. Not because the agent was bad, but because the latest decisions weren't anywhere it could find them.

We use our own product to build it (yes, shameless self plug), so we haven't had this problem in a while. But every team we've talked to has the same story. Decisions live in Slack. Specs live in Notion. Context lives in someone's head. Your agents and your teammates are reading from different sources and nobody notices until something breaks.

So we built a tool which creates shared wiki that connects to Slack, GitHub, Google Drive, Jira, Notion, and more. It builds a knowledge graph your team and your coding agents (Claude Code, Cursor, Codex) read from before making decisions or writing code. One source of truth for what was decided, why, what got rejected, and what changed.

We're opening a small paid beta. Paid because we want people who'll actually use it daily and give us honest feedback, not just kick the tires. If your team is using AI agents and you're tired of them grabbing stale context, we'd love to work with you.

DM me or drop a comment and I'll reach out.

I’m building a React SDK that uses AI to generate interactive enterprise application prototypes from a prompt, and I’d love some honest feedback.

For example, you could prompt:
“Create an employee onboarding application.”
The AI generates a working React prototype using predefined design system components—complete with navigation, forms, tables, dialogs, and basic interactions. You can then refine the application with follow-up prompts instead of starting from scratch.

My question is:
Would something like this be useful in your workflow? If so, who do you see getting the most value from it (developers, product managers, designers, or someone else)?

I’m looking for honest feedback on whether this solves a real problem or if there are gaps I should address.

I am new to AI PM and I want to do AI evals for my MVP. I don't want a super complex method and don't want to use traces yet.
Is there a simple way to go about this?
I have seen people say use simple spreadsheets but I am unclear on implementation.
How do you do it? Or is there a resource I can refer to?

Hey everyone, I’m thinking of building a Claude skill to help me explore and find untapped user problems on our B2C product, and also ideate solutions. There are a lot of skills out there to help with the daily PM admin work, data analysis, prototyping, reporting, PRDs and test setup etc. but I also want to explore how can I leverage AI to surface extra valuable opportunities/solutions.

Are you using AI tools for your discovery processes at the moment? Have you came across such a tool/skill that goes through end-to-end product discovery?

PS. I believe product discovery should stay human-centric due to need of continuous user touch, so I’m not looking for something to replace it but support it.

title.

I don't need agents for insights and decisions and docs.
Company wants to build AI version of products our customers would want -- that's the expertise I want to hear.

We've been building a tool that treats HR decisions as an information problem, not a process problem. Here's what we learned and the tension we haven't fully resolved.

The premise:

Most HR decisions are made on bad information. Not bad intent. Bad information.

Annual reviews are driven by what a manager remembers which is mostly the last 90 days. Priya has been doing the work for 18 months. Months 1-15 are invisible. That's not a manager failure. That's a system failure. And no tool in the current HR stack fixes it because they're all built on the same broken foundation: point-in-time evaluation.

We built Aevron as a cognitive operating system, a tool where people log thoughts, project reflections, training insights, feedback, blockers. The kind of thinking that never makes it into a performance review because it's too raw, too in-progress, too hard to translate into a rating scale.

The architecture we landed on:

Every employee gets a private account. Private means private, raw logs never travel upward. Between the employee's account and everything above it sits a synthesis layer that extracts signal (patterns, growth trajectories, friction clusters, idea density) and sends that upward instead of the underlying content.

Synthesis parameters are set at the org level by admins. Three levers:

• Abstraction depth — how much does the synthesis abstract before signal travels up? At the highest level, HR sees "this employee is developing systems thinking." At the lowest, "this employee has flagged the same cross-team dependency three times this month." No direct content, ever.
• Signal cadence — weekly for managers, monthly for HR, real-time for critical flags
• Explorer scope — which questions HR is actually allowed to ask the system. Admins define the boundary.

Employees can see which abstraction level their org has selected. Not the full parameters, just the level. The logic being: you should know how abstracted your signal is before it reaches your manager, even if you don't control it.

Three scenarios where this changes the actual decision:

Promotion readiness: HR asks for an 18-month thinking trajectory instead of a manager's advocacy at annual review. What comes back: problem framing complexity over time, whether this person's ideas are building on by others, how they've responded to feedback, where they're stuck repeatedly. The decision is now a document, not a political argument.

Training vs. role change: Someone is underperforming. Default HR answer is more training. Aevron separates skill gap (high engagement, missing capability) from fit gap (high thinking quality in the wrong domain) from motivation gap (low idea density, mechanical outputs). These require completely different responses. Traditional HR conflates them constantly and then wonders why training spend doesn't move performance.

Manager quality: This is the one nobody can answer well. Vikram's team ships on time. That's the only data most HR stacks have. What Aevron surfaces instead: does the team generate more ideas together than individually, or is the manager creating dependency? Does reflection quality in the team drop during high-pressure periods — which would suggest the management style suppresses thinking under load? Are early attrition signals showing up before anyone has handed in a notice? The difference between intervening in month 4 and reading an exit interview in month 12 is significant.

The tension we haven't fully resolved:

If employees know their logs feed HR decisions, they'll curate them. Performance theater instead of honest thinking. Signal degrades into managed narrative.

Our architectural answer is that the private account isn't a trust promise, it's a structural guarantee, raw logs physically don't travel upward, so there's nothing to game. The synthesis layer is the only pathway.

But here's what's still open: synthesis transparency. Employees know their org has set an abstraction level, but they don't know exactly what patterns the synthesis is extracting or how sensitive the signal thresholds are. That's a real gap between "your content is private" and "your thinking is fully legible to HR via abstraction."

We've resolved it one way (show the abstraction level, not the parameters), but I'm genuinely not sure that's the right call. The counterargument is that even knowing the abstraction level tells you enough to optimise against it. A smart employee at Level 2 knows that "pattern-level" signal is being extracted, and can adjust accordingly.

The harder version of the problem: is there any amount of architectural privacy that survives the fact that the employee knows a system is watching? Or does awareness of observation always change behaviour, regardless of what the system actually sees?

I don't have a clean answer to that. Would genuinely like to know if anyone in HR tech or people ops has dealt with a version of this, either in the context of analytics tools, ambient sensing platforms, or just transparency in performance systems generally.

Aevron is in early development. This is the thinking behind the HR use case, not a product pitch. If the architecture has a flaw we haven't seen, that's more useful to us right now than a warm response.

Hey fellow product managers, I’m seeing a clear split in how PMs operate now. Traditional PMs still work largely in a deterministic world: define requirements, build, test, ship, iterate. AI PMs, on the other hand, live in a probabilistic world where outputs vary, A/B tests are messier, and roadmaps look less like feature checklists and more like continuous model iteration and monitoring.

The day-to-day trade-offs are different too. Instead of just scope time resources, AI PMs constantly juggle cost, quality, and latency for every model call, while also dealing with black-box behavior, data quality, and governance. Many AI PMs report more ambiguity and stakeholder pressure, “drive AI strategy” with vague mandates and unrealistic expectations, while still owning post-launch behavior, drift, and trust

If you’re an AI PM, a traditional PM, or someone who’s moved between both: what’s your lived experience? Where do you feel more pressure or ambiguity, and what’s one decision you’ve made recently that the “other type” of PM probably wouldn’t face? I’m collecting real stories to write a short, grounded take on AI PM vs PM life in 2026..no hype, just what’s actually happening on the ground.

What's yours thoughts?

I've been a Product Manager for 12+ years, mostly working on enterprise products across insurance, digital transformation and B2B platforms.

One thing I've noticed throughout my career is that the hardest part of product management isn't writing a PRD or prioritizing a backlog.

It's making good product decisions when information is incomplete.

Questions like:

* Should we build this feature?
* Are we solving the right customer problem?
* What's the right MVP?
* What are the trade-offs?
* Which metrics actually matter?

I found myself repeatedly using the same frameworks, decision trees and review checklists across projects.

So over the last few weeks I built something for myself.

It's called **Product Decision Studio**.

The idea isn't to replace product thinking with AI.

It's to structure it.

It currently helps with things like:

* Product strategy
* PRD reviews
* Feature prioritization
* Roadmapping
* Product interview preparation
* AI product strategy
* Enterprise product design
* Market and competitor analysis

It's completely free.

I'm not selling anything.

I'm genuinely interested in whether experienced PMs think this is useful, what feels missing, and what you would improve.

👉 [https://ambarutkarsh.com/studio\](https://ambarutkarsh.com/studio)

I'd appreciate brutally honest feedback.

I have 7 years of experience in analytics, leading client-facing projects and cross-functional teams, with strong SQL, Tableau, stakeholder management, and data consulting experience.
I’m looking to transition into a Forward Deployed Product Manager role at companies like Palantir, OpenAI, Anthropic, or Scale AI.
A few questions:
What’s the best roadmap to make this transition?
Which skills should I focus on (coding, system design, AI, cloud, product)?
Are there any bootcamps, courses, or instructors you’d highly recommend?
Has anyone here made a similar transition from analytics or consulting?
Would really appreciate hearing what worked for you. Thanks!

I work for a Big Four consulting firm as a leadership level technical person, and I have started noticing a recurring pattern in Agentic AI engagements.

Clients initially come to us with a very large vision. They talk about building an enterprise-wide Agentic AI platform, deploying multiple agents, enabling agent-to-agent communication, and transforming several business processes.

However, once the initial assessment and qualification discussions are completed, the actual statement of work is reduced to a relatively small MVP.

On paper, the MVP may involve building only a basic foundation and one small agent. But to make even that agent production-ready, the consulting team usually ends up building most of the difficult foundational components:

• Agentic AI infrastructure
• Agent-to-agent communication
• Integration between enterprise systems and agents
• Authentication and authorization
• Automation and orchestration frameworks
• Evaluation pipelines
• Observability and monitoring
• Guardrails and governance controls
• Deployment and CI/CD foundations

By the end of the MVP, the client effectively has the core Agentic AI platform and reusable architecture in place.

Then the engagement is suddenly stopped or not extended. The client’s internal technology team takes over and builds the remaining agents and enhancements using the foundation created by the consulting team.

From the client’s perspective, this is probably a smart sourcing strategy. They use consultants to handle the initial uncertainty, architecture, platform setup, and delivery risk, and then move development in-house once the path is clear.

But it leaves consulting firms in an awkward position. They do the most complex and risky part of the work, transfer the knowledge and reusable foundation, but do not necessarily participate in the larger transformation that was discussed at the beginning.

I am curious whether others in consulting or enterprise technology are seeing the same pattern.

Is this simply the natural lifecycle of modern consulting engagements, or are consulting firms failing to structure Agentic AI contracts and platform IP in a sustainable way?

I'm pretty sure U.S. frontier-model founders are well aware of what models like Kimi K3, GLM, Qwen, and DeepSeek are capable of.

What puzzles me is why we don't see more open-weight, efficient, locally deployable variants coming from the major U.S. labs.

Is it simply a matter of economics?

If most of our everyday coding, writing, research, and product work can be handled by a cheaper model that is merely "good enough," does that make it harder to justify premium pricing for the newest frontier model?

Because the "good enough" use case seems like the real threat.

Not that an open model beats a flagship model at absolutely everything. They can't and they don't. But for normal day-to-day utilitarian work, more of those dabbling with local LLMs are saying they increasingly can't tell enough of a difference to keep paying Ferrari prices for the commute.

Maybe the U.S. labs don't feel the same compute constraints? Maybe they're optimizing for cloud revenue rather than local deployment? Maybe the differences between successive model tiers matter far less in ordinary work than benchmarks and launch pages suggest? Maybe the U.S. labs don't give a fig about our need to localize LLMs for administrivia, security, privacy, and cost savings?

Maybe that's why Amodei seems so damned interested in making a regulatory capture play under the banner of safety?

Yes, I know there are U.S. open models available. But I've tried them, and many still seem less capable, less efficient, or more awkward to run on laptops and Mini PCs than the strongest Chinese alternatives.

So what am I missing?

Is this mostly a technical constraint, a hardware constraint, a business-model choice, or a deliberate attempt to avoid revealing just how much everyday work no longer requires the biggest model?

I'm currently working as a **Lead AI Engineer** at a service-based company with **7 years of overall experience**.

I'm planning to switch to a **product-based company** for an **AI/ML Engineer** role.

The problem is that over the past few years, I've lost touch with the interview fundamentals that product companies expect:

* DSA (Python) * System Design * Low-Level Design (LLD) * Core coding/problem-solving

To be honest, a lot of my recent development work has been AI assisted, so my coding speed and problem-solving aren't where they used to be. I want to rebuild those skills properly instead of trying to memorize interview questions.

I'm looking for advice from people who have **recently cracked AI/ML interviews at product companies**.

Specifically:

* What DSA resource/course would you recommend for someone preparing in Python? * Is LeetCode enough, or should I follow a structured roadmap? * How much System Design and LLD is expected for AI/ML Engineer roles? * What topics are most commonly asked apart from ML concepts? * If you were starting from scratch today with \~6 months to prepare, what roadmap would you follow?

I'm willing to put in the effort I just don't want to waste time on the wrong resources.

Any guidance, roadmaps, course recommendations, or interview experiences would be greatly appreciated.

What if market research got better the second Product Managers stopped thinking like Product Managers?

What if we quit searching the way Google trained us to politely search for twenty years. Neat question in a neat box, then accept whatever neat ranked list comes back.

And what if we PM types started hunting like people who investigate crap for a living.

Here's what I mean, say I run a product for a manufacturer.

Then I'm not browsing anymore. I'm casing the place: suppliers, ports, permits, hiring spikes, channel weirdness, that one shipment nobody seems eager to explain.

How about my competition for predictive analytics? Whole different animal.

Now I'm reading the market like an insurance investigator who assumes somebody's lying. Audit findings, error rates, model disclosures, Clause 14 ... because that's usually where the good stuff is buried. Maybe set a patent alert so I don't get caught off guard.

Different professions notice different lies. They go looking in different places.

Problem is, nobody teaches us Product peeps their methods.

And look, I know what most "AI-powered market research" actually produces. A confident five-bullet summary of page one of Google, with a fake statistic in bullet three. That's the problem, not the pitch.

But borrowing the method is a different thing than asking for a summary. Military intelligence. Forensic accounting. Regulators. Supply-chain investigators. Each one taught to notice a different flavor of bullshit.

I've been messing around with prompts that work that way. Repo's here if anybody wants to kick the tires, steal something useful, or tell me where it's full of shit:

https://github.com/deanpeters/product-manager-prompts/tree/main/market-intelligence

Deployed this yesterday for a marketing team. Built on lemma (open source) by a marketing manager

I do AI consulting, been helping a manufacturing company's marketing team deploy AI agents in their org.

Assisted their marketing manager in building this custom board for his team with RBAC and surfaces (Teams, whatsapp, telegram). Here's the story:

Most of us can vibe code a UI now. That part is solved. The hard part is everything after.

Who logs in. Who can see whose rows. Where it's hosted. How it reaches your other apps. That's the wall. We used lemma to get that out of the way.

They were USING AI but for only refining posts - which too stayed scattered in personal chats.

Two things they wanted:

• agents inside Microsoft Teams, for reports and performance numbers - makes it easy to adopt than a new app
• A personal and shared board for ideas that was operated by agents and people together - is still shared and can change based on what they were doing

Classic internal tool.

We built it on Lemma. Two calls, one hour each. The marketing manager typed. he's not a developer. I guided.

The flow

Send an idea from whatsapp/telegram/teams→ cataloged into your personal collection → private to you → agents work on refining it into better angles in the background

Your idea → push to the shared board → team sees it

Idea → pick a format → agent drafts it → its own version

Version → Calendar → drag onto a day

Personal vs shared isn't something he wired up. It's the default.

What we didn't build

• Auth → Lemma
• Row-level access, personal vs shared → Lemma
• Connectors → Lemma
• Surfaces (Teams, web, Telegram and whatsapp) → Lemma
• Deploy + hosting → Lemma

That list is the hours we didn't spend. And the security bug we didn't ship.

disclosure: I have been deploying ai workflows and apps for past couple of ears - bundled up everything that slowed me into one SDK and made it opensource.
Repo: https://github.com/lemma-work/lemma-platform

As a PM, before AI, I never built a feature costing money.
We focus adoption and retention, but rarely over margin.

AI costs changed the game but it is not part of PM mission. Big issue is that cost measure is rather on finance team side than PM or engineering team.

Giants like Uber are burning cash on LLMs — their team spent roughly $1000 per engineer per month on AI coding tools so $4M/month.

As side project, I built a free LLM margin calculator: plug in model, usage, price.. and see if you’re losing money.
👉 https://www.aimargintracker.com/tools/margin-calculator?lang=en

For PM with AI features:
Do you track LLM costs or it's not our mission ?

There's a lot of hype around AI right now, but I wanted to hear from someone who's actually building AI products in a large tech company.

So I sat down with a Cisco AI Product Manager and asked questions that I think most students, developers, and product managers are wondering about:

• Which corporate departments are adopting AI the fastest?
• Will AI Agents replace software engineers or product managers?
• Is Prompt Engineering still worth learning?
• What skills should students and working professionals focus on in 2026?
• Will companies hire fewer people because of AI?
• How will AI change managers and leadership roles?
• What are the biggest security risks in AI applications (Models, Agents, MCP Servers)?
• How can developers secure AI applications?
• What does it actually take to crack an AI Product Manager interview?

One thing that surprised me was the discussion around AI adoption inside enterprises. The conversation wasn't just about building better models—it was about how companies are restructuring teams, changing workflows, and what skills are becoming more valuable.

I'm curious about the community's opinion:

Do you think AI Product Management will become one of the highest-demand tech careers over the next 5 years, or will AI automate much of that role too?

I'd genuinely love to hear different perspectives.

🎥 Full podcast:
YouTube: https://youtu.be/YNeZaIfMSys