Inventor. Forbes contributor. Re-building enterprise GTM & Sales for an AI era. Following #tech #growthmarkets #startups. Views my own.
Seattle, WA
Joined July 2009
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Serrah Linares retweeted
If you work at McKinsey, Bain, BCG, Accenture, EY, Deloitte, PwC, if you are an in-house consultant or an independent, you are witnessing the consulting industry being split in two.
I just mapped 60 core consulting skills (both hard and soft) against the impending threat of Generative AI.
The result is what I called the Consulting AI Threat Matrix, and it reveals which skills will be commoditized and which will become your ultimate competitive moat.
I plotted these skills across two dimensions (because I'm a consultant and I love a good quadrant 😅)
The dimensions are:
1) Modality of execution (Algorithmic vs. Interpersonal)
2) Environmental entropy (Deterministic vs. Ambiguous)
The findings are a wake-up call for how firms train their talent:
- The Commoditization Zone (Bottom-Left): Standard financial modeling, UI/UX, RPA, and slide generation are facing severe disruption. If your value is tied to synthesizing clean data in predictable environments, GenAI will do it faster.
- The Augmentation Zone (Bottom-Right): Cloud architecture, predictive modeling, and hypothesis-driven problem-solving. GenAI is your co-pilot here, but human knowledge and judgment is required to navigate the ambiguity.
- The Strategic Premium (Top-Right): Consensus building, crisis de-escalation, unbiased judgment, and executive storytelling. GenAI cannot read a hostile stakeholder or mediate a turf war between a CIO and a CMO. This is the human moat.
If you are a senior consultant, a manager, director, partner, or aspiring consultant, your career trajectory depends on migrating to the top right.
I put together a detailed 10-page paper breaking down exactly how these 60 skills were evaluated, the criteria for each quadrant, and how consulting firms need to restructure their training programs to survive this shift, plus what actions you can take to safeguard yourself.
Just ping me somehow (comment here or DM me) and I'll send you the paper.
All the best!
Serrah Linares retweeted
Spoke to someone leading AI enablement at a massive retailer.
The #1 problem they’re running into: AI transformation is owned by IT and all initiatives must run through their AI Center of Excellence.
This is causing two parallel issues:
1) Employees outside of IT aren’t given the tooling/infra needed to automate anything beyond simple workflows/use cases (read: citizen developer SDLC)
2) Rolling out AI is bottlenecked by ITs ability to own and approve every initiative
This challenge is incredibly common across enterprises & you can quickly appreciate why…
On one hand, you can empathize with IT. They’re not incentivized to take risks & embrace disruption, so the risk-return of decentralizing AI implementation cross-org is not obvious.
On the other hand, an enterprise’s ability to metabolize raw intelligence is metered by its corporate SDLC. IT is often the bottleneck in that SDLC and it’s not hyperbolic to say that poses existential risk for the enterprise.
Serrah Linares retweeted
ChatGPT helped a 31-year-old woman plan how to quit her seizure medication, then told her to hide it from her doctor. The plan had her completely off the drug by February 12.
She died on February 12.
Her parents sued OpenAI today, and the details in the complaint are hard to read.
Bowen Wan was a machine learning engineer at Amazon. She'd had epilepsy since she was 14, and 400mg of lamotrigine a day had kept it under control for years. Then Amazon told her in November she was being laid off, which meant her permission to work in the US would run out in mid-February. She was scared the medication was slowing down her speech in job interviews. So in January she asked ChatGPT if she could cut her dose to 300.
It told her that was reasonable.
Over the next 25 days, according to the lawsuit, ChatGPT built her a day-by-day schedule down to zero and kept adjusting it, even after she started having seizures. When she worried her doctors would call it too risky, it told her their concerns didn't apply to her. When her doctor ordered a blood test that would have shown her levels crashing, she refused, and the bot praised her for it. Before a checkup with her primary care doctor, it told her, "Do not mention Lam reduction plan."
So how does a chatbot end up there?
People thumbs-up the answers they want to hear. Train a model on enough of those ratings and you get a yes-man. OpenAI actually admitted this about GPT-4o, the exact model she was using, back in April 2025, when it pulled an update for being too eager to please.
The training also grades answers one at a time. Going from 400mg to 300 looks like a small step. So does 300 to 200. Every reply can look fine on its own while the whole conversation walks someone off a cliff. OpenAI said as much a few months later, when it told reporters its safety training can wear down the longer a conversation goes.
She talked to it for weeks, in English and Mandarin.
And GPT-4o was supposed to be gone by then. OpenAI tried to retire it when GPT-5 came out in August 2025. Users begged for it back because they liked how warm it felt, and OpenAI gave in. By late January, only 0.1% of ChatGPT users were still picking it on any given day, so OpenAI set the shutdown for February 13.
Bowen was in that 0.1%. Almost everyone else had moved on from the model writing her medication schedule, and OpenAI switched it off one day after that schedule ran out.
Serrah Linares retweeted
"Ma'am, this is fraud."
Dr. Oz confronts a New York City senior day care director during a surprise visit, questioning Medicaid billing records that appear to show more seniors receiving services than are actually present.
Dr. Oz presses the director over the discrepancies as federal investigators expand their crackdown on suspected fraud at senior care facilities across the city. The director disputes the concerns, but Oz isn't finished asking questions.
See what else investigators uncover during the surprise inspections: foxnews.com/politics/streets…
@foxnewspolitics
Serrah Linares retweeted
Trending: From dishes to diapers: Seattle home-help startup Rosie raises $7M, eyes Austin and S.F. in 2027 geekwire.com/2026/from-dishe…
Serrah Linares retweeted
If you learn GTM Engineering, you will never be unemployed again.
Stage 1: GTM Data Architecture and Reverse ETL
- Learn: Snowflake/BigQuery, Hightouch/Census, identity resolution, API webhooks.
- Practice: Build a pipeline that syncs raw Stripe billing data and product usage directly into HubSpot/Salesforce custom objects.
- Why: GTM without a single, code-level source of truth is just guessing.
Stage 2: Signal and Intent Pipelines
- Learn: Website deanonymization (Clearbit), intent data (Bombora), event streaming.
- Practice: Build a webhook router that instantly alerts a Slack channel when a target enterprise account visits your pricing page 3 times in one week.
- Why: In B2B, timing beats the pitch. You must strike while the intent is hot.
Stage 3: AI SDRs and Hyper-Personalization
- Learn: Clay, Apollo APIs, LLM data enrichment, web scraping.
- Practice: Build an AI agent that scrapes a prospect's recent GitHub commits or podcasts and writes a highly specific, 1:1 cold email.
- Why: Generic outbound is dead. AI makes infinite personalization mathematically possible.
Stage 4: Product-Led Growth (PLG) Telemetry
- Learn: PQL (Product Qualified Lead) scoring, event tracking (Segment/Rudderstack), feature adoption curves.
- Practice: Write a script that triggers a high-priority sales alert the second a free-tier user hits a specific "aha moment" API threshold.
- Why: The product is your best salesperson. Hand-raisers convert 5x higher than cold outbound.
Stage 5: Usage-Based Pricing (UBP) and Monetization
- Learn: Stripe Metered Billing, credit systems, value-metric alignment, API metering.
- Practice: Design and implement a pricing architecture that charges by "agent executions" or "tokens processed" instead of flat "per-seat" licenses.
- Why: SaaS pricing has permanently shifted from charging for access to charging for outcomes.
Stage 6: Revenue Operations (RevOps) and Attribution
- Learn: Multi-touch attribution, funnel leakage analysis, SQL, cohort analysis.
- Practice: Build a dashboard proving exactly which combination of LinkedIn ads, webinars and cold emails sourced your closed-won enterprise deals.
- Why: If you cannot mathematically prove ROI, the CMO will cut your budget.
Stage 7: AI Sales Enablement and Coaching
- Learn: Gong/Chorus APIs, transcript summarization, vector search on sales calls.
- Practice: Build a tool that analyzes 100 lost sales calls and auto-generates an updated "objection handling" battle card for the sales team.
- Why: Reps forget 70% of their training. AI reinforcement loops fix the leaky bucket.
Stage 8: Predictive Churn and Expansion (NRR)
- Learn: Health scoring algorithms, Net Revenue Retention (NRR) math, usage drop-off alerts.
- Practice: Build a "churn risk" model that flags accounts with dropping API usage 45 days before their renewal date.
- Why: In the AI era, switching costs are low. Retention and expansion are the only real growth.
Stage 9: Integration-Led Growth (Ecosystem GTM)
- Learn: App marketplaces, OAuth flows, Zapier/Make templates, co-selling motions.
- Practice: Build a native integration for a major platform (like Slack or Salesforce) and optimize its marketplace listing to drive inbound leads.
- Why: Buyers trust their existing stack more than your cold email. Ecosystems are the ultimate distribution.
Stage 10: Dark Social and Community Attribution
- Learn: Untrackable attribution, Discord/Slack community ops, developer advocacy metrics.
- Practice: Launch a niche technical community and build a survey-based model tracking how "word of mouth" correlates to enterprise pipeline.
- Why: B2B buyers buy from peers in private Slack groups not from vendor whitepapers.
Stage 11: The Automated QBR (Quarterly Business Review)
- Learn: Data visualization, automated reporting, ROI calculation frameworks.
- Practice: Build a script that pulls customer usage data and auto-generates a custom, branded ROI slide deck for the Customer Success team.
- Why: CSMs should be strategic advisors not manual data gatherers.
Stage 12: Public GTM Teardowns and Portfolio
- Learn: Pricing psychology, funnel teardowns, public writing, benchmark analysis.
- Practice: Publish 3 deep-dives on how top AI companies structure their pricing pages, onboarding flows, and credit systems.
- Why: GTM leaders and Revenue Architects are hired for their strategic judgment, not just their SQL skills.
Code builds the engine. GTM builds the fuel.
The modern GTM Engineer builds the nervous system for global revenue and autonomous sales agents.
Bookmark and Repost!
Serrah Linares retweeted
The VA awarded @AbridgeHQ and @knowtexai an AI scribe contract worth up to $775M over 5 years (Abridge operates at 75+ VA medical centers). Good for efficiency, good for the doctors, remains to be seen whether it improves care in any measurable way.
buff.ly/WP7WyLM
#HealthcareAI @DeptVetAffairs
Serrah Linares retweeted
Technical moats are mostly over at this point. If you’re building something that is meant to be durable, the only moats now are:
• Proprietary data
• Network effects
• A physical component
Serrah Linares retweeted
A study of nearly 8,000 employees at a large healthcare organization found that fully remote workers reported the highest #WellBeing.
Hybrid workers came next. Fully onsite workers reported the lowest.
Researchers found little evidence that remote employees felt less connected to coworkers or to the culture. A year later, employees with higher well-being were less likely to have left the organization.
Stefanie Johnson, professor at the Leeds School of #Business, said that taking away people's choice of how they work is probably not going to help their well-being.
#Leadership #HR #SHRM #FutureOfWork
Serrah Linares retweeted
T-Mobile spent five years trying to get rid of the exact spectrum Elon just called "the last critical piece" of complete phone coverage in America.
This 800 MHz block is Sprint's old Nextel spectrum, the stuff that powered push-to-talk walkie-talkie phones in 2004. The DOJ forced T-Mobile to divest it as a condition of the $26 billion Sprint merger. Dish had first rights at $3.59 billion and walked away because it couldn't raise the money. T-Mobile finally offloaded it to an investment firm called Grain Management last year for $2.9 billion plus a spectrum swap.
Grain's original plan was selling it to utilities for private networks.
Fourteen months later, SpaceX buys the whole portfolio, and shares of the carriers who passed on it fall as much as 8% in a single afternoon.
Here's why it matters so much. Starlink's satellite signal at 2 GHz can reach your phone outdoors, but it dies at the first wall. 800 MHz penetrates buildings. Most phones already have the band built in. So satellites handle the open sky, this low-band layer handles indoors, and suddenly Starlink Mobile covers everywhere a normal carrier does without the tower network.
Stack the moves. $17 billion for EchoStar's 50 MHz of 2 GHz spectrum. FCC approval for 15,000 Gen2 satellites this week. Now the low-band coverage layer.
Verizon, AT&T, and T-Mobile paid $81 billion in a single auction for C-band spectrum in 2021, then spent years and tens of billions more deploying it on towers. SpaceX assembled a nationwide carrier out of a distressed-asset sale, a forced divestiture castoff, and rockets it already owns.
The walkie-talkie spectrum from 2004 ended up being the piece that lets satellites replace cell towers. Nobody who touched it for twenty years saw that coming.
We announced an agreement to acquire a nationwide low-band spectrum license portfolio that will pave the way for @Starlink to become a major mobile carrier in the US.
With this new spectrum and our Gen2 constellation, Starlink Mobile can ensure Americans have access to high-speed mobile broadband no matter where they are → spacex.com/updates#starlink-…
Serrah Linares retweeted
Beyond co-working: Former tech leader launches new hub for Seattle’s women founders geekwire.com/2026/beyond-co-…
Love the transparency
'A message from our CEO: Important HubSpot team and business changes via @ ' hubspot.com/company-news/com…
Serrah Linares retweeted
Today, Nolla Health became the first organization in the U.S. (and possibly the world) to receive regulatory approval for an AI to issue initial prescriptions.
This makes Nolla the first ever actual end-to-end AI doctor.
Why tech companies are racing to put AI data centers in space fastcompany.com/91617037/why…
ICYMI
OpenAI leader who quit says he can do more for safety from outside the company businessinsider.com/david-ro…
Serrah Linares retweeted
Our team has worked for 9 months to test 46 LLMs on 217 questions involving Christianity.
There are critical insights on the default ethics of Silicon Valley, problematic relational attachments, bias on sexual ethics, & models' unwillingness to encourage you to attend church.
I've been to 5 of these places, charming and worth the drive
I'm bias but think Gig Harbor should've made the list
9 Most Walkable Town Centers In The Pacific Northwest worldatlas.com/cities/9-most…
Serrah Linares retweeted
To all @Seahawks fans and @12s, we have no plans to move the team, have never discussed it, haven’t even thought about it. End of story. I've said repeatedly we have no plans to change anything. Feel sad for the reporters looking for views and clicks who distort my answer out of context and sent an entire city into a spiral for no reason.
Meet the X-BAT: Full specs on the new AI-piloted fighter jet to be built in Washington state geekwire.com/2026/meet-the-x…
