Devin Reed

Yo! I help SaaS marketers and Founders grow their audience, brand, and pipeline with strategic content. 

My portfolio
→ 2x Head of Content at billion-dollar startups (Gong, Clari)
→ Scaled Gong’s brand and content from $20M → $200M ARR 
→ Advised some the fastest-growing startups, including Notion, Wiz, AudiencePlus, and many more.

I also share everything I've learned through via my newsletter, cohort training, and videos series:

✅ The Reeder Newsletter: www.thereeder.co/newsletter

🍿 Reed Between The Lines show: youtube.com/@TheReederCo
(also available on Spotify and Apple)


Now that you're all signed up, let's make waves 🌊

Founder, Writer, Advisor

I create content strategies for fast-growing startups and solo creators who want to grow their audience, brand, pipeline, revenue, and income. 

-Content strategy
-LinkedIn strategy 
-Content marketing
-Outbound sales messaging
-Keynotes for industry and internal events 

✅  Website: thereeder.co
✔️ Email: devin@thereeder.co

B2B marketing is going through a brutal shift. Here's what 2026 is looking like after talking to 17 marketing leaders this year

1. "AI-first" strategy is big a** red flag
 Companies optimizing for efficiency over effectiveness will wonder why their content gets zero engagement. Turns out, when everyone uses the same AI tools, everyone sounds the same. Boring doesn't compound.

2. Paid ads are costing more and converting less and less 
This is what happens when there are no more "in-market" people left to target. So companies keep increasing ad spend, hammering the wrong message to the wrong 95%. The winners? They're investing in owned audiences.

3. LinkedIn thought leadership ads are exploding
Organic reach is cheap on LinkedIn. Thought leadership ads let you boost what's already working to the exact accounts you want. It's possibly the best underrated distribution play available.

4. Taste is the new differentiator 
When anyone can create content, who can create *different* content wins. That means hiring someone who understands strategy AND knows what makes people stop scrolling. You can't hire a VA to "do social" and expect massive growth.

5. The 95-5 split is undeniable 
CMOs are realizing that most of their budget targets the 5% actively buying. Meanwhile the 95% who aren't in-market yet are mostly ignored. They're flipping their spend and focus to earn mindshare now, knowing it'll turn into pipeline later. 

Here's the pattern: you can't buy attention anymore. You have to earn it.

The teams betting on relationships, owned audiences, and human creativity will lap everyone else.

What did I miss? What trends are you seeing?

I help B2B marketers and execs build durable content systems | Founder @ The Reeder | 12K+ newsletter readers

LinkedIn has changed a TON. Everyone's reach is down. Here's what good looks like based on an analysis of 50,000 posts in January.

Context:
I help execs launch their LinkedIn strategy and posts. 

So I study proven formats and aim for outliers every day. 

Execs usually have 5k-10k connections. So when we break 5,000 impressions, that's a win. 

When we break 10,000? That's MASSIVE.

Worth knowing how you stack up before you quit.

Check out the data 🔽

Here's what jumps out to me:

If you have 5k-10k followers and you're consistently hitting 1,000-2,000 impressions? You're in the top 25%. That's strong.

Breaking 5,000? You're in the top 10%. That's elite.

The hardest part about LinkedIn is that no one tells you if you're winning. 

The algorithm has gotten tighter, but the bar is lower than you think.

Don't quit because you're comparing yourself to influencers with 100k+ followers.

Compare yourself to your tier. Then try to move up one level.

—
Source: The Shield AI Index

PS: I share tips for improving your content every Saturday in my newsletter: https://lnkd.in/eBfWYaAC

Jonathan Moss

Turning GTM into a revenue factory through recurring impact & helping GTM leaders do the same | GTM Executive leveraging AI ✨ | Keynote Speaker & Advisor | Podcast 🎙️ Host |

I created the Revenue Nervous System™ a framework for building intelligent, self-improving growth engines that learn, adapt, and scale. The IMPACT+ Framework™ is how you implement it: a structured approach for embedding AI across the revenue lifecycle and turning strategy into execution.

I help Founders, Business Executives and Go-to-Market leaders transition to AI-native GTM and architect Agentic Growth Systems that eliminate friction and turn AI into measurable performance outcomes across their business.

At Experity, I serve as EVP & GM for Patient Engagement and Care Agent, the industry's first AI-native clinical intelligence platform. We're transforming how urgent care connects with patients through a multi-agent system that automates communication, personalizes engagement, and unifies clinical + commercial workflows across 3,000+ clinics.

Think of it as the Orchestration and Routing Layer for Healthcare: where data, context, and intelligence finally work together.

Beyond Experity, I work with founders, executive teams, and AI-native startups on growth architecture, GTM execution, and multi-agent system design.

Quick professional stats:
→ 87% median growth rate across all of my roles
→ $1B+ capital raised across portfolios
→ Scaled revenue $0 → $15B
→ Backed by Sequoia, Lightspeed, GTCR, Warburg Pincus, SoftBank

Core expertise: Revenue Nervous System™ | IMPACT+ Framework™ | Agentic AI Growth Strategy | AI-Native GTM | Patient Experience & Healthcare AI

Executive Member

Sprouts.ai named some amazing people and somehow I was included in the top 20 GTM Leaders to Watch in 2026.
 
Grateful. Also a little on the hook now.
 
Because if there's one thing I'm convinced of heading into this year, it's that the GTM leaders worth watching won't be the ones with the most tools, the most initiatives, or the loudest voices.
 
They'll be the ones who figured out how to build systems that actually compound.
 
Here's what I keep seeing: teams stall out not because they lack effort, but because they're running on architecture that was never designed for how buying actually works today.
 
Buyers are more informed. Cycles are less predictable. AI is raising the output bar for everyone.
 
In that environment, staying busy is easy. Knowing what matters is hard.
 
What I've found is that most GTM breakdowns trace back to the same root cause: the system doesn't remember, doesn't learn, and doesn't coordinate in real time.
 
You fix ICP in January. By March, the signal has shifted and nobody updated the model.
 
You run a feedback loop between sales and marketing. But nothing gets captured, so the same mistakes repeat six months later.
 
You add an AI tool. But it's bolted onto a broken process, so you get faster noise instead of better decisions.
 
The teams winning in 2026 aren't just using AI differently. 

They're building differently, around layers of intelligence that compound over time instead of resetting every quarter.
 
Clear context. Institutional memory. Coordinated execution. AI that amplifies judgment instead of replacing it.
 
That's the work. And honestly, it's still early enough that the teams who start now will have a gap that's very hard to close later.
 
Grateful to be in a community of operators who are helping make sense of the GTM foundations, what is working on the "field" and how to manage this transition into the AI-era. 

Some of my good friends Elizabeth McCalley.eth 🦋, Koen Stam 🐰 Jen Igartua, Stephanie Valenti, Steph Reck ⚓, Dale Zwizinski, Sophie Buonassisi ,Sarah Allen-Short, Kiva Kolstein 
 
Strong company to be in.
 
If you're building GTM for 2026, make sure you to follow these great leaders and tell us what is on your mind.

undefined undefined

Experity

Forum Member

Winning by Design

Pavilion

Advisory Council Member

EVP @ Experity | AI-Native GTM Architect | Revenue Nervous System™ | Care Agent | Healthcare AI

Sprouts.ai

Founder & CEO @StartStak.ai | Strategy & GTM | TED Speaker | AI Practitioner & Artist | Chief Member

Leading International @Personio | Building community @Pavilion | Architecting Growth @Winning By Design | Showing revenue teams what’s next @GTM OS

BILL | VP of Sales | GTM Executive | 4x Top 100 Most Powerful Women in Sales by Demandbase

CRO & GTM Executive | Scaling SaaS, AI, & AdTech/MarTech Revenue Engines ($2M → $70M) | High-EQ Leader | Builder of Aligned, High-Performing Sales, CS & Partnerships Teams | Speaker

Managing Partner | GTM Operating Partner to CEOs | Developed The GTM Gap™ Framework | AI-Executive Whisperer | Revenue Leader | GTM Strategy - Data vs. Gut | AI Enabled GTM | Recovering Coder | Extending Runways

SVP Marketing at GTMfund | Host of The GTMnow Podcast

Work doula for visionary GTM leaders doing hard things. 25 yrs in B2B, GTM, tech, startups & scale-ups. I coach through growth, change, and chaos—with the frameworks and experience to back it up.

President & Chief Revenue Officer at AlphaSense | YPO | Fast Co Exec Board | Dean, Pavilion CRO School | Gold Stevie Winner - Worldwide Sales Exec of the Year | 2x GSL Top 100 | GTM Awards CRO of Year

The opportunity is massive for the builders of agents in back office, GTM and may other places in business. 

Gonna be a fun next couple of years! 

Let’s cook! 👨‍🍳

Google's VP of Startups just said the quiet part out loud.

LLM wrappers and AI aggregators have their "check engine light" on.

I've been saying this for months. But hearing Darren Mowry from Google Cloud put it that bluntly? That hits different.

Here's what he means: if your entire AI startup is a nice UI sitting on top of GPT or Claude, you're toast. 

The model providers are coming for your lunch. They always were.

The startups that survive? They have vertical depth. 

Cursor doesn't just "use" AI—it understands how developers actually write code. 

Harvey AI doesn't just "chat"—it knows how law firms think about case research.

I've spent the last year building AI-native GTM systems. Not wrappers. Not aggregators. Systems where AI agents run actual operations—prospecting, content, competitive intelligence, pipeline management.

And the difference is night and day.

A wrapper adds a step. An operations-native system removes twelve.

The moat was never the model. It was never the prompt engineering. It's the workflow. 

It's understanding—really understanding—how a specific type of work actually gets done, and then building intelligence into that process.

After 21+ years of scaling operations, I can tell you: this is the same pattern every technology wave follows. The shiny demo phase dies. The "actually runs my business" phase lives.

We're entering that second phase right now.

If you're building on AI, ask yourself: would your product survive if the underlying model got 10x better and the provider launched their own version of your feature?

If the answer is no, that check engine light is for you.

Vice President, Google Global Startups | Creating global businesses that scale | Startup Advisor | Angel Investor

What Cassie Young said! 

When she talks you should 👂! 👇

Partner at Primary Venture Partners, Seasoned Enterprise CRO/CCO

🌶️🌶️ CROs, there's a GTM reckoning coming for the board room. Your board members are done rewarding your AI experimentation efforts.🌶️🌶️

The efficiency gains of the last two years were a half-truth. Sure, you trimmed the fat, but the GTM engine has been atrophying for most companies.
Here's the cold hard truth: GTM inefficiencies used to hide behind 80% SaaS margins but in the age of AI, there is nowhere left to hide. As AI compute costs eat into gross margins, you can no longer afford higher operating expenses.

In 2026 Boards will stop rewarding AI experimentation and start demanding P&L impact through the PRIME framework:
⚙️ Productivity: Is ARR/revenue per GTM head actually climbing?
📈 Retention: Are AI investments moving the needle on GRR/NRR?
💲 Investment Efficiency: Why is CAC payback still stuck at ~16 months?
🪴 Momentum: Is growth predictable and coming from a healthy mix of new and existing?
⬇️ Expense Reduction: Have you actually eliminated labor or software costs?

Most CROs use AI for "marginal gains"—better meeting summaries or slightly faster cycle times. That won't save your P&L. The winners are thinking transformationally:

🟢 Jeanne DeWitt Grosser's team at Vercel replaced a 10-person inbound SDR team with one "Lead Agent"
🟢 Kyle Norton's team at Owner.com 2x'ed productivity per rep with their AI operating system
🟢 Matt Braley’s team at EliseAI had three AEs close $50M in new ARR by supercharging their talent with AI infrastructure
🟢 Amanda Kahlow and the 1mind team use the same AI Superhumans they sell to fuel their own growth, comfortably surpassing the age-old T2D3 growth thresholds with a <6-month CAC payback

PRIME has become our non-negotiable for GTM investing, and it needs to be yours for GTM operating. Are you ready to be held accountable for the P&L? 👀👀

This is a great group of GTM leaders to follow! 

Do it and you won’t regret all the knowledge that is dropped 👇

GTM Leaders to Follow in 2026 🌟

Go-To-Market today is more than a playbook; it’s coordination, clarity, and constant iteration.

The leaders on this list are the ones building adaptive systems that align product, sales, marketing, and success around revenue momentum. They think in experiments, customer segments, and long-term, compounding motions.

Swipe through to explore the list, and follow these operators for grounded insights on winning in competitive, noisy markets.

Check them out 👇

Dale Zwizinski, Sophie Buonassisi, Sarah Allen-Short, Andrew Ettinger, Steph Reck, Joshua Bruton, Alice Wyatt, Sonali S., Stephanie Valenti, Beau Brooks, Matt Schalsey, Jessica Kao, Joon Choi, Alex Richards, Kiva Kolstein, Jonathan Moss, Jen Igartua, Koen Stam, Marcus Bening, Elizabeth McCalley.eth

GTM Leaders to Follow

I am finding it pretty easy these days to keep up with everything changing in AI, Business and GTM....

HAY? (How About You - Not sure if that is a shorthand or not, but that's all my kids talk in these days)..

Your org chart is about to become irrelevant.
 
Not because AI is "replacing jobs." Because it's dissolving the reason departments exist in the first place.
 
Anthropic just launched Agent Teams—multiple AI agents that coordinate autonomously, share context, and divide complex work without a human managing every handoff.
 
Everyone's covering it as an engineering story. They're missing the bigger picture.
 
This is a GTM story. And it changes how revenue organizations get built.
 
For 100 years, we've organized companies around functional expertise. 
 
But that structure was designed for a world where humans were the only source of specialized execution. 

When the only way to get competitive analysis done was to hire someone who does competitive analysis.
 
That world ended last week.
 
What agent teams actually enable is a shift from organizing around WHO has the skills to organizing around WHAT work needs to happen.
 
Microsoft calls it the shift from "org charts" to "work charts."
 
I call it the jobs-to-be-done model for organizational design.
 
Here's how the work actually splits:
 
𝗝𝗼𝗯𝘀 𝗮𝗴𝗲𝗻𝘁𝘀 𝗳𝗶𝗹𝗹 → Data assembly and enrichment. Pattern recognition across thousands of interactions. First-draft content creation. Coordination and handoff management. Continuous competitive monitoring.
 
The execution layer. The coordination layer. The stuff humans are slow and inconsistent at doing across scale.
 
𝗝𝗼𝗯𝘀 𝗵𝘂𝗺𝗮𝗻𝘀 𝗼𝘄𝗻 → Strategic judgment calls. Relationship architecture. Creative vision. Ethical guardrails and edge cases. And the meta-job—deciding which work goes to agents and which stays human.
 
The judgment layer. The trust layer. The stuff that requires experience, intuition, and the ability to read a room.
 
That's not "humans vs. AI."
 
That's a work chart. Jobs to be done, assigned to whoever—or whatever—does them best.
 
The pattern is consistent. Companies reorganizing around agent-augmented work are collapsing departments that existed primarily to manage coordination overhead.
 
Think about your GTM org today. How much of your headcount exists to coordinate between functions rather than create value?
 
Agent teams don't need alignment meetings. They share context natively. 

They don't lose the thread between marketing's campaign intent and sales' first outreach. 

They don't need a three-week runway to align five departments on a product launch.
 
The companies that win in 2026 won't just "use AI tools."
 
They'll have structurally different organizations. Fewer silos. More outcome-oriented teams. Agents handling execution and coordination. Humans concentrated where judgment, creativity, and relationships drive outcomes.
 
The question isn't "should we adopt AI agents?"
 
It's "which boxes on our org chart exist because the work demands them—and which exist because that's just how we've always organized?"
 
That's the uncomfortable one.

$357 billion.
 
That's how much Microsoft lost in a single day.
 
Same day, Meta gained $100 billion.
 
Both companies announced essentially the same thing: massive AI spending for 2026. Somewhere in the $65-80B range each.
 
The market's reaction wasn't just different. It was a brutal verdict.
 
I've been thinking about this all weekend. 

I think what happened is the an important signal for anyone building with AI right now.
 
Here's the thing: Wall Street has stopped caring 𝘩𝘰𝘸 𝘮𝘶𝘤𝘩 you spend on AI.
 
They only care about one question: Does AI make your core business stronger, or does it make your core business obsolete?
 
Meta passed that test. Their core business is advertising. AI makes ad targeting better. More precise. More profitable. And you can't use AI to generate 3.58 billion daily active users. Their moat is people, not software.
 
Microsoft failed that test. Their core business is software and cloud. The very AI they're building—the code generation tools, the agents, the automation—threatens the licensing model that funds it all.
 
That's the question every company needs to answer right now:
 
Are you AI-Enhanced or AI-Threatened?
 
There's no middle ground anymore. Half a trillion dollars moved in 24 hours because the market has made up its mind about how to value these two camps.
 
If you're leading an AI initiative, this is your wake-up call. The ROI reckoning is here. 

"We're investing in innovation" without clear returns? That era just ended.
 
What's your honest assessment? Is your company building moats or flooding them?

We are seeing a shift in software moats. Claude Code built an entire product in 10 days.
 
There are two views on this: 

𝗧𝗵𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰 𝗩𝗶𝗲𝘄: 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 This is "obviously... really impressive" that Claude Code was able to build Cowork in such a short timeframe. People find the product "magical," "incredibly functional," and "incredibly valuable".

𝗧𝗵𝗲 𝗦𝗸𝗲𝗽𝘁𝗶𝗰𝗮𝗹 𝗩𝗶𝗲𝘄: 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗗𝗲𝗳𝗲𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 If a product can be developed in only a week and a half, there cannot be much complexity or a significant barrier to entry. This raises questions about the product's defensibility, as every other model company will likely realize they can simply build their own version immediately.

Andreessen uses this example to illustrate the difficulty of predicting "moats" in the current AI landscape, noting that products which appear to be fundamental breakthroughs can be replicated and "lapped" by competitors very quickly.
 
Here's what actually happened. 

Anthropic watched users hack Claude Code for non-coding tasks—vacation research, organizing emails, creating presentations, cancelling subscriptions. One person used it to control their oven.
 
Most product teams would've panicked. Written a blog post clarifying "intended use cases."
 
Anthropic shipped a whole new product instead. 

In ten days. Written entirely by Claude Code itself.
 
When code becomes cheap and fast to produce, the bottleneck shifts upstream. Way upstream. The scarce resource isn't engineering time anymore. It's knowing what to build in the first place.
 
Anthropic didn't stumble into Cowork. They pattern-matched across thousands of user behaviors for months. The code took ten days. The insight behind it? That took way longer.
 
This is the part that matters for every founder and operator reading this:
 
Your code is not your moat. It probably hasn't been for a while. But the rate at which that moat is eroding just went from gradual to vertical.
 
Defensibility now lives in three places:
 
→ Distribution 
→ Data feedback loops 
→ Judgment (knowing what to build, not just how to build it)
 
We're moving from a world where execution is the bottleneck to one where intelligence is the bottleneck.
 
Code is becoming commodity infrastructure. Judgment is becoming the scarce resource.
 
And the companies that figure that out first won't need an 18-month head start. They'll just need to be right—and fast.
 
What's your take—does build speed signal weakness or a new kind of advantage?

Anthropic commands 40% of enterprise AI spend.
 
But only ~3% of consumers know they exist.
 
That's why they just spent millions roasting OpenAI during the Super Bowl.
 
And it might be the smartest move in AI this year.
 
Here's what most people missed about the "no ads" play:
 
This wasn't a principled stand disguised as marketing. 

It was a consumer acquisition strategy disguised as a principled stand. (The principle happens to be real, which is what makes it work.)
 
The timing was surgical. 

OpenAI announced ads in ChatGPT in January after Sam Altman called them a "last resort" just two months earlier. 

Anthropic saw the crack in the narrative and drove a Super Bowl ad through it.
 
But the ads were just the headline. The real story is the product blitz.
 
In the last 90 days, Anthropic shipped:
 
→ Claude Code — research preview to $500M+ run-rate revenue in 6 months 
→ Cowork — "Claude Code for the rest of your work" (no terminal required) 
→ Plugins for Cowork — agentic automation for sales, finance, legal, marketing 
→ Claude in Chrome — browser-based agent automation
 
All running through one desktop app with three tabs: Chat. Cowork. Code.
 
That's not a product lineup. That's a funnel.
 
You start with Chat because it's familiar. 

Graduate to Cowork when you realize Claude can do more than answer questions. 

Step up to Code when you want full developer-grade automation.
  
Meanwhile the Menlo Ventures data tells the enterprise story:
 
→ Anthropic: 12% share (2023) 
→ 24% (2024) 
→ 40% (2025) 

→ OpenAI: 50% share (2023) 
→ 34% (2024) 
→ 27% (2025) 

→ Claude owns 54% of the coding market alone
 
The enterprise game is won. Now they're coming for consumers.
 
And Sam's response confirmed the strategy landed. He called the ads "clearly dishonest" and said Anthropic "serves an expensive product to rich people."
 
Except Claude's subscription tiers are $0, $17, $100, $200. 

ChatGPT's are $0, $8, $20, $200. 

The math doesn't support the argument. 

But the defensiveness tells you the positioning hit a nerve.
 
The real question for GTM leaders watching this unfold:
 
Anthropic just showed that you don't need to be the biggest to reframe the entire conversation. 

They picked the one moment when 120 million people were paying attention and made OpenAI's monetization decision the story—not their own market share gap.
 
That's not marketing. That's positioning warfare.

AI isn't coming for your job.
 
It's coming for your Tuesday.
 
MIT just found that current AI can handle tasks worth 11.7% of the U.S. labor market — roughly $1.2 trillion in wages.
 
But only 2.2% of that capability is actually deployed.
 
Yale and Brookings went hunting for the AI jobs apocalypse. 33 months of data after ChatGPT launched. What they found?
 
Basically normal.
 
The occupational mix is barely shifting faster than the early internet era. 

Workers in the most "AI-exposed" jobs? Their employment share hasn't moved since January 2023.
 
So why does it feel like everything's changing?
 
Because the change is happening at the task level — not the job level.
 
Marc Andreessen nailed it: a job isn't a single thing. It's a bundle of tasks. And that bundle changes constantly.
 
Executives used to dictate to secretaries who typed letters. Email killed the task. Both jobs survived. Every task within them changed completely.
 
Same thing happening now with AI.
 
Anthropic's data proves it: 
→ 52% of AI usage is augmentation (collaboration) 
→ Only 45% is full automation 
→ Just 4% of occupations use AI for 75%+ of tasks 
→ But 49% of jobs now use AI for at least 25% of tasks
 
The pattern isn't "some jobs disappear."
 
It's "most jobs change."
 
That's what I mean by "coming for your Tuesday." 

AI eliminates the parts of the job that were draining people — not the people themselves.
 
The companies that get this right aren't asking "which roles can we automate?"
 
They're asking "which tasks are preventing our best people from doing their best work?"
 
That reframe changes everything.

What would you attempt to do if you knew you would not fail?

Important question to ask yourself especially with all the opportunity right now. 

State your goal, go achieve it. 

Regardless of the outcome you have succeeded. 

The outcome is always learning, experience, connections, and many things you would have never accomplished without going for it.

Anthropic just open-sourced plugins for Cowork.
 
Most people will see this as a product update. It's not. It's the first real crack at solving the biggest problem in enterprise AI.
 
Here's the problem nobody talks about: every AI tool we use has context amnesia.
 
You spend 45 minutes teaching Claude your sales process on Monday. 

By Tuesday, it's forgotten everything. 

You re-explain your brand voice on Thursday. 

Gone by Friday. 

You're basically training a new employee who wipes their memory every night.
 
Plugins fix this at the architectural level.
 
You encode your workflows, your data sources, your slash commands—once. 

Then Claude operates like it already works at your company. Not as a chatbot. As a department-specific operator.
 
What this actually means for GTM teams:
 
→ Sales plugin connects to your CRM and runs your exact discovery framework. Every rep, every call prep, same intelligence.
 
→ Marketing plugin embeds your positioning and brand voice. Content comes out consistent without the 3-page style guide nobody reads.
 
→ RevOps plugin standardizes pipeline hygiene and reporting as executable commands anyone can run.
 
The real move? Building your own custom plugins. Anthropic says they're "easy to build, edit, and share" without deep technical expertise. 

Organization-wide sharing is coming in weeks.
 
Think about what that means. New hire installs the team plugin on day one. 

Immediately operates with the accumulated intelligence of the entire department. 

No 90-day ramp. No tribal knowledge dependency.
 
This is what AI-native architecture actually looks like—not adding AI to your existing process, but encoding your institutional knowledge into your AI infrastructure.
 
The companies that figure this out first don't just move faster. They compound advantages that become nearly impossible to replicate.
 
What department would you build a plugin for first? Curious where people see the highest leverage.

Anthropic

Let's go!! This could be a once in a lifetime opportunity for someone to help us disrupt healthcare and the way patients engage it today. You don't want to miss the opportunity!

We’re #hiring.

#Healthcare has lived too long with patient experiences that feel dated and transactional. We’re building something different, a modern, AI-native Care Agent that helps clinics respond faster, communicate better, and reduce friction for patients. It’s already live in 2,000+ clinics, handling nearly a million patient conversations each month, so this is real-world impact at real scale.

We’re looking for a Software Engineer - Technical Lead for our Care Agent solution to help take the next leap in transformation. You’ll drive technical delivery and raise the bar on quality, reliability, and speed of learning, while working in a modern stack (Node.js, TypeScript, Next.js, AWS Bedrock AgentCore).

Direct hire only, no agencies.
Apply here: https://lnkd.in/eq5Pm2PX

Want to be considered?
* Apply (link above)
* Please do not message me directly

Ask someone we both know to send me an intro with your resume

Be ready for: Why this problem, how are you using AI today, what impact can you make here

#Engineering #HealthcareAI #SaaS #TechLeadership #Innovation

Know anyone who might be interested?

AI has changed from a chat back and forth to a coworker.. catch up on everything that happened this week 👇

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