Sign in to view Jeff’s full profile
or
New to LinkedIn? Join now
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
Sign in to view Jeff’s full profile
or
New to LinkedIn? Join now
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
Austin, Texas, United States
Sign in to view Jeff’s full profile
Jeff can introduce you to 10+ people at Pequity, an ADP company
or
New to LinkedIn? Join now
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
12K followers
500+ connections
Sign in to view Jeff’s full profile
or
New to LinkedIn? Join now
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
View mutual connections with Jeff
Jeff can introduce you to 10+ people at Pequity, an ADP company
or
New to LinkedIn? Join now
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
View mutual connections with Jeff
or
New to LinkedIn? Join now
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
Sign in to view Jeff’s full profile
or
New to LinkedIn? Join now
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
About
Welcome back
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
New to LinkedIn? Join now
Articles by Jeff
-
Navigating the Complexities of Team Compensation: An Engineering Leader's Perspective
Navigating the Complexities of Team Compensation: An Engineering Leader's Perspective
In the ever-evolving landscape of technology and engineering, one constant remains: the pivotal role of compensation in…
19
1 Comment -
What Really Drives Execution, Anyway?Mar 4, 2015
What Really Drives Execution, Anyway?
All businesses celebrate great execution, that moment where you finally hit the goal you’ve been building toward for…
26
2 Comments -
Validation in the SpotlightFeb 20, 2015
Validation in the Spotlight
Tomorrow is my birthday, and sitting here thinking about it from a professional perspective, I have not called it out…
13
3 Comments -
The Value of One-on-OnesFeb 11, 2015
The Value of One-on-Ones
Time flies by so fast. I’ve been working at the art of two parallel concepts for some time now— managing people in the…
27
4 Comments
Activity
12K followers
-
Jeff Auston reposted thisJeff Auston reposted thisEvery comp cycle starts with a budget conversation with finance. Teams that walk out with the numbers they asked for prep for it differently. First, get my best practice merit cycle template free here: 🚀https://lnkd.in/eBEZFpzV - then see three questions finance asks in every comp cycle conversation - and what having them ready actually looks like: 𝟭) "𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗰𝗼𝘀𝘁 𝗮𝘀 𝗮 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 𝗼𝗳 𝗽𝗮𝘆𝗿𝗼𝗹𝗹?" Build budget bottoms-up before the meeting: • Eligible headcount x average salary = eligible payroll base • Eligible payroll x merit % = merit budget • Run market corrections as a separate bucket • Roll those up and divide by total payroll That number is what finance uses to compare you to last year and against other budget requests. 𝟮) "𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗳 𝘄𝗲 𝗰𝘂𝘁 𝗶𝘁 𝗯𝘆 𝗵𝗮𝗹𝗳?" Have tradeoffs ranked before they ask. Score every eligible employee on three factors: • Compa-ratio - how far below market are they • Tenure - 12-24 months is peak flight risk • Performance - high performers at low compa is your highest risk combination Sort by score. The lowest-risk employees get deferred first. Now you have a specific, defensible answer instead of "we can't cut it." 𝟯) "𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗰𝗼𝗺𝗽𝗮𝗿𝗲 𝘁𝗼 𝗹𝗮𝘀𝘁 𝘆𝗲𝗮𝗿?" Don't just give a number - give them a breakdown of each driver accounted for separately: • Headcount growth: net new employees x avg salary x merit rate • Salary movement: headcount x avg salary change x merit rate • Rate change: difference between this year's merit % and last year's • Market corrections: new bucket, explain why it exists One unexplained line item tanks the whole ask. Four clean lines and the conversation moves on. 𝟰) 𝗕𝗼𝗻𝘂𝘀: "𝗪𝗵𝗼 𝗮𝗿𝗲 𝘄𝗲 𝗺𝗼𝘀𝘁 𝗮𝘁 𝗿𝗶𝘀𝗸 𝗼𝗳 𝗹𝗼𝘀𝗶𝗻𝗴 𝗶𝗳 𝘁𝗵𝗶𝘀 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗴𝗲𝘁 𝗳𝘂𝗻𝗱𝗲𝗱?" Finance rarely asks this. Bring it anyway. Name two or three critical roles, their replacement cost (typically 50-200% of salary), and their current compa. It reframes the conversation from expense to risk. That is the shift that gets budgets approved. If you want a resource to help you walk in prepped, I have something for you 👇 I built a comp budget prep kit with a bottoms-up payroll model, a cut scenario builder that ranks tradeoffs by retention risk, and a YoY variance explainer. Everything finance will ask, answered before the meeting starts. 💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗕𝗨𝗗𝗚𝗘𝗧 and I'll send it over.
-
Jeff Auston reposted thisJeff Auston reposted thisWhen Arif Ender, Director of Compensation (EMEA & LATAM) at Palo Alto Networks, runs compensation, it’s not one country. It's 65+ Feb 18th, Arif and I are going live to break down the systems and frameworks we’ve seen work for global teams. We’ll cover: • How Arif runs comp across 65+ countries • Where global programs break (and the mistakes most teams repeat) • How to handle inflation + fast market shifts • Practical structures for global merit cycles that actually scale If you own global comp, this will save you months of trial and error. 🚀 Join 500+ comp leaders here: https://lnkd.in/dHVE6gZx Also, I have a surprise for you 👇 I put together a global pay band architecture template for structuring geo differentials and localized salary bands, plus a Manager Enablement doc with FAQs, talk tracks, and a pay convo guide to help leaders explain compensation decisions with confidence. 💬 𝗝𝘂𝘀𝘁 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 “𝗚𝗹𝗼𝗯𝗮𝗹” and I’ll message you both resources.
-
Jeff Auston reposted thisJeff Auston reposted thisThe best performing compensation teams do one thing well. They track the right pay risk signals early. Not once a year. Not just during cycles. All year long. Here are the signals I’d monitor no matter the company size: (First, get a demo to see how Pequity AI Insights helps you track flight risk signals 👉 https://pequity.com/demo) 𝟭. 𝗠𝗮𝗿𝗸𝗲𝘁 𝗱𝗿𝗶𝗳𝘁: When certain roles move 5–10% before your ranges do, that’s the earliest sign you’re about to lose offers. 𝟮. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘀𝗶𝗴𝗻𝗮𝗹𝘀: A 6–8% gap between new hire pay and internal averages is one of the strongest early indicators of compression. 𝟯. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘃𝘀. 𝗽𝗮𝘆 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Your strongest talent should move through the range at a faster velocity. If they drift toward midpoint, regret is building quietly. 𝟰. 𝗥𝗮𝗻𝗴𝗲 𝗽𝗲𝗻𝗲𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀: Range clustering signals structural issues. Top-heavy teams point to leveling or performance calibration gaps. Bottom-heavy teams suggest the market is outpacing you or you targeted too high in the market. 𝟱. 𝗩𝗲𝘀𝘁𝗶𝗻𝗴 𝗿𝘂𝗻𝘄𝗮𝘆: Attrition spikes when someone is 12–18 months from vesting out. You should know that timeline long before their manager does. 𝟲. 𝗕𝘂𝗱𝗴𝗲𝘁 𝗰𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲: Merit, hiring, promotions, equity, and market movement need to live in the same model. When they don’t, planning season becomes triage. 𝟳. 𝗣𝗮𝘆 𝗲𝗾𝘂𝗶𝘁𝘆 𝗱𝗿𝗶𝗳𝘁: Gaps don’t explode overnight. They drift a little each quarter. Tracking that movement tells you the real story long before a regression does. Tracking these signals turns comp into one of the most strategic levers inside the business and makes your team proactive, not reactive. Also, I have a surprise for you.👇 I built a plug-and-play Compensation Risk Intelligence Playbook with the scoring model, risk map, and dashboard sample if you want to operationalize this. 💬 Just 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 “𝗥𝗶𝘀𝗸” and I’ll send you the link to the Google Doc. P.S. I’d also love to hear in the comments what signals you track to stay ahead of comp risk
-
Jeff Auston reposted thisJeff Auston reposted thisMost companies treat equity refresh grants like a spreadsheet exercise. But they’re one of the strongest levers you have for long-term retention. The secret? How you design the vest matters just as much as the equity amount itself. When you get vesting design right, you can keep your strongest talent engaged year after year, even as their role, scope, and contribution evolves. Here are the 4 common models I see. Each one creates a different retention curve and a different emotional experience for employees. First — reviewing your refresh design? Model your cycle free in Pequity 👉 https://lnkd.in/etkwdbTg 𝟭. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘃𝗲𝘀𝘁𝗶𝗻𝗴 Predictable. Easy to communicate. Works well for companies where comp needs to be steady while teams scale. But the danger is the year 4 trough. Example: if a $100K new hire grant is vesting $25K per year and your refresh grant only vests $6.25K per year, the employee suddenly vests $18.75K less starting year 5. They feel this is like a pay cut. 𝟮. 𝗕𝗼𝘅𝗰𝗮𝗿 𝗺𝗼𝗱𝗲�� Keeps vesting even, and removes a cliff. Example: a $25K refresh vests 0 percent for three years, then 100 percent in year 4. This is excellent when you want senior leaders to think like owners who stay for outcomes. But if your top talent expects annual value and fast recognition this feels like punishment. Culture fit matters. 𝟯. 𝗙𝗿𝗼𝗻𝘁𝗹𝗼𝗮𝗱𝗲𝗱 𝗩𝗲𝘀𝘁𝗶𝗻𝗴 / 𝗡𝗼 𝗰𝗹𝗶𝗳𝗳 𝘃𝗲𝘀𝘁𝗶𝗻𝗴 This is where you change the new hire grant vest to smooth the entire experience. Nvidia, Airbnb, and Google are adopting front-loaded vesting and more precise refreshes. Done well, it reduces cash reliance, manages equity burn, and aligns refreshes with performance instead of entitlement. It also eliminates the “year five drop-off” of traditional models. 𝟰. 𝗢𝗻𝗲 𝘆𝗲𝗮𝗿 𝗴𝗿𝗮𝗻𝘁𝘀 Focuses the most on performance. Grants are awarded and refreshed annually. It offers precise control over who receives equity and when, but provides minimal long-term retention value. Strong for budget discipline, weaker for continuity. It determines which talent receives what grants but has minimal long-term retention value. It does, however, offer strong budgetary controls. The point is you do not choose a vesting model based on preference. You choose based on what behavior you want to shape. For example, if leadership wants loyalty, choose a boxcar. If you want consistency, choose no cliff. Also, if you made it this far, I have a surprise for you.👇 I built a vesting options comparison matrix so comp teams can easily show executives how different vesting models play out side by side. 💬 𝗝𝘂𝘀𝘁 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 𝗘𝗾𝘂𝗶𝘁𝘆 below and I'll send you the Google Sheet link.
-
Jeff Auston reposted thisJeff Auston reposted thisBig news for CompTech. ADP welcomes Pequity 🎉 ADP closes a clear gap in compensation planning. Pequity adds configurable cycles, complex approvals, multi-currency, and off-cycle automation many teams still run in spreadsheets. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘀𝗺𝗮𝗿𝘁 • ADP strengthens Workforce Now and Vantage with deeper comp workflows and new attach levers. • Pequity gains distribution and payroll write back at scale. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘁𝗼 𝘄𝗮𝘁𝗰𝗵 • Can ADP embed Pequity’s approvals and write backs across existing solutions without slowing live cycles? • Can they keep packaging simple so clients know what to buy or retire? • Can they retain Pequity’s product talent so roadmap speed continues? Time will tell. If they deliver on these, this deal raises the standard for compensation planning and execution. More detail in the next CompTech News Roundup ⭐ Subscribe here: https://lnkd.in/d4GNVxEF Link to press release: https://lnkd.in/dAJHjdkX
-
Jeff Auston reposted thisJeff Auston reposted thisAfter six years of building Pequity, today I get to share some big news: Pequity 𝗶𝘀 𝗷𝗼𝗶𝗻𝗶𝗻𝗴 ADP. When we started Pequity, our goal was simple: build a compensation system that actually worked for the people who used it, all in the hopes of creating better pay outcomes. A platform flexible enough to handle complex approvals, re-orgs, and multi-currency planning — but simple enough that anyone could run a comp cycle in days, not months. Over time, we built a product that supported companies from 100 to 100,000 employees — and along the way, we heard the same message from every client: “This is what we’ve always wanted our HR system to do.” So when we started talking with ADP, it became clear we shared the same vision: empowering companies to plan and reward their people with transparency and speed. I know that for some of our customers, teammates, and friends, this news might come as a surprise. I understand that deeply. Which is why I wrote a longer note — about why this acquisition is happening, what it means for Pequity’s customers and team, and how we’ll continue serving you with the same heart and speed that got us here. You can read it here 👉 https://lnkd.in/eYnkn5sN Or to see the official press release, read here: https://lnkd.in/e6b8fRR3 I’m feeling all the things — excitement, pride, nostalgia, and so much gratitude for everyone who’s supported us along the way. Here’s to what’s next 💛
-
Jeff Auston reposted thisJeff Auston reposted thisWhen we first met Pequity through ADP Ventures, we were immediately impressed by how thoughtfully they approached equity and compensation management. What started as an investment conversation with Husam (Sam) Nasser naturally evolved into something more as our enterprise business unit dove in deeper. Welcome to the ADP family, Kaitlyn Knopp, Warren Lebovics and the Pequity team!
-
Jeff Auston reposted thisJeff Auston reposted thisI’ve run global merit cycles across 20+ countries. Here are my 6 rules to keep currency swings from blowing them up. 💥 First, steal 𝗺𝘆 𝗖𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗠𝗲𝗿𝗶𝘁 𝗖𝘆𝗰𝗹𝗲 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲 (just released today) w/built-in FX rules 💶💱 — free in Pequity 👉 https://lnkd.in/etkwdbTg 1. 𝗔𝘀𝘀𝗲𝘀𝘀 𝗮𝗻𝗱 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗲 𝗙𝗫 𝗩𝗼𝗹𝗮𝘁𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸𝘀: Start by mapping how merit budgets in your base currency translate locally amid rate fluctuations. For instance, a planned 5% increase could dwindle to 3% in a region with a strengthening currency, diminishing employee motivation. Solution: Conduct a pre-cycle audit using historical data and forecasts to identify high-risk markets, then build in buffers like a 2-5% contingency fund to absorb swings and maintain intended value. 2. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗥𝗮𝘁𝗲 𝗟𝗼𝗰𝗸𝗶𝗻𝗴: Fix conversion rates at the merit cycle's outset through forward contracts or internal hedging agreements. This locks in predictability, preventing mid-cycle surprises. Aim to set rates based on 3-6 month projections, ensuring global consistency while adapting to local realities. 3. 𝗕𝘂𝗱𝗴𝗲𝘁 𝗶𝗻 𝗗𝘂𝗮𝗹 𝗖𝘂𝗿𝗿𝗲𝗻𝗰𝗶𝗲𝘀 𝗳𝗼𝗿 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Allocate overall merit pools in your "home" currency for centralized finance oversight and reporting, but empower managers with real-time local currency equivalents in planning tools. This dual-view approach minimizes surprises from FX fluctuations, ensures alignment, and allows for agile adjustments. 4. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: Round merit increases to employee-friendly local denominations (e.g., multiples of ¥100 in Japan, not odd ¥3 increments) to avoid perceived nickel-and-diming. Set alerts for significant percentage discrepancies across markets (e.g., >5% variance from global targets). 5. 𝗔𝗱𝗼𝗽𝘁 𝗛𝘆𝗯𝗿𝗶𝗱 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Combine centralized guidelines with localized data from compensation surveys and Purchasing Power Parity (PPP) adjustments. This ensures raises feel competitive in employees' home markets. Advanced tip: Segment your workforce by exposure levels (e.g., high-volatility regions) and apply tiered conversion strategies to optimize outcomes. 6. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗦𝗺𝗮𝗿𝘁 𝗖𝗼𝗺𝗽 𝗧𝗲𝗰𝗵: Use platforms (like Pequity) that store rates, run "what-if" scenarios (e.g., USD up 5%), and provide audit logs for compliance. It's a game-changer for scalability. Automating these FX rules in your merit cycle prevents budget overruns, inequitable increases, and compliance risks. Also, I have a surprise for you.👇 I’ve put together a complete job leveling template & steps matrix—mapping survey titles to company titles with a simple 1–9 ladder, and detailing IC, Manager, and Support paths with clear steps for growth (built from top comp team best practices). 𝗝𝘂𝘀𝘁 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 “𝗟𝗲𝘃𝗲𝗹𝘀” and I'll send you the link to the Google Sheet.
-
Jeff Auston reposted thisJeff Auston reposted thisI’ve been talking with HR and comp leaders nonstop over the past few months. You know what they’re sick of? 𝗩𝗮𝗽𝗼𝗿𝘄𝗮𝗿𝗲. Platforms that promise the world, demo well… and then collapse when it’s time to actually run a comp cycle. Sound familiar? What if I told you we just flipped the script at Pequity? Starting today, you can sign up FREE and build out your entire merit cycle – AI formula building, budget modeling, best practice templates, unlimited team invites - the works. No credit card. No strings. Just pure value to plan like a pro. Only pay if you decide to launch and you can even buy a single comp cycle – flexible as heck for startups or one-off needs. 🚀 Start building for free here: https://lnkd.in/etkwdbTg Also, I’ve got a surprise for you 👇 I’ve put together a merit matrix training deck that covers target merit increases, ratings, promotion guidelines, how to use compa-ratios, and more (built on best practices from top-performing teams). 💬 𝗝𝘂𝘀𝘁 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 “𝗠𝗲𝗿𝗶𝘁” and I’ll message you the Google slide link.
-
Jeff Auston liked thisJeff Auston liked thisEvery comp cycle starts with a budget conversation with finance. Teams that walk out with the numbers they asked for prep for it differently. First, get my best practice merit cycle template free here: 🚀https://lnkd.in/eBEZFpzV - then see three questions finance asks in every comp cycle conversation - and what having them ready actually looks like: 𝟭) "𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵��𝘀 𝗰𝗼𝘀𝘁 𝗮𝘀 𝗮 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 𝗼𝗳 𝗽𝗮𝘆𝗿𝗼𝗹𝗹?" Build budget bottoms-up before the meeting: • Eligible headcount x average salary = eligible payroll base • Eligible payroll x merit % = merit budget • Run market corrections as a separate bucket • Roll those up and divide by total payroll That number is what finance uses to compare you to last year and against other budget requests. 𝟮) "𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗳 𝘄𝗲 𝗰𝘂𝘁 𝗶𝘁 𝗯𝘆 𝗵𝗮𝗹𝗳?" Have tradeoffs ranked before they ask. Score every eligible employee on three factors: • Compa-ratio - how far below market are they • Tenure - 12-24 months is peak flight risk • Performance - high performers at low compa is your highest risk combination Sort by score. The lowest-risk employees get deferred first. Now you have a specific, defensible answer instead of "we can't cut it." 𝟯) "𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗰𝗼𝗺𝗽𝗮𝗿𝗲 𝘁𝗼 𝗹𝗮𝘀𝘁 𝘆𝗲𝗮𝗿?" Don't just give a number - give them a breakdown of each driver accounted for separately: • Headcount growth: net new employees x avg salary x merit rate • Salary movement: headcount x avg salary change x merit rate • Rate change: difference between this year's merit % and last year's • Market corrections: new bucket, explain why it exists One unexplained line item tanks the whole ask. Four clean lines and the conversation moves on. 𝟰) 𝗕𝗼𝗻𝘂𝘀: "𝗪𝗵𝗼 𝗮𝗿𝗲 𝘄𝗲 𝗺𝗼𝘀𝘁 𝗮𝘁 𝗿𝗶𝘀𝗸 𝗼𝗳 𝗹𝗼𝘀𝗶𝗻𝗴 𝗶𝗳 𝘁𝗵𝗶𝘀 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗴𝗲𝘁 𝗳𝘂𝗻𝗱𝗲𝗱?" Finance rarely asks this. Bring it anyway. Name two or three critical roles, their replacement cost (typically 50-200% of salary), and their current compa. It reframes the conversation from expense to risk. That is the shift that gets budgets approved. If you want a resource to help you walk in prepped, I have something for you 👇 I built a comp budget prep kit with a bottoms-up payroll model, a cut scenario builder that ranks tradeoffs by retention risk, and a YoY variance explainer. Everything finance will ask, answered before the meeting starts. 💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗕𝗨𝗗𝗚𝗘𝗧 and I'll send it over.
-
Jeff Auston liked thisJeff Auston liked thisWhen equity loses value and budgets tighten, retention becomes a compensation problem. The teams that protect headcount in a downturn spend differently, not more. Here are the levers mature comp teams pull when SaaS valuations compress: 𝟭) 𝗦𝗰𝗼𝗿𝗲 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗿𝗶𝘀𝗸 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝘀𝗽𝗲𝗻𝗱 Flight risk × role criticality = retention priority score. • High risk + critical role → act now • Low risk + replaceable role → monitor only • Don't allocate budget evenly. Triage first. 𝟮) 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗯𝘂𝗱𝗴𝗲𝘁 𝗳𝗿𝗼𝗺 𝗺𝗲𝗿𝗶𝘁 Mixing retention spend into merit dilutes both pools. • Merit = reward for performance • Retention = cost to hold a critical person Two separate decisions. Two separate buckets. 𝟯) 𝗥𝗲𝘃𝗮𝗹𝘂𝗲 𝗲𝗾𝘂𝗶𝘁𝘆 𝗮𝘁 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝟰𝟬𝟵𝗔, 𝗻𝗼𝘁 𝗴𝗿𝗮𝗻𝘁-𝗱𝗮𝘁𝗲 𝗙𝗠𝗩 Peak-value grants don't retain anyone when the strike price is above current valuation. If grant value has dropped more than 50%, cash becomes your more credible lever. 𝟰) 𝗕𝗿𝗶𝗱𝗴𝗲 𝘁𝗵𝗲 𝗲𝗾𝘂𝗶𝘁𝘆 𝗴𝗮𝗽 𝘄𝗶𝘁𝗵 𝗰𝗮𝘀𝗵 Three structures work: • Spot bonus - immediate, no commitment required • Retention bonus - tied to a 12-18 month cliff • Structured payout - quarterly over 1-2 years to extend commitment 𝟱) 𝗖𝗼𝗿𝗿𝗲𝗰𝘁 𝗹𝗼𝘄-𝗰𝗼𝗺𝗽𝗮 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗳𝗶𝗿𝘀𝘁 Employees below 85% compa with broken equity are the most exposed to outside offers. A $5K base correction costs a fraction of a $50K backfill. 𝟲) 𝗥𝗲𝗮𝗻𝗰𝗵𝗼𝗿 𝘁𝗼𝘁𝗮𝗹 𝗰𝗼𝗺𝗽 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗵𝗼𝗻𝗲𝘀𝘁𝗹𝘆 Employees already know equity has lost value. Pretending otherwise accelerates attrition. Reframe the conversation: current cash value, vesting timeline, and realistic upside scenario. Honest math beats false optimism. The levers above are just a starting point, but #3 is where most teams get stuck. If you're not sure how to revalue existing grants at current 409A, I built a calculator that does it for you. 👇 Drop in grant date FMV, current 409A, shares, and strike price and it tells you the real dollar value employees are sitting on today vs. what they were promised. 💬 Just comment 𝗥𝗲𝘃𝗮𝗹𝘂𝗲 and I'll send it over to you.
-
Jeff Auston liked thisJeff Auston liked thisClaude Code is only as good as your orchestration workflow. I spent 6 months testing it so you don't have to. [ P.S. You can get my Ultimate Claude Code guide for engineers here at no cost: https://lnkd.in/e64Jvdrt ] So Boris Cherny (the creator of Claude Code at Anthropic) recently shared the internal best practices his team actually uses daily. Someone brilliantly distilled those threads into a structured CLAUDE .md file you can drop straight into any project root. It acts as a system prompt. Turns Claude into a far more autonomous, rigorous engineering partner. Here's what it does: → 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Mandates "Plan Node Default" for any task over 3 steps. Uses subagents liberally to keep the main context window clean. → 𝗦𝗲𝗹𝗳-𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗟𝗼𝗼𝗽: This is the real magic. After ANY correction, it updates a tasks/lessons .md file. You're building a compounding system where the mistake rate drops over time because it actively learns from your feedback. → 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗗𝗼𝗻𝗲: Can't mark a task complete without proving it works. Diffs behavior. Runs tests. Checks logs. The bar? "Would a staff engineer approve this?" → 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗕𝘂𝗴 𝗙𝗶𝘅𝗶𝗻𝗴: Zero hand-holding. Point it at failing CI tests or error logs... it just goes to work. No constant context switching from you. → 𝗦𝘁𝗿𝗶𝗰𝘁 𝗧𝗮𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Forces a "Plan First" approach written to a todo .md with checkable items before any implementation starts. And perhaps the most important part? It forces the AI to prioritize simplicity, find root causes instead of temporary fixes, and minimize the blast radius of every change. Senior developer standards. Not shortcuts. If you're spending hours a day in the terminal with AI, setting up a strong .md instruction file like this isn't optional anymore. It's the difference between AI that drifts and AI that compounds. It takes time to set up. But if you do, you're ahead of almost everyone else.
-
Jeff Auston liked thisJeff Auston liked this5 AI projects that will get you hired in 2026: P.S. I've shared 10 AI Agent Projects and 100+ Pro Hacks for Claude Code here: https://lnkd.in/dcibJhzQ Let's get into it: 1. RAG from Scratch GitHub: https://lnkd.in/dJBbR-dJ 2. Al Social Media Agent GitHub: https://lnkd.in/dyxAneHk 3. Medical Image Analysis GitHub: https://lnkd.in/dtMEGFQ4 4. MCP Tool-Calling Agents Notebook: https://lnkd.in/dxyNvjaj 5. Al Assistant with Memory GitHub: https://lnkd.in/dwkYtgAC
-
Jeff Auston liked thisJeff Auston liked thisWhen Arif Ender, Director of Compensation (EMEA & LATAM) at Palo Alto Networks, runs compensation, it’s not one country. It's 65+ Feb 18th, Arif and I are going live to break down the systems and frameworks we’ve seen work for global teams. We’ll cover: • How Arif runs comp across 65+ countries • Where global programs break (and the mistakes most teams repeat) • How to handle inflation + fast market shifts • Practical structures for global merit cycles that actually scale If you own global comp, this will save you months of trial and error. 🚀 Join 500+ comp leaders here: https://lnkd.in/dHVE6gZx Also, I have a surprise for you 👇 I put together a global pay band architecture template for structuring geo differentials and localized salary bands, plus a Manager Enablement doc with FAQs, talk tracks, and a pay convo guide to help leaders explain compensation decisions with confidence. 💬 𝗝𝘂𝘀𝘁 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 “𝗚𝗹𝗼𝗯𝗮𝗹” and I’ll message you both resources.
Experience & Education
-
Pequity
**** ** ***********
-
********
** ***********
-
********
******
-
************ ***** **********
******* ********** *********** undefined
-
********** ** *********** *****
** ********** ***********
View Jeff’s full experience
See their title, tenure and more.
Welcome back
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
New to LinkedIn? Join now
or
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
Recommendations received
17 people have recommended Jeff
Join now to viewView Jeff’s full profile
-
See who you know in common
-
Get introduced
-
Contact Jeff directly
Other similar profiles
Explore more posts
-
Jellyfish
21K followers
💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ
20
-
Ashish Chaudhary
Spotnana • 5K followers
Six months ago, Spotnana engineering committed fully to Claude Code and Cursor. Adoption happened quickly and naturally. What surprised us most was a feature released last July in that many developers still haven't discovered: custom sub-agents. When you use ClaudeCode in its default form, you're working with a capable generalist. Custom sub-agents let you create specialists instead. You define them by placing a markdown file in the .claude/agents/ directory with simple YAML frontmatter that sets the agent's name, persona, and allowed tools. Claude then automatically delegates tasks to the appropriate specialist based on context and no manual prompting required. Each sub-agent runs in its own isolated context window, so your main conversation stays clean. The agents are also shareable across projects, which lets institutional knowledge build over time. The biggest challenge we faced was getting routing reliable. When agent descriptions overlap even slightly, Claude struggles to choose the right one. We've learned to make each persona description extremely precise and distinct. Another lesson came the hard way: sub-agents inherit your permission settings by default. I once gave a research-focused agent write access, and it began refactoring code while trying to investigate a bug. One improvement I would love to see is a clear post-session audit log that shows which agent handled which actions and when. Right now, tracing responsibility requires digging through long transcripts, which slows down debugging and makes accountability harder. If you're already using Claude Code (or Cursor with Claude) and haven't tried the /agents feature yet, you're missing out on significant capability. What sub-agents has your team built, and how have they changed your workflow? #ClaudeCode #AICoding #AgenticAI #CursorAI #DeveloperTools
58
3 Comments -
Paul Chiusano
Unison Computing • 2K followers
I thought this was a good and very reasonable talk by Eleanor Millman on how to prioritize projects for a platform engineering team: https://lnkd.in/gaYtUHRH A few insights I took from it: - Unlike product features which can be more directly tied to revenue, platform engineering work is a couple steps removed. But that doesn't mean it's unimportant! Far from it, platform engineering projects can make everyone at the company more efficient, able to produce higher quality software, etc. - While "urgency" is always going to be a factor, you don't want to just be putting out fires, you want be able to prioritize work which is "high impact" ... even if that impact isn't felt immediately. - Good rule of thumb: prioritize "highest impact for lowest effort". - The talk has some ideas on what factors to choose for impact, and how to blend them. For instance "speed of development" is one factor, "cloud cost optimization" might be another. The weighting of different impact factors can change over time, depending on the needs of the business. I would say that a lot of companies don't have much methodology here but I can really see the value in codifying it. You can always change the methodology or the weighting if it's spitting out results that don't pass the smell test or you really feel it is leading the company astray. Clarity can give the org more freedom to put resources behind projects that would otherwise never be taken on. When things are unclear, prioritization still happens implicitly, but the decisions tend to be a lot more random and fear-based and the org is worse off as a result.
15
3 Comments -
Alex Laats
Independent • 7K followers
In today’s Substack post from the Plan of Record series, I walk through the operational frameworks I’ve relied on to build high-performance product development teams and why they’re essential for diagnosing and fixing what’s broken. I also explain why we had to create the PoR Prioritization Framework to address a critical gap no other model could fill. Here’s the link: https://lnkd.in/ek9J_cqq #SaaS #ProductManagement #EngineeringLeadership #R&D #Prioritization #Execution #CPO #CTO #CPTO
14
-
Aidan Harding
Aquiva Labs • 1K followers
This is a really interesting article about writing agents with the Claude Agent SDK. It gets into design advice that you should consider when writing an agent with Claude. It chimes with what I’ve read about the development of Claude Code (write as little business logic as possible and concentrate on getting out of the way of the model). The general takeaway for me is from the three-block flow chart in the middle: Give the agent good tools/affordances in three categories: Gather Context, Take Action, Verify work. Do that at the right granularity, with good feedback, and then let the model do the driving. It’s deceptively simple, but it has a goldilocks quality to it. Trying to make a more complex flow chart with many more boxes and lines constrains the AI to just the things you could think of in advance and makes it overfit to what you know today. Trying to make it simpler by, for example, giving the agent a whole existing API as a single tool can overwhelm it with too much choice and then too much context in the responses. It’s definitely worth reading if you're interested in building or using agents, irrespective of AI vendor. https://lnkd.in/eKF2rXWb
7
-
Eddy Recio
1K followers
Should you avoid the em dash (—)? Some argue it signals AI-generated text. I disagree. Used sparingly, it’s a helpful tool — adding pause, emphasis, or flow to your writing. The real issue is overuse or fully outsourcing writing to AI without care. If you want to insert it: Mac: Option + Shift + Hyphen iOS: Long press the hyphen key. Write like a human — em dash and all.
16
23 Comments -
Bhavin Surela
Twilio • 1K followers
What does it take to build a successful engineering organization? 🚀 It’s not just strategy decks or scaling headcount. It’s the messy, behind-the-scenes work—building clarity when there’s chaos, balancing autonomy with alignment, and constantly evolving your architecture and culture as you grow. I’ve spent the last few years learning (and re-learning) this. So, I wrote a 3-part blog series to share some nuggets. If you're leading (or hoping to lead) at scale, I hope there's something useful in here for you. Would love your thoughts, and even more—your own lessons 🙂 #engineering #leadership #lessonslearned
29
1 Comment -
William Sun
Auctor • 9K followers
What is agentic delivery? Imagine a world where every software implementation starts aligned— and stays that way. Where discovery isn’t buried in notes, where scope changes don’t break everything, where tribal knowledge isn’t lost between teams. Imagine if: 🧠 Requirements were structured as they were spoken 📄 Artifacts were generated in real-time 🔁 Context followed every decision, across every tool ⚙️ Change was handled with traceability, not fire drills That’s what agentic delivery unlocks. It’s not just AI stitched onto old workflows. It’s a new operating system for how delivery gets done— powered by agents that structure, generate, and evolve work with you. Not to replace people. But to give your best people the leverage they’ve never had before. → Fewer handoffs. → Less rework. → Stronger knowledge reuse. → Faster, more confident go-lives. This is the future of services. And it’s already being built. Welcome to the era of agentic delivery.
34
4 Comments -
Alexander Shulman
Tototheo Global • 2K followers
CPTO Revolution: Why SaaS Companies Are Merging Product and Tech Leadership I’ve lived the pain of siloed leadership and the inefficiencies it creates many times. And I’ve also seen how tightly integrated product and tech can transform not just delivery, but outcomes. Traditional org charts are no longer keeping up. The most competitive SaaS companies aren’t hiring separate CTOs and CPOs anymore - they’re bringing both roles under one roof with CPTOs. This isn’t about headcount optimization. It’s about reducing friction and moving faster in a world where complexity is climbing and customer expectations evolve by the week. Here’s the core issue: when product and engineering leadership are separate, you’re always playing a game of telephone. Product defines what’s needed. Engineering estimates and negotiates. Product pushes. Engineering pushes back. Handoffs everywhere. Every step is a delay. A CPTO owns the entire journey - from insight to shipped product. One person accountable. One vision. One continuous flow. And it works. Companies running this model are seeing up to 40% faster go-to-market. Less tech debt. Clearer priorities. Higher team velocity. What Actually Changes 1. Aligned Metrics You stop optimizing for isolated outcomes. Product isn’t chasing adoption while engineering shields uptime. Both roll into shared business goals. 2. AI Gets Strategic With unified leadership, AI isn’t a bolt-on. It becomes core to how you design, build, and validate products - through data, feedback loops, and automation. 3. Developer Experience Matters Platform engineering gets the attention it deserves. Internal tools align with product delivery, and suddenly teams aren’t waiting - they’re shipping. UX maturity, platform architecture, technical scalability - all of it becomes a single continuum. You’re not just improving “process”; you’re aligning every decision to what users need and what’s feasible to build. The CPTO Skillset This isn’t a hybrid role - it’s an integrated one. You need the technical depth to guide architecture and the product judgment to say no to overengineering. You talk to engineers about latency and reliability, and to the board about market traction and differentiation. As AI accelerates software development, what matters is orchestration—of systems, teams, and strategy. You need to see the whole board. Cloud infra spend is growing ~10% yearly, mostly due to AI. That spend needs direction. CPTOs are the ones who can match innovation speed with platform efficiency. Companies clinging to the old CTO/CPO split are playing a slower game. Integration is no longer optional - it’s a competitive advantage. So—what’s keeping your org from making the leap?
15
-
Varun Jain
Default • 9K followers
How I would hire a polyglot engineer. 1. Evaluate one strong area - backend or frontend or mobile. In rare cases somebody might have two of these with nearly equal expertise. 2. Fundamentals - DS, Algo, System Design 3. Flexibile mindset when it comes to stack - during screening and subsequent conversations 4. Build with AI - get them to build something on a language, platform or framework they haven’t previously by leveraging AI. Make this a discussion, as evaluation time is limited, don’t have somebody build a mobile app who has never built one before. Ex: if they have minor web/js experience make them build something on react. #GenAI #Copilot #cursor #windsurf #sofwaredevelopment #software #Codegen
9
-
Ben Taylor, CPA
SoftLedger • 5K followers
We signed a new customer yesterday that heavily uses Stripe for billing and has some complex multi-entity and multi-currency challenges. They tried to develop an integration with a couple other accounting solutions and while doing so, asked Claude Code what other systems they should consider. Claude suggested they explore SoftLedger and by the time we got on our first call, he already had a pretty good integration specification. So he got access to his account yesterday. This morning, this is the message received by our customer success team: "This is unbelievable. I would estimate I am 60-70% of the way through what I need to do. Brought in 23,000 journal entries overnight." Something has changed in the buying journey for companies seeking an integrated accounting solution and I love it. Accounting technology should be an API driven solution that conforms to a companies processes. Tools like Claude Code enable companies to build bespoke integrations for their internal processes like never before.
51
13 Comments -
Ayyoub El Amrani
Mirage Metrics • 5K followers
Hiring engineers for hard operational problems is different. Most candidates can write clean code. Fewer can sit with a dispatcher at 7am and understand why their shift planning breaks down. Fewer still can go back to the codebase and design something that actually works in production with those constraints. What I look for at Mirage Metrics is not just skill with Python, SQL, or distributed systems. It is a willingness to go into environments that are messy, sometimes chaotic, and keep digging until the real bottleneck shows up. That can mean inventory mismatches hidden in Excel, planning rules written on a whiteboard, or API endpoints that return inconsistent data depending on who queries them. The best engineers I have worked with are the ones who do not flinch at this. They treat debugging a customs declaration pipeline with the same seriousness as debugging a memory leak. They know that solving “boring” problems in data quality or workflow mapping is what unlocks everything else. On the technical side, I test for clarity. Can someone explain the trade-offs between running a model via an API versus hosting it on GPUs without getting lost in jargon. Can they design a schema validator that will still make sense six months later when requirements change. Can they write glue code that is robust, not fragile. On the personal side, I look for stamina. These projects are rarely about building a shiny feature in isolation. They require iteration with operators, late-night debugging of OCR failures, and the patience to integrate into legacy systems that were never designed for AI. If you want to work on AI for logistics, manufacturing, or mining, the question is not only whether you can code. It is whether you can hold your ground in the real world, in front of the people whose work depends on your system. That is the bar.
17
5 Comments -
Marcos Heidemann
symphony.is • 13K followers
While everyone was talking about Opus 4.6, for me the true killer feature of the recent Claude Code updates is the agent teams. It's been something i've been trying to achieve with customization for a while. Custom agents, orchestration scripts, specific CLAUDE.md instructions to coordinate work... with some degree of success. But what Anthropic shipped natively is a WHOLE different level. What makes this stand out is the inter-agent communication. We're not talking about simple fan-out/fan-in where you spawn workers and collect results. These agents talk to each other. Peer-to-peer messaging, dependency-aware task graphs that auto-unblock, agents that self-claim work from a shared task list. The lead can even enter Delegate Mode where it does ZERO implementation, only coordination. The image below is from one of my setups. A team manager orchestrating a librarian agent, a PhD lead, and 5 research sub-tasks with blocking dependencies. The librarian unblocks the research tasks, the PhD lead aggregates everything. All coordinated autonomously. And with this a whole new world of orchestration just unveiled itself. Distributing work across agents is the "easy" part. You break down tasks, assign owners, define dependencies. The HARD part, and what MOST stands out now, is aggregation. How do you take the output of 5 parallel agents, each with their own context window, and synthesize it into something coherent? That's the new skill. Anthropic themselves used 16 parallel agents to build a 100,000 line Rust C compiler that compiles the Linux 6.9 kernel. No human actively coding. ~$20,000 in API costs over ~2,000 sessions. We went from pair-programming with AI to managing AI engineering teams. The skills that transfer are the ones from engineering management: task decomposition, context management, knowing when to intervene vs let the team self-organize. This is a new paradigm, and i think it opens up several possibilities we haven't fully explored yet. ref.: https://lnkd.in/dVCe344z
68
12 Comments
Explore top content on LinkedIn
Find curated posts and insights for relevant topics all in one place.
View top content