Many private, venture-backed companies are facing this scenario (or something like it) right now. With timelines to exit much longer than they used to be, startups need new tools to retain and motivate employees for the longer haul.

Many such companies see restricted stock units (RSUs) instead of stock options as part of the solution. It can be a smart move—but only if done at the right time, with a clear sense of the potential pitfalls. We prepared this explainer with help from compensation experts at Sequoia, as well as actual scenarios that have emerged within our portfolio. We’ll walk you through the basics of RSUs: what they are, how they differ from stock options, and what type of companies might offer them. We’ll briefly cover common questions your employees may have. Then we’ll explore some real-world questions company leaders should think through before making a switch.

Let’s start with the basics.

What is an RSU?

A restricted stock unit (RSU) is basically a promise to grant someone a given number of shares in a company at a certain time—or when certain conditions are met. Typically, RSUs are either time-bound, linked to performance, or both. So a new hire’s RSUs might vest in tranches every year for five years. Or a long-time employee might be granted a chunk of RSUs that vest when the company hits a specific revenue milestone.

How is an RSU different from a stock option?

Options give an employee the option to buy a certain number of shares at a set “strike price” at any time. They offer the traditional startup dream: Get in on the ground floor of the next Google/Uber/etc., and when the company goes public, you can buy your options for the pennies they were worth when you started—and then sell them for the millions they’re (in theory) worth now.

RSUs, on the other hand, are simply worth whatever they’re worth when they vest. Unlike options, employees don’t have to pay anything to exercise them—once RSUs vest, they’re freely granted. From the employee perspective, it’s more like being granted equity at a big, established company: less potential for massive upside and overnight wealth, but more predictability.

Why startups make the switch from options to RSUs

RSUs tend to make more sense for companies at a later stage of growth. As your company grows and your valuation climbs, that strike price starts to become more of an issue—later hires see less upside potential might experience more of a scramble to pull together cash if they decide, for whatever reason, they need to exercise their options before an IPO. One portfolio company we spoke with recently explained their strategy: “We made the switch almost four years ago due to rising strike price/convergence.”  Another concurred: “We started to issue [RSUs] a few years ago during IPO readiness, and because strike price was getting quite high.”

Companies with large international operations may also see benefit in switching to RSUs. International taxes are already complicated and costly to manage, so a simple grant of shares at a predetermined time may work better than options for employees living overseas. One of our portfolio companies started issuing RSUs because they have a “huge presence in India, and not having a strike price helps attract talent.”

Eliminating the strike price and (from the employee’s perspective) upfront tax payment involved in exercising options streamlines the offering for employees, too. An RSU can essentially function as a bonus that is easily converted into cash in one step. As one HR leader at a mature portfolio company put it: “We’re still using options for our new-hire and refresh grants, but we introduced RSUs for retention and recognition grants. We wanted a way to differentiate these by offering an equity vehicle that didn’t require the individual to come out of pocket to exercise.”

Why RSUs are becoming more popular now

Over the past few years, we’re seeing more later-stage, larger companies stay private for longer as they wait for a liquidity event. Switching to RSUs makes sense for many of these larger companies because it offers more predictability and stability for employees who joined the company more recently.

RSUs vs. stock options: A comparison chart

Let’s review the key differences between RSUs and stock options:

To sum up, RSUs are a simple grant of shares that vest at a predetermined time and/or when the company hits certain milestones. Once they vest, they’re immediately available as shares, and employees can sell them right away.

Because RSUs tend to be granted by later-stage companies, there’s more predictability. Employees can be fairly confident that shares in an established company will be worth something in five years’ time. And because there’s no cost to exercise, even if the value of the shares dips between the time they’re offered the package and the time the shares vest, it will still probably translate into money in their pocket.

Stock options, by contrast, require the employee to make decisions, usually after talking to a tax professional. They must choose when to exercise the options—and if they don’t sell right away, they may end up paying alternative minimum tax in the year they exercise, and then paying capital gains tax when they eventually sell. Options are most attractive for employees when there’s a significant upside—when the exercise price is significantly lower than the price at which they’re able to sell. From the employee’s perspective, options do confer downside risk—if the company doesn’t succeed, the value of the shares could fall below the exercise price, making them worthless.

The leadership perspective: Downsides to consider

We’ve covered the basics from the employee’s perspective. Now let’s consider the company leadership perspective. Leaders should consider a few points before making the switch to RSUs from options:

• Internal divisions. Whenever you switch to RSUs, you’ll create a division within the company between early employees and later hires. Typically, companies grant fewer shares when they offer RSUs (because they involve less downside risk). So switching to RSUs too soon could create a group of ‘early RSU’ employees who benefit less from the company’s growth than peers who were hired earlier—and potentially even walk away with less cash altogether, because they’ve been granted fewer shares.

SOLUTION: Time the switch right. Wait until you enter that slower-growth stage, and/or until the strike price for options becomes problematic for employees.

• Tax headaches. Options create complexity because employees have to choose when to exercise them, but the lack of choice in RSUs means it’s up to you to structure an equity package to avoid causing unintended problems for employees. Because employees will be taxed when their RSUs vest, if RSUs vest before the company goes public, employees could potentially owe a tax bill on shares they can’t easily sell. This is particularly relevant when the blackout period occurs during the first few months after an initial-public offering and shares are listed. During this period, insiders, including employees, cannot sell their shares.

SOLUTION: This is why companies go for a double-trigger structure for RSUs. If shares don’t vest until X years have passed and the company has gone public, then employees won’t be taxed until they’re easily able to sell their shares to cover the bill.

• Employee confusion. Any kind of switch like this can create uncertainty and confusion for your people. If employees are already familiar with how options work, they may not be as prepared to manage RSUs.

SOLUTION: Make it simple. Offer employee education to make sure people are prepared for the different tax implications of their equity packages. Many companies also offer employees the option to withhold some of their shares when their RSUs vest, to cover the tax bill and make the process as smooth as possible.

• Raised expectations. Employees who do understand the differences between options and RSUs may take the switch to RSUs as a signal that you’re preparing for an IPO. If you’re still some years away from going public, you could be setting them up for disappointment.

SOLUTION: Timing is everything! Make sure you switch at the right time, when you’re prepared to send this signal to your savviest employees. Battery can help portfolio companies pinpoint the precise moment for their company’s trajectory. Contact us to discuss your situation.

Bottom line

If timed right, a switch to RSUs should be beneficial both for a company and its employees. As a company leader, you’re looking for the moment when offering options no longer makes sense from a burn perspective. If you’re finding that the spread between strike price and fair market value has narrowed enough that you have to offer more options to create the same financial value to the employee, it’s time to think about a switch. As an advocate for your employees, aim for the moment when the cost to exercise new options becomes too expensive, or the upside potential of new options levels off. If you choose your moment well, everyone should feel good about the change.

The post Restricted Stock Units (RSUs): When and Why to Make the Switch appeared first on Battery Ventures.

As in every industry, Excel is the workhorse financial professionals have relied on to handle calculations of arbitrary levels of complexity.  In fund accounting, it is typical for it to be employed twice: once by third-party fund administrators — relied upon by some firms for most finance operations — and again by internal teams to verify their work. But for all its strengths, Excel was not intended to serve as a transactional database, and its very flexibility allows users to create calculation logic too complicated to parse or debug.

Thus, as the industry scales, the cracks in Excel are widening: Spreadsheets multiply, errors compound, and firms risk institutional knowledge walking out the door with each departing employee.  Fund accountants spend days untangling or rebuilding models, reconciliations drag on between admins and teams, and GPs sometimes remain one step removed from the calculations that matter most.

Maybern*’s founders started with a straightforward “Excel to software” premise. A platform that generalized correctly across the breadth of financial “primitive” concepts used by firms would enable them to define their calculation logic lucidly with proper transactional histories, permissions, and audit trails in a single and dynamic performance book-of-record. Moreover, this logic would be simple to execute, and calculations that can take days to prepare could be handled in minutes.

Achieving this simplicity has been no small feat, especially given the diversity of limited-partner agreements (LPAs), side letters, and rounding conventions that must be correctly applied in each calculation. Instantiating this context precisely enough to deliver precision down to the penny requires deep understanding of the domain, an expert delivery methodology, and a calculation engine capable of representing all the nuances in play. The stakes are higher than just efficiency: Clean audits and avoidance of restatements are essential to the LP trust and regulator approval.

From our first conversation with co-founders Ross Mechanic and Ashwin Raghu more than two years ago, it was clear they built Maybern with this reality in mind. Engineers by training, they had lived the problem themselves at Cadre, a real estate investment platform, and immersed themselves so deeply in the mechanics of fund operations that, as one customer put it, “they should have honorary degrees in fund accounting.”

That fluency shows up in the product, a canonical data model for fund accounting that is built to serve as the system of record for every calculation. Each time a fund is onboarded, Maybern ingests its LPAs, side letters, and historical calculations, then abstracts them into reusable primitives: fee rules, waterfall tiers, rounding conventions, and more. These primitives form the foundation of a library that grows to capture the quirks and edge cases of the industry.

Over time, we think this library becomes something larger: a living alphabet of fund economics, powering a scalable system that makes even the most complex fund structures simple to model, audit, and trust.

In just 18 months since launch, Maybern is already partnering with some of the largest funds across the alternatives landscape. CFOs describe how Maybern provides them leverage as operational complexity grows — ensuring accuracy, reducing latency in critical calculations, and delivering both tighter control and greater transparency. For internal fund accounting teams, Maybern is unlocking time savings and beginning to enable more-sophisticated forms of reporting and scenario modeling. The platform is also laying the foundation for AI-driven LPA ingestion and auditability, providing not just results but clear explanations of every calculation.

Battery is proud to partner with the Maybern team on this journey and to announce that we are leading the company’s $50 million Series B, alongside our peers at Primary, Human Capital, and MetaProp Ventures.

The post Maybern: The Performance Book of Record for Fund Accounting appeared first on Battery Ventures.

This shift isn’t just anecdotal. According to a BCG survey of enterprise Chief People Officers (CPOs), among companies experimenting with AI at all, 70% are doing so within HR, with talent acquisition ranking as the top HR use case. And 92% of these enterprises already see the benefits of AI; more than 10% report productivity gains of 30% or greater.

AI is no longer a future consideration in talent—it’s part of the present-day toolkit. Our portfolio company survey confirms this trend: Sixty-eight percent of recruiting execs say AI is already impacting hiring at their companies, and nearly two-thirds actively use AI recruiting tools.

So what benefits can recruiters derive from AI tools today? Yes, AI delivers efficiency and reduces administrative tasks. But it also offers value-added benefits:

• Stronger hiring decisions, through structured interview analytics and more consistent evaluation.
• Broader candidate reach, via AI-powered sourcing that surfaces talent from unexpected places.
• Improved candidate experience, by reducing delays, ghosting and bias-prone interactions.

That said, it’s important to acknowledge that AI’s rise in recruiting has created real friction for some job seekers—in some cases, longer application processes, automated rejections, and the sense of shouting into a void. When thoughtfully applied, however, AI in recruiting can meaningfully improve the hiring experience on both sides and help recruiters make better, more thoughtful hires.

Whether you’re an AI newcomer looking for tools that are easy to adopt or a power user aiming to uplevel your stack, we’ve organized the top AI tools for recruiters by each stage of the hiring journey—complete with links, superpowers and limitations of each tool.

Step 1: Writing the job description & aligning internally

Crafting a compelling, inclusive job description (JD) and gaining alignment across stakeholders can be a heavy lift, especially when timelines are tight. Many teams use tools like ChatGPT to quickly generate first drafts of job descriptions, provide role templates, outreach emails or even interview questions. A number of all-in-one recruiting platforms such as Greenhouse, Ashby and Kula have these capabilities as well, with the goal of streamlining recruiters’ workflows and aligning internal teams faster. Brighthire is also worth mentioning: its toolkit encompasses a JD generator, interview tools, analytics and more. HiBob* is an AI-powered full stack platform that integrates the full employee lifecycle – from hiring to retention. HiBob’s Hiring offering also includes JD builders, applicant tracking system (ATS), collaborative workflows and other key features to streamline the recruiting process.

To improve tone and inclusivity, writing intelligence platforms such as Datapeople by Payscale analyze job language in real time, flagging jargon, suggesting stronger verbs and surfacing potential biases that might limit the candidate pool.

Where AI tools fall short: In many cases, they still struggle to reflect the nuance of brand-new or hybrid roles. And achieving consensus on the profile of an ideal candidate still requires old-fashioned, human conversation.

Step 2: Posting the role & finding talent

Once the job description is approved, the next challenge is attracting qualified candidates, especially for competitive or hard-to-fill roles. AI can help widen the net by sourcing from multiple platforms, enriching candidate data and identifying promising fits based on skills rather than job titles alone. It also enables recruiters to engage candidates at scale through personalized outreach and automated follow-ups.

For broader talent discovery, Juicebox is a good option. The company’s AI-powered search tool PeopleGPT helps jumpstart the process using natural-language queries. Hiring managers can search over 30 data sources, filtering for desired candidate criteria, shortlist favorites and integrate email, enabling faster and more efficient candidate engagement.

Recruiters looking to fill roles requiring specialized skillsets are turning to tools like Tezi or ama. These tools use AI to identify highly matched candidates who may not surface in traditional databases. Each tool uplevels searches in its own way.

Other sourcing platforms aggregate and enrich profiles across multiple data sources, enabling deeper research into candidate experience and potential fit. Once again, Juicebox is a prime tool. With a massive database of over 800 million professional profiles, few talent pools remain hidden. In addition, its filters, automated outreach, talent market insights and ATS/CRM integrations make talent sourcing both precise and streamlined.

For hiring managers who believe the best candidates aren’t looking for a new job, Boostie is worth exploring. This tool sources candidates who might be a good fit (based on AI-driven data insights and online behaviors) but haven’t actually applied for the role. Using this tool calls for a careful touch, though, to avoid feeling intrusive.

In addition to Juicebox, Noon and Pin are also notable end-to-end talent sourcing platforms. Noon rapidly learns your needs, scaling patterns and writing style, mimicking your best recruiters. And Pin is like a digital recruiting assistant, from outreach to interviews.

Where AI tools fall short: No tool can adequately assess softer qualities like team chemistry, growth potential or adaptability. Recruiters’ instincts remain critical in moving beyond resumes to people.

Step 3: Screening & coordinating interviews

Reviewing resumes, narrowing down the candidate pool, and managing interview logistics are major time sinks for recruiters. AI tools can triage applicants, flag top matches, automate communications and coordinate scheduling across busy calendars.

Implementing applicant tracking systems like Greenhouse, Gem, Kula and Ashby should be tablestakes to improve the entire recruitment process, from finding candidates to scheduling interviews, streamlining communications and tracking evaluations. Special features include Greenhouse’s consistent and standardized evaluation of candidates throughout the interview process to prevent bias and easily benchmark potential fits. Ashby, similarly, layers AI into every phase of the hiring cycle, with automation and customization integrations for companies of all sizes. Kula is another AI-native recruiting platform that supports sourcing, outreach, interview assistance and analytics with a single tool. HiBob’s integrated ATS bridges the gap between recruiting and workforce planning. By centralizing candidate, employee, and business data, it enables HR leaders, hiring managers, and finance teams to make smarter, data-driven hiring decisions in real time.

Gem also blends CRM features with scheduling support, candidate sourcing and pipeline analytics. Modernloop reduces recruiter workload by automating interview scheduling across teams and time zones. Persona* has built more horizontal agentic AI avatars that can be applied to the interviewing use case.

Where AI tools fall short: AI-based screening can help you find underrepresented talent more effectively, but they can just as easily introduce bias if not properly audited. Moreover, automating communications too aggressively can alienate candidates and contribute to the “black hole” feeling people often describe when applying to jobs.

Step 4: Conducting interviews

AI can support interviewers with structure and consistency, generate post-interview analytics and surface coaching opportunities for interviewers. Video-based tools also offer standardized assessments that help reduce subjectivity.

Metaview and Granola are AI-native tools with features designed to help with this stage. Metaview records and summarizes interviews automatically, generating structured notes and scorecard inputs to optimize and speed up decision-making. Granola transcribes and organizes meeting notes (including interviews); smaller teams who don’t need a full-featured recruiting platform like this tool in particular.

Several other notable platforms can also take interviewing to the next level. Alex allows hiring managers to screen more qualified candidates by engaging in live two-way conversations. Braintrust AIR helps eliminate bias and identify top talent by generating detailed scorecards of video interviews based on key criteria. Ribbon enhances the candidate experience by keeping potential hires engaged and informed. And HeyMilo incorporates an AI Voice agent that adapts during interviews based on candidates’ responses so that follow-up questions dive deeper into a candidate’s skills.

Where AI tools fall short: Human judgment and rapport-building remain irreplaceable. AI can’t evaluate team chemistry or adapt questions dynamically to the same degree a skilled interviewer can.

Step 5: Making offers & closing candidates

The offer stage is all about speed, clarity and persuasion. AI tools can auto-generate offer letters, surface compensation benchmarks and identify which candidates are likely to accept or walk. These insights can help teams act quickly and avoid common negotiation pitfalls.

Ashby include features to streamline this phase, such as pre-built offer templates, customizable approval workflows and data on close rates. Ashby makes it particularly easy to track offer-stage performance by recruiter or role type, giving teams visibility into where they’re winning or losing candidates. Gem, Greenhouse, Kula and HiBob also have offer-approval features.

Where AI tools fall short: Final-stage decisions are deeply personal. AI can’t replace the human touch in negotiating details, answering nuanced questions, or managing competing offers with empathy.

What AI can’t (yet) fix: Common pitfalls for talent teams

While AI brings powerful capabilities, it also introduces new risks. Here are a few common issues to stay aware of:

• Bias in the system: AI models trained on historical data can perpetuate existing biases, especially in resume screening and ranking. Regular audits and human-in-the-loop systems are essential.
• Candidate skepticism: Job seekers may perceive AI-driven hiring as impersonal or unfair. Over-automating outreach or screening can erode trust and damage employer brand.
• Compliance concerns: Some jurisdictions have stricter legal requirements around AI usage in hiring (e.g., NYC’s AEDT law). Make sure tools are properly vetted.
• One-size-fits-all solutions: Not all roles benefit equally from AI. High-volume, transactional hiring may lend itself to automation—but executive or deeply technical searches still require nuance.

The bottom line

AI isn’t replacing recruiters, it’s empowering them—with faster execution, stronger decision-making and smarter candidate engagement. From job kickoff to closing, today’s tools can meaningfully improve both the recruiter’s workflow and the candidate’s experience.

The post Hiring in the Age of AI: What Talent Teams Should Know for 2026 appeared first on Battery Ventures.

That’s a missed opportunity. As CFOs take on broader mandates around data, capital efficiency, and long-range planning, I believe engineering productivity belongs squarely within their remit. But measurement requires alignment. To get there, CFOs and CTOs must learn to speak each other’s language and build a shared framework for tracking what matters.

Here are four steps to get you started:

1. Start with a common lexicon—and a shared commitment
   It’s easy to analyze pipeline velocity or CAC payback. But what’s the engineering equivalent of these metrics? The answer varies depending on a company’s product maturity, development model and team structure. That’s why the goal isn’t a universal dashboard; it’s an evolving framework, co-developed by finance and engineering and grounded in trend-based tracking and continuous iteration. For example, in a company shipping weekly releases, it might make sense to measure deployment frequency and mean time to recovery (MTTR). In a company building a complex infrastructure product with longer development cycles, planning accuracy and cycle time might be more relevant metrics. A CFO doesn’t need to know what every pull request does. But they do need to understand how engineering is allocating resources, delivering value and responding to change. Moreover, CFOs can bring structure to the conversation: aligning metrics to investment theses, time horizons and business outcomes.
2. Build a consistent communication rhythm
   We often ask CFOs when they last had a one-on-one with their CTO. The answer, more often than not: “It’s been a while. ”For CFOs, understanding engineering productivity isn’t about micro-management—it’s about visibility. Regular touchpoints, monthly or even quarterly, can help bridge the gap. These conversations shouldn’t just be about budgets or headcount. They should include discussions of development goals, challenges and how engineering output ties to strategic priorities. CFOs can help clarify what trade-offs the business is willing to make (e.g., time-to-market vs. technical debt), while CTOs can shed light on where additional investment would accelerate velocity or improve quality. When these conversations are grounded in data, they move from abstract to actionable. Of course, CFOs must also be thoughtful about how they approach these conversations. A common misstep is leading with budget scrutiny or requests for cost cuts, which can put technical leaders on the defensive. Instead, CFOs can frame their involvement as an opportunity to unlock more impact from engineering, not limit it. One powerful way to earn trust is by asking how finance can help remove friction, asking questions like “Where do we lose momentum?” or “Is there anywhere we’re underinvesting that’s slowing you down?” By showing genuine curiosity and offering operational leverage—whether that’s better forecasting, smarter capital allocation or tools to surface hidden bottlenecks—CFOs can become valued partners, not gatekeepers.
3. Choose metrics that evolve over time
   Engineering productivity is multidimensional: There’s no single metric that captures it all. Instead of searching for the perfect KPIs, choose a handful that provide directional insight into throughput, speed, quality and team health. Start by asking: What are we trying to improve? Then align metrics accordingly. You don’t need to measure everything all at once. In fact, the most effective metrics programs start small, focusing on a few data points that help explain what’s working, what’s slowing things down and what’s improving over time. At the end of this post, I’ve included a sample table of commonly used engineering productivity metrics, complete with definitions and benchmarks, to help jumpstart those conversations.
4. Start with a spreadsheet—and scale from there
   Don’t over-engineer the process. In the early days of this new collaboration, a shared spreadsheet or lightweight dashboard can go a long way towards building trust and visibility. The goal isn’t to launch a full-blown reporting suite overnight. It’s to establish a habit of reviewing and discussing metrics together on a regular cadence. As teams mature and the need for scale increases, purpose-built tools can dramatically improve the fidelity and timeliness of your metrics. For example, LinearB* provides real-time visibility into engineering performance with minimal setup. It integrates directly with your existing developer tools (e.g., Git, Jira) and automates reporting on metrics like cycle time, deploy frequency and code churn—helping teams identify inefficiencies, reduce bottlenecks and accelerate delivery. Tools like this help transform engineering conversations from anecdotal to analytical—and make it easier for both finance and engineering leaders to work from a shared source of truth.

Final thoughts: Align on the why

Ultimately, engineering is one of the largest, most complex and least understood cost centers in modern software businesses. CFOs and CTOs who align early—on goals, language and metrics—are far better positioned to optimize both investment and impact.

Remember, this isn’t just about improving margins. It’s about staying competitive, delivering value to customers faster and positioning R&D as a strategic advantage. When finance and engineering collaborate, the result isn’t just better measurement, it’s better business.

Appendix: Engineering Productivity Metrics – Definitions & Benchmarks

CFOs can use the table below to familiarize themselves with common engineering metrics. Don’t think of these as rigid KPIs–they’re more directional benchmarks to help CFOs and CTOs start speaking the same language, ask better questions, spot patterns and make smarter trade-offs together.

The information contained here is based solely on the opinion of Alex Auchter, and nothing should be construed as investment advice. This material is provided for informational purposes, and it is not, and may not be relied on in any manner as legal, tax or investment advice or as an offer to sell or a solicitation of an offer to buy an interest in any fund or investment vehicle managed by Battery Ventures or any other Battery entity. The views expressed here are solely those of the author.

*Denotes a Battery portfolio company. For a full list of all Battery investments and exits, please click here.

The information above may contain projections or other forward-looking statements regarding future events or expectations. Predictions, opinions and other information discussed in this publication are subject to change continually and without notice of any kind and may no longer be true after the date indicated. Battery Ventures assumes no duty to and does not undertake to update forward-looking statements.

The post 4 Ways Software CFOs Can Partner with CTOs to Unlock Engineering Productivity appeared first on Battery Ventures.