Of all the late-stage private companies that survived both the 2022 SaaS reset and the beginnings of what most investors are calling “the SaaSpocalypse,” few have done so as quietly as Gusto. The company recently announced that it surpassed $1 billion in earned revenue over the last twelve months while being cash flow positive for over two years. By any standard measure of company quality, Gusto is one of the best-built businesses in private SaaS.

Still, Gusto’s valuation has been essentially flat for the last four years while revenue has nearly tripled. Much of that is multiple compression (their 2022 Series E was done at ~21x ARR), but the company is also facing competitive pressure from several directions: legacy incumbents, modern HR suites, embedded payroll providers, and AI-native startups.

Is Gusto a growing and durable business that can withstand the public markets? The rest of this piece walks through Gusto’s founding, the four layers of its infrastructure stack, their acquisition of Symmetry, the financial profile and valuation, the competitive landscape, and the bull and bear cases I see.

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Founding Story

Gusto was founded in 2011 by CEO Joshua Reeves, CPO Tomer London, and Head of Technology Edward Kim. While each brought different skills and experiences to the company, the similarities are striking: all three are Stanford-trained electrical engineers, all three had already founded and sold a company, and all three have a small-business owner in their families.

The team incorporated in October 2011, joined Y Combinator’s Winter 2012 batch, and launched ZenPayroll publicly in California in December 2012 with a $6.1M seed round, then the largest YC seed in history. The investor list included Drew Houston, Aaron Levie, David Sacks, Jeremy Stoppelman, Salesforce, and Google Ventures. The original product served only California; over the following eighteen months, the team expanded state-by-state, building out the regulatory and tax-filing infrastructure that would later become the company’s hardest-to-replicate asset.

The September 2015 rebrand from ZenPayroll to Gusto coincided with the launch of health benefits and workers’ comp, and with the implosion of Zenefits, whose similar name had created persistent market confusion. Fourteen years in, Reeves, London, and Kim are all still in their original roles, an uncommon tenure profile for a company at this stage.

Payroll Infrastructure

Gusto’s defensibility lives in four stacked layers:

Compliance:

A small business with employees in even a single state has to file federal payroll taxes with the IRS and SSA, calculate withholdings at the state level, register with state unemployment and labor agencies, manage workers’ comp, and handle paid family leave filings where required. Add a second state and the surface area roughly doubles. Add remote employees and contractors and the registration burden compounds. Companies with fewer than fifty employees face roughly $14,700 per employee per year in compliance costs.

The compliance layer at Gusto splits cleanly into calculation and registration. The calculation engine handles the tax math across jurisdictions and is built on Symmetry, the tax engine Gusto acquired in 2021 (more on them later). The registration side, which Gusto historically outsourced through a partnership with Middesk, is now being brought in-house through the Mosey acquisition. Mosey automates state and local business registration, payroll account setup, paid family leave filings, workers’ comp registrations, and Secretary of State filings. The combined product, Gusto Business Compliance, is scheduled to launch later in 2026.

Licensing:

This is what legally permits Gusto to move customer money. Every payroll run involves Gusto pulling funds from a customer’s bank account and disbursing them to employees and tax authorities. In most states, that activity now requires a money transmitter license (MTL), the same regulatory regime that governs Stripe, PayPal, and Coinbase. The MTL framework exists because state regulators want oversight of any entity holding customer funds in transit: licensed entities post surety bonds (often $500K to $1.5M per state), submit to regular financial examinations, and maintain AML/KYC programs.

Payroll companies historically avoided this regime by operating under “agent of payee” exemptions, a legal theory that holds the payroll provider is not transmitting money on its own behalf but is acting as the authorized agent of the employer paying the employee. Under that theory, no MTL is required. It has an enormous structural advantage where it applies: no surety bond capital tied up per state, no state-by-state examinations, no separate compliance overhead. Around 2020, however, state regulators began rejecting the agent-of-payee theory for modern cloud payroll providers and pushing them toward direct MTL registration.

Gusto was the first major modern payroll company to capitulate. Following enforcement actions from Vermont, Connecticut, and New Hampshire in 2021, Gusto launched a multi-year licensing project and now holds MTLs directly in most US states. The strategic effect is that Gusto absorbed the first-mover regulatory tax. Any new entrant must now pre-license rather than ship-then-settle, which adds 18-36 months and significant surety bond capital per state to the timeline. Rippling acknowledges it is “seeking money transmitter licensing, where applicable” for its Rippling Payments subsidiary, putting it years behind Gusto on the regulatory build-out. The incumbents have each taken a different path through the regulatory shift, which I’ll cover in the banking section below.

Banking:

This is the operational rails the licensing permits and the compliance requires. Once Gusto has legal authority to move money and a compliance obligation to disburse it correctly, it needs actual bank accounts and ACH origination capacity to do it. Gusto operates through FDIC-member sponsor banks rather than holding direct Federal Reserve master account access, with customer balances spread across a deposit network of dozens of partner banks to maintain pass-through FDIC insurance well above the $250K per-account cap. The architecture is deliberately redundant: multiple sponsor bank relationships that can be hot-swapped if one fails.

The redundancy was vindicated during the March 2023 SVB collapse, when Gusto’s payroll operations were unaffected because customer funds were spread across the deposit network. Rippling, by contrast, had its primary banking relationship at SVB and had to wire $130M of its own capital to bridge customer payroll while raising a $500M emergency round.

In 2019, ADP secured a federal trust bank charter (not FDIC insured) from the OCC, which lets it hold client funds directly and preempts state MTL requirements entirely. They transitioned away from state MTLs once the trust bank was operational. Paychex and Intuit operate under direct state-by-state MTLs, the same architecture as Gusto. Intuit also partners with a network of banks, similar to Gusto.

The tradeoff here is float income. Because ADP holds client funds directly in its trust, it captures the full interest yield on the ~$37 billion in average client balances, generating > $1 billion in high-margin interest income in FY2025. ADP actively extends the maturity of those funds out to five-year AAA paper to capture term premium, then uses short-term financing to bridge daily disbursement needs. Gusto’s sponsor bank model gives up most of that upside: the sponsor banks earn the spread on customer funds, and Gusto captures only a fraction through revenue-share arrangements. In a high-rate environment, ADP’s architecture wins on float economics. In a zero-rate environment, the float advantage compresses to near-zero and Gusto’s leaner architecture looks correctly built.

International settlement runs through a Wise integration (launched March 2026) and a USDC stablecoin pilot via Zerohash (launched January 2026), so Gusto is now plumbed into both the traditional correspondent banking rails and the stablecoin rails.

Distribution:

This is how the underlying infrastructure actually reaches customers. Gusto runs two distinct go-to-market motions, and the dual GTM is the underappreciated strategic asset.

The direct business serves 500,000+ SMBs through a flat fee and pricing tiers ranging from $6 to $22 per-employee-per-month. This is the original and core business.

The embedded business, launched in 2021, exposes Gusto’s payroll infrastructure as APIs to other software platforms, with 800+ partners reaching over 1 million SMBs collectively. The marquee wins are bank-channel partnerships: Chase Payment Solutions (~5 million SMB clients, launched 2023) and U.S. Bank Payroll (1.4 million SMB clients, launched September 2025). No embedded payroll competitor has signed tier-1 banks, making them one of the more established choices.

Acquisition of Symmetry

While Gusto recently acquired Guideline and Mosey to expand their product offerings and build a larger suite of HR products and services, their acquisition of Symmetry is the one to pay the most attention to.

In 2021, Gusto acquired Symmetry Software, a Phoenix-based payroll tax engine founded in 1984 by Tom Reahard. Symmetry calculates payroll tax for approximately 60 million working Americans, roughly 40% of the U.S. workforce, and licenses its engine to Deel, ADP, and Paychex, among others.

The most obvious benefit is cost: Gusto stopped paying per-calculation license fees, and the unit economics of every payroll run improved by a measurable margin. Less obvious is the integration benefit. Owning the tax engine means Gusto can ship product changes (new state launches, new tax categories, new compliance edge cases) without negotiating roadmap with a vendor. Gusto Embedded almost certainly does not close the U.S. Bank or Chase deal without owning Symmetry, because banks have a much higher bar for the tax calculation layer than vertical SaaS does, and owning the calc engine is what makes the audit and error-rate story defensible.

The most underrated benefit is structural. Gusto now owns the regulated substrate that its largest direct competitors depend on. Deel, ADP, and Paychex all run their payroll math through code that Gusto controls. The relationship is deliberately arms-length: Symmetry continues to operate as a standalone brand under CEO Elizabeth Oviedo and Chairman Tom Reahard, reporting to Tomer London at the parent, and no Symmetry customer has publicly left following the acquisition. But the strategic posture has changed. Gusto sees its competitors’ jurisdiction expansion roadmap implicitly (which states they’re requesting tax tables for, which edge cases they’re filing tickets on), collects high-margin recurring license revenue from those competitors, and holds a soft kill switch on the underlying infrastructure that it will never use but doesn’t need to. The Symmetry acquisition could become foundational to their future.

Financials and valuation

Based on publicly available sources, I can piece together Gusto’s topline revenue and valuation multiples over time. Revenue has increased about 10x in the last 6 years and the company has been cash flow positive since early 2023 while serving 500,000+ small businesses directly and over 1 million indirectly.

Gusto last raised primary capital in May 2022 at a $9.4 billion pre-money valuation, and the June 2025 Ontario Teachers' Pension Plan tender offer set a secondary mark at $9.3 billion, slightly down. Over the same window in which revenue 10x'd, the implied EV/ARR multiple compressed from roughly 37x in 2019 to 8x today.

Gusto’s current valuation gives it a strong premium to public HR SaaS companies, but growth and profitability seem to justify it. When looking at the other two scaled private HR Tech companies, Gusto’s multiple is considerably lower. Deel recently announced that they had passed $1.4 billion in ARR and are profitable, while Rippling recently announced that ARR passed $1 billion, growing 78% and reaccelerating.

Competition

Deel and Rippling are two of the more visible names in Gusto’s competitive set, but they are in just one of five distinct buckets of competition. Each bucket pressures Gusto from a different angle:

• Incumbents: ADP, Paychex, and Intuit collectively own the bottom and middle of the SMB market. ADP has shipped an “ADP Embedded Payroll” SKU but faces classic innovator’s-dilemma economics: API-distributed payroll cannibalizes their direct seat revenue. Intuit gated its Payroll Compensation API behind Gold and Platinum partner tiers in July 2025, a half-step move. The incumbents are not asleep, but their structural incentives slow the response.

• Modern direct competitors: Rippling and Deel have both lapped Gusto on valuation by going broader rather than deeper. Rippling just announced $1 billion ARR growing 78% and reaccelerating; Deel passed $1.4 billion ARR while remaining profitable. Both are pulling Gusto’s natural upmarket expansion path away from it: Rippling via its IT-and-Spend bundle, Deel via global EOR and contractor payments. The conventional read is that they are up-market and Gusto is safe at the bottom, but Rippling has also been moving downmarket.

• Modern embedded: Check is the serious threat in this bucket. Stripe-funded ($35M Series B in 2021, $75M Series C in 2022), 35K+ businesses and 4M+ employees across all 50 states as of early 2022, and arguably ahead of Gusto Embedded on developer experience. Check likely built its own tax engine in-house, while Salsa licenses symmetry. Gusto is the only one to publicly sign a tier-1 bank, a likely multi-year sales cycle.

• Stripe: Stripe does not currently offer payroll, but it led both of Check’s major rounds and has been steadily building an SMB infrastructure stack via Atlas (incorporation), Bridge (stablecoin, acquired 2024), and Privy (crypto wallets, acquired 2025). If Stripe ever wants payroll, the cleanest path is to acquire Check. That would collapse the embedded payroll market into a Stripe-vs-Gusto duopoly and put Gusto Embedded’s bank-channel distribution under price pressure.

• AI-native entrants:Warp runs on Check’s API and uses AI agents to automate state tax registrations, filings, and notice resolution for startups. Central took a hybrid managed-service approach with AI agents and was recently acquired by Mercury, a business banking platform. These companies are betting on agents commoditizing the UX layer of payroll, but they aren’t going after licenses and compliance infrastructure the same way that Gusto is.

Bull & bear debate

What the bulls see:

• The cleanest pre-IPO comp in HR tech: $1 billion in revenue, cash flow positive since early 2023, 20%+ growth, founder-led with intact original team, ~8x trailing revenue. Growth adjusted valuation is in line with public comps, and a steep discount to Rippling and Deel on a category-comparable basis.

• Embedded is the next leg, and it’s just starting: Gusto is already reaching 1M+ SMBs and is live with two major financial institutions. If Embedded crosses 25% of revenue by 2027-2028, the company could get re-rated as fintech infrastructure (Stripe & Visa comparable, 10-20x revenue) rather than SaaS payroll (5-7x). The bank channel is the single best distribution moat in the category.

• Licenses and infrastructure keep Gusto insulated from the SaaSpocalypse: Tax engines and payment licenses are unlikely to be vibe coded. Even if someone does vibe code their HR, they are likely to use Gusto’s embedded offering.

What the bears see:

• The opportunity isn’t big enough for the moment: Public investors seem most excited about companies training frontier models, going to Space, or doing both. Institutional investors may not be excited about SMB software.

• Lack of high margin float revenue: Because of Gusto’s banking infrastructure, they own considerably less float than ADP. At ~95% margin, this revenue is highly accretive to earnings.

• Rippling and Deel target a bigger and more durable market: Both of these companies appear to have larger topline growth rates than Gusto and are going bigger businesses, which pay more money, are easier to upsell, and less likely to churn. Their multiples are much higher but it’s plausibly justified.

Conclusion

Gusto is valued as a premium, high-growth SaaS payroll business because they are one. They’re also building adjacent products and services for SMBs while vertically integrating the financial infrastructure underneath. The MTL portfolio, Symmetry ownership, multi-bank deposit network, Mosey, and tier-1 bank channels through Embedded add up to something closer to a regulated infrastructure layer for SMB payments and compliance.

I’ll be tracking whether Embedded crosses 25% of revenue (the threshold that justifies a fintech infrastructure comp set rather than SaaS payroll), whether Rippling’s downmarket motion accelerates into Gusto’s core SMB base, and whether Stripe makes a move on Check. Any of the three would reset the multiple.

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Thanks for reading! Let me know in the comments if you prefer longer deep dives like this to the usual earnings & funding summaries.

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Footnotes & Disclosures:

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

Following a massive quarter to close out 2025, the HR Tech sector has entered a period of relative calm to start 2026. While deal counts in many areas remained steady, the total capital invested saw a noticeable decline, largely due to a lack of outsized funding rounds.

Concentration and consolidation

Most outsized funding rounds have gone into two distinct areas:

1. All-in-one HCMs: Horizontal platforms like Rippling and Deel offering core HR, payroll, and adjacent solutions like interview scheduling & performance management

2. Data annotation marketplaces such as Mercor, Turing*, and Micro1 helping AI labs and enterprises source experts for model post-training

All of the above companies announced large deals in 2025, but none of them did in the last quarter, which is why overall capital is down. 4 companies raised $50M+ rounds in 2025 Q4 and typically 2-3 companies raise a round like that each quarter, but nobody did this last quarter.

On to the regular funding highlights!

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Seed Stage Financings

• Seed stage capital and deal sizes pulled back after a few quarters of acceleration

• Companies have opted not to disclose valuation in press releases; valuation visibility has been trending lower. Median valuations have been steadily increasing, likely a result of smaller valuations not being disclosed

Notable Seed Rounds

• HCM designed to work on top of ServiceNow raises $8M seed investment from Stalwart Ventures, Bell Ventures, and ServiceNow Ventures

• AI interviewing platform Ezra AI raises $3M seed investment from Penny Jar Capital and LMNT Ventures after participating in a16z Speedrun (and purchased by Greenhouse just a few weeks later)

Series A Financings

• Only 3 Series A deals this quarter; the lowest in a single quarter since 2018. Humands’s $66M raise kept overall capital this quarter relatively high

• All three companies that raised this quarter did so 2-3 years since their last round

Notable Series A Rounds (Well, all of them)

• Deskless WFM platform Humand raises $66M Series A from Kaszek and Goodwater Capital

• Benefits operations platform Pasito raises $21M Series A from insight Partners

• SMS-based frontline workforce communications platform Yourco raises $6M Series A from High Alpha

Late Stage Financings (Series B and beyond)

• Deal counts remained steady but overall capital was down due to a lack of big fundraises

• Compa*, Paraform, and Juicebox’s Series B rounds took up about ⅔ of all growth capital this quarter

• Median valuation was weighed down by a few companies (BenefitBay, Engin) raising small late-stage rounds

• Time between rounds has begun to inflate as companies wait longer to fundraise

Notable Growth Rounds

• Recruiter marketplace Paraform raises $40M Series B from Scale Venture Partners

• Compensation intelligence platform Compa* raises $35M Series B from Jump Capital

• AI recruiting platform Juicebox raises $80M Series B from DST Global

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

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Footnotes & Disclosures:

*Indeed is an investor through their HR Tech Investments affiliate

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

For years, autonomous driving was measured in test miles and pilot programs. In 2026, the metrics that matter are paid rides, contracted freight revenue, and production facility buildouts.

Waymo, the category creator, hits escape velocity

The Alphabet subsidiary is now delivering 500,000 paid rides per week across 10 US cities. That figure is up 2x from last year and 10x from May 2024. The company is targeting 1 million weekly paid rides by the end of 2026.

The expansion pace is staggering. At the start of 2025, Waymo operated in three cities: Phoenix, San Francisco, and Los Angeles. Over the past year it added Austin, Atlanta, Miami, Dallas, Houston, San Antonio, and Orlando. The company has announced plans for 20+ additional cities, including New York, London, and Tokyo. They have an active fleet of ~3,000 cars.

Waymo capitalized on this success by raising $16B at a $126B valuation. While the company is still majority owned by Alphabet/Google, this investment was led by outside investors Dragoneer, DST Group, and Sequoia Capital.

Their safety record strengthens the bull case. Across 127 million miles of autonomous driving, Waymo reports 90% fewer serious injury-causing crashes than human drivers and 82% fewer airbag deployments. While Waymo is being investigated by the NHTSA over a recent accident, their overall safety statistics appear fantastic.

Waymo has the first mover advantage in AVs and is acting like it, pursuing growth while positioning themselves as the premium rideshare option. A recent study saw that Waymos were ~13% higher than Ubers on average, although the premium was just 2% on rides between 3-6 miles.

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Here come the challengers

Waymo proved that consumers want AVs and provided a regulatory framework for competitors, who are eager to scale their operations.

Zoox, Amazon’s autonomous vehicle subsidiary, is live in Las Vegas and San Francisco with roughly 13 active purpose-built robotaxis across the two cities. The company has carried over 350,000 riders and logged nearly 2 million autonomous miles to date. Unlike Waymo’s retrofitted cars, Zoox vehicles have no steering wheel, no pedals, and seats that face each other.

The big unlock for Zoox is revenue. Zoox rides are currently free because the company is waiting on an NHTSA exemption to operate commercially. A 30-day public comment period opened March 11, with a decision expected in April. CEO Aicha Evans has said they are “ready to charge” the moment approval comes through. An Uber partnership will make Zoox available through the Uber app in Las Vegas starting this summer. Plans to launch in Austin and Miami are underway, which would bring the fleet to roughly 100 robotaxis across four cities.

Tesla is finally launching unsupervised robotaxi rides in Austin after months of operating with a safety driver, which is still the case in the Bay Area and majority of Austin. The company unsurprisingly has ambitions of a fast takeoff, planning to both expand into 7 additional cities and begin cybercab production in 2026.

What separates Tesla from the others is that their cars rely only on cameras and forgo the expensive but reliable LiDAR sensors. Scaling a fleet without LiDAR sensors is theoretically possible and has much more attractive unit economics, but safety record is less proven. Putting my regulator hat on, I’d have a hard time letting sensor-light AVs operate in my jurisdiction when there are 4-5 other providers with robust sensors looking to operate. Safety has to come before unit economics and Tesla is trying to jump the gun with unsafe, unproven AVs.

Wayve and Nuro are also testing in different cities, adding to a growing list of companies drafting behind Waymo’s lead.

Waymo laid the groundwork and the others could scale quickly

Waymo didn’t just prove consumer demand for robotaxis. It established the regulatory playbook, the safety data benchmarks, and the public trust that every competitor now benefits from. Cities that approved Waymo have a template for approving the next provider. Consumers who rode in a Waymo are primed to try a Zoox or a Tesla. The hardest part of building a new market is convincing people it should exist. Waymo did that. Challengers now have an easier path to execution.

That pattern is now repeating in trucking.

Long haul trucking crosses the commercial chasm

Driverless trucks are on the road and two companies, each with their own distinctive approach, are leading the charge. Aurora Innovation has autonomous trucks driving on public Texas highways while Kodiak Robotics has autonomous trucks hauling frac sand on private industrial roads in West Texas. Both companies hit key milestones in 2025.

These companies also have shared DNA from early self-driving pioneers. Aurora’s co-founder and CEO Chris Urmson spent 7 years in Google’s self-driving group before leaving in 2016 to start Aurora and focus on trucks. Kodiak’s co-founder and CEO Don Burnette spent 5 years in the same Google group before joining Otto, the self-driving company that Uber acquired for $700M and later shut down. When Uber sold its self-driving division to none other than Aurora in 2018, Don left and started Kodiak.

Aurora: Vertically integrated with proprietary LiDAR

Aurora’s driverless trucks started their first commercial operation in mid 2025. Today they have “tens” of Class 8 trucks driving on public roads in Texas. Class 8 trucks are the heaviest commercial vehicles, with a gross vehicle weight over 33,000 pounds. For reference, a Waymo is <6,000 pounds.

The company raised $1.2B over 5 years from Uber, automotive OEMs, and tier 1 VCs before going public via SPAC in November 2021. Despite the unfortunate financing timing (stock is down ~75% from their public debut), the team kept building and is now generating real commercial traction.

Their approach to LiDAR is their key differentiator. After acquiring Blackmore Sensors in 2019 and OURS in 2021, Aurora has been building proprietary LiDAR sensors that use FMCW (Frequency Modulated Continuous Wave) rather than the conventional pulsed/time-of-flight LiDAR that Ouster and most of the industry uses. FMCW sends out a continuous beam of light that changes frequency at regular intervals, rather than discrete pulses. This gives it greater range (400+ meters, with next-gen targeting 1,000 meters), instant velocity measurement via the Doppler effect, and immunity to interference from other LiDAR sensors or sunlight.

Why does this matter for trucks specifically? A 6,000 pound Waymo driving 5-30 mph can slow down and stop much easier than a 35,000 pound truck driving 60+ mph. Long-range sensors need to be much more advanced.

Aurora currently owns and operates its fleet. They are planning to transition into a Driver-as-a-Service (DaaS) model where they supply the technology and charge a per-mile fee.

Kodiak: Modular hardware

While Aurora vertically integrated on sensing, Kodiak took a modular approach focused on ease of adoption. Their signature hardware innovation is the SensorPod: a modular unit that replaces the truck’s stock side-view mirrors and houses all the sensors the Kodiak Driver needs to operate. Unlike Aurora’s proprietary LiDAR, Kodiak buys sensors off the shelf (Luminar LiDAR, Hesai 360-degree scanning LiDAR, ZF radars, cameras) and packages them into a field-swappable unit.

Long haul trucks with emerging tech can break down in the middle of nowhere. Sensor systems that require specialized technicians and hours of maintenance are likely not an option, so Kodiak designed these pods to be swapped out by any truck mechanic in less than 10 minutes.

Kodiak raised less than $300M, roughly ¼ what Aurora raised, before going public via SPAC in September ‘25. Their 20 driverless trucks are operating on private oilfield roads in west Texas, hauling frac sand for Atlas Energy Solutions as part of a 100-truck order. They are also running supervised autonomous trucks on public highways (Dallas to Houston, Dallas to Atlanta, Dallas to Oklahoma City) and targeting fully driverless highway operations in H2 2026. Kodiak is already running the asset-light DaaS business model that Aurora is moving towards, with Atlas owning/operating the trucks and paying for the Kodiak driver technology.

Two paths, same destination

Aurora and Kodiak are converging on driverless long-haul freight on public highways from different starting points. Aurora is betting on proprietary sensing and public-road deployment first, accepting higher R&D costs to build a defensible technology moat. Kodiak is betting on modularity and commercial pragmatism, proving unit economics on private roads before stepping onto the highway.

Conclusion

A year ago, autonomous driving was still a technology story. Today it’s a business story. Waymo is doing 500,000 paid rides a week and targeting a million. Aurora is hauling freight on Texas highways with no one in the cab while Kodiak is fulfilling a 100-truck order in the Permian Basin.

The enabling tech layer also reflects this shift. Ouster shipped a record 8,100+ sensors in Q4 with 60% gross margins while Aeva, an independent FMCW sensor company, doubled revenue year over year.

The sector still trades on narrative more than fundamentals. Kodiak trades at >100x forward revenue while Aurora trades at >500x.

That being said, the gap between narrative and reality is narrowing as companies begin deploying fleets and generating revenue. The question is no longer whether autonomy works. It’s who scales fastest.

Quarterly autonomy “Vibe check”

Charts and Graphs

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Footnotes & Disclosures:

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

Service and frontline roles remain stable, with quit rates rising and layoffs normalizing. Office roles, however, are deteriorating, with layoffs increasing across sectors.

This is a K-shaped labor market, where some segments show resilience while others weaken. The drivers of this divergence are showing up most clearly in office-based work.

This is most visible in the labor mobility spread (quits minus layoffs), which measures whether job movement is driven by worker confidence or employer pressure. In frontline roles, the spread is normalizing above the long-term median, indicating continued worker leverage. In office roles, it has compressed sharply, reflecting a shift toward employer-driven dynamics.

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Zooming in on office role layoffs, all major segments are seeing an uptick, with tech layoffs increasing the fastest.

Most layoff cycles are driven by macro-level events, but 2014 was different and serves as a parallel for the tech layoffs happening today.

In 2014, Microsoft cut 18,000 jobs, their largest RIF ever, under the banner of becoming ‘Cloud and Mobile’ native. IBM, Intel, Cisco, and HP also did layoffs and restructurings during this period as they navigated the cloud and mobile transition.

While the industry shift was real, it also provided a convenient justification for Microsoft to dismantle their failed Nokia deal. We may be seeing history repeat itself, with AI providing the same kind of narrative cover that ‘Cloud and Mobile’ did a decade ago.

Regardless of whether the recent layoffs are driven by AI substitution or corporate housekeeping, they’re concentrated in office roles.

This is the structural driver behind the K shape. Office roles are exposed to both genuine AI substitution and the organizational cover that AI provides for overdue restructuring. Frontline roles, by contrast, require a physical human in a physical place. I’ve been blown away by demos from both AI software agents and autonomous robots in the past year, but it’s clear that robots aren’t ready for at-scale deployment, whereas AI agents are.

How labor marketplaces are reacting

The K shape isn’t just showing up in labor data. It’s directly shaping the strategy of labor marketplaces.

Full-time hiring marketplaces are mitigating the overall decrease in job volume with decreased friction through AI and good old fashioned price hikes. On Recruit Holdings’ earnings call, management highlighted how Indeed raised average revenue per job (ARPJ) by 18% while total US job volume fell 7%. Management also disclosed how ⅔ of Indeed’s US paid job advertising revenue comes from service, frontline, and field service roles, where the labor market is stable and employer demand is stickier. Their business is on the more durable side of the K, allowing them to exert pricing power.

Freelance and gig market data is harder to quantify, but the K shape shows up in platform strategy. Upwork and Fiverr are prioritizing larger, more complex projects that are less likely to be disrupted by AI. They are courting AI-native workers and letting go of workers that mainly do lower-skill, small-scope projects. Upwork also announced a partnership with OpenAI to offer certifications and upskilling for their workers.

Gig work is more exposed to autonomous driving than software-based AI, and it’s dominating every earnings call. Uber is still growing, with MAUs hitting 200M and bookings re-accelerating in areas with AV partnerships. Uber and Doordash both commented on how a hybrid network of humans and multiple AV providers can best serve consumer demand, while Lyft mentioned how AV’s could have a 20% lower cost-per-mile, driven by both the lack of human drivers and a decrease in insurance premiums from AV’s high safety records.

Conclusion

The K shape in the labor market isn’t just a macro observation. It’s showing up in how every public labor marketplace is repositioning its business. Full-time hiring platforms are leaning into the side of the K where demand is structurally durable and raising prices accordingly. Freelance platforms are actively sorting their supply base, courting the workers who use AI as a multiplier and letting go of the ones they think AI can replace. Gig platforms, whose labor force is insulated from software-based AI substitution, are preparing for the longer-horizon disruption of autonomous vehicles by positioning themselves as the demand layer that sits above whatever supply network emerges.

Quarterly labor marketplace “Vibe check”

Other charts and graphs

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

Footnotes & Disclosures:

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

Software was already going through a selloff period on fears of AI disruption, and Citrini’s article perfectly described software’s bear case, triggering a sharp drop in stock prices. What happened next was a flood of responses from financial institutions, investors, tech executives, and really anyone with an X account. While the Citrini-triggered selloff has passed and the Bessemer Cloud Index has rebounded, the debate continues: Is generative AI good or bad for the software industry?

There are thoughtful people on both sides, and predicting the future of software is genuinely hard right now. Still, the fundamental premise of software hasn’t changed: One large team building a product for thousands of customers creates scale advantages in engineering, support, and reliability that no individual company can replicate internally. What’s changing is how software is being developed and how it is being paid for. To better understand the value proposition of enterprise software, you have to understand how enterprises actually buy software.

What enterprises want

When I talk to enterprise leaders, three things come up over and over again:

• They want safe software that works: No errors or hallucinations, hard to breach, full audit logs, regulatory compliance, and the ability to clear procurement.

• They want as few systems as possible: The less systems to manage, the better. Decent features from a core platform have an edge over best-of-breed solutions. There are hundreds of great examples, but Microsoft Teams versus Slack is one of the more well-known.

• Switching software is an organizational transformation, not a technology swap: Replacing a CRM or HCM at a large company is a multi-year effort that touches workflows, reporting, compliance, training, and change management. Workday built a large part of its go-to-market on consulting partners running full workforce transformation projects alongside the software, and channel partners accounted for 25% of their net-new ACV last year.

These aren’t preferences that change because AI gets better. They’re structural features of how large organizations operate. And they create a gravitational pull toward the core platform.

The gravity of core platforms

That gravitational pull is what makes core platforms so durable. Over time, they become the center of their customer’s software galaxy, where point solutions revolve around and integrate with them. Every sales tool needs to integrate with Salesforce. Every HR tool needs to connect to Workday. And if Salesforce or Workday builds something similar to what a point solution offers, they’re the likely winner because they already have the trust, the data, and the integration surface area. A vendor that can’t integrate with the core platform has little chance of commanding a meaningful ACV, and that’s if they can even sell into the enterprise.

The scale economy logic of software (one team, thousands of customers) doesn’t just apply to the vendors themselves. It also explains why enterprises consolidate around a small number of platforms. Managing fewer vendors means less integration overhead, less security exposure, and less organizational change. The same instinct that makes SaaS economics work for the vendor also makes platform consolidation attractive for the buyer.

How generative AI accelerates consolidation

Generative AI accelerates this dynamic. Core platforms can now build and ship adjacent capabilities faster than ever before, consolidating solutions that previously required standalone vendors. The engineering leverage AI provides disproportionately benefits platforms that have data, distribution, and customer trust.

The counterargument is that AI-native platforms will offer a large enough improvement to justify the pain of switching. By automating time-intensive data entry workflows and repetitive tasks, these new systems of action are positioned to create and capture much more value than incumbents. If that promise is realized at scale, the switching calculus could shift in ways it hasn’t before.

Still, it’s worth remembering that organizational change takes time. When an enterprise adopts a new system of record, they’re typically making a 10+ year commitment. That decision involves years of evaluation, implementation, and organizational change before the new system is even fully operational. The technology being better isn’t enough on its own. The improvement has to be dramatic enough to justify a multi-year transition, and the vendor has to be trustworthy enough to bet the next decade on. That’s a bar that most companies, no matter how good their product is, will struggle to clear. The consolidation will happen, but it’ll happen on long timelines. Anthropic and OpenAI use Workday for a reason.

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Narrowing to HR software

With that framework in mind, let me narrow the lens to the category I spend the most time in: HR SaaS. The same dynamics apply here, but with a few sector-specific wrinkles worth calling out.

• The seat-based model is showing cracks. HR SaaS has always been tied to employee headcount and payroll volume, and for years that felt like a tailwind. But headcount growth has stalled across most segments, and enterprises are getting more cost-conscious about per-seat pricing. When customers are investing in AI to do more with fewer people, a pricing model that scales with headcount starts working against you. The revenue base isn’t collapsing, but the model that got these companies here may not be the model that takes them forward.

• Payroll will get vibe-implemented, but not vibe-coded: Just as Stripe simplified payments, I expect payroll implementation to get dramatically easier over time. However, the core payroll engine that handles complex infrastructure (constantly changing tax codes, live connections to authorities, garnishments, and benefits) won’t be commoditized by AI coding tools, even if the abstraction layer changes.

• AI in hiring is running into regulatory and legal walls: Workday and Eightfold both face lawsuits that challenge their status as neutral tool providers. Workday is accused of discrimination via its AI screening, with the court finding it acted as an employer’s agent, triggering direct liability. Eightfold faces a proposed class action for building candidate reports with scraped data, allegedly violating the Fair Credit Reporting Act (FCRA). Regardless of where the cases land, they are starting a necessary conversation about disclosures to candidates and how much autonomy in hiring decisions AI should have. The legal framework hasn’t caught up to the technology, and widespread adoption of these tools will likely wait for answers.

The bottom line

Enterprise software isn’t dying, it’s transforming. The scale advantages of building one product for thousands of customers are arguably more real today than ever. Enterprises are and will remain cautious buyers who want safe, compliant, and integrated systems.

Still, durable demand doesn’t mean that incumbents will gracefully navigate this technology shift. Companies that are built on seat-based pricing are watching their customers invest in doing more with fewer people, and that creates real counter-positioning risk. Seat-based revenue is still enormously profitable, which makes it hard for incumbents to voluntarily cannibalize it by moving toward consumption or outcome-based models. Workday’s “Consumption Flex” credits and Docebo’s usage-based pricing experiments are early signs the industry sees the shift coming, but these are incremental moves from companies protecting large installed bases. A challenger with no legacy revenue to protect can build around the new pricing architecture from day one. The longer incumbents wait to make that transition, the wider the window gets for someone else to own it.

Quarterly HR SaaS “vibe check”

The table below translates the consolidation thesis into what’s actually showing up in public company earnings. A volatile macro environment is elongating sales cycles and tightening budgets. Customers are cost-conscious and only appear willing to pay up for AI agents that provide real automation. AI adoption is creating new pricing models but CFOs are cautious to commit to less predictable expenses.

Charts and graphs

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

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Footnotes & Disclosures:

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

AI hiring marketplaces emerged as the hottest category in HR Tech

Marketplaces historically take ~12% of venture dollars each year. In the last year, that share jumped past 25%. At the growth stage, venture dollars invested in marketplaces exploded 10x from $62M in 2024 to $615M in 2025, driven by large raises from Mercor, Micro1, and Turing*.

These marketplaces connect AI labs and enterprises with vetted technical talent and domain experts (engineers, PhDs, researchers) for model training, RLHF, data labeling, and AI development work. No company exemplifies this more than Mercor, which raised $450M across two growth rounds this year and has publicly disclosed an annualized revenue run-rate of ~$840M, up 24x from $35M just one year ago.

Whether this is durable infrastructure or a transient phase of the AI cycle remains to be seen. The entire category’s growth is tied to two assumptions: that frontier models will continue to need high-skill human feedback to improve, and that AI labs will continue to outsource this work rather than build the capability in-house. In 2026, I’ll be watching for signs of labs verticalizing their data operations and for how marketplace revenue grows in relation to compute and CapEx spending.

Investors are ceding post-hire to Rippling and Deel while betting that newer agentic startups will claim pre-hire

Half of all growth capital invested in post-hire apps in 2025 was taken by Rippling and Deel, while early-stage capital invested in the category continues to decrease. Investors are choosing to stay out of the category or fund vertical specialists like Miter rather than horizontal challengers.

While HR SaaS investors are hesitant to write early checks into core HR startups, they’re showing an appetite for agentic recruiting. As I covered a few months ago, several agentic recruiting companies have raised $10M+ rounds from tier 1 VCs like Sequoia, Coatue, and Menlo Ventures, suggesting that companies have product market fit and are beginning to scale.

The contrast with post-hire is striking. Investors have largely ceded that market to Rippling and Deel, choosing to stay on the sidelines rather than fund challengers. But in pre-hire, they’re betting that the incumbents from the last cycle won’t consolidate the market in the same way. Beamery and Fountain each raised ~$200M while Eightfold raised almost $400M, yet none of them have announced a raise, markup, or meaningful scale metric in years.

The implication is clear: investors see pre-hire as a market still waiting for its winner, and they’re betting on the next generation of agentic startups, not last cycle’s unicorns, to claim it.

On to the regular funding highlights!

Seed Stage Financings

• Seed stage capital is concentrating into fewer companies, with overall capital declining for 4 straight years while median deal sizes have increased

• Investors are most likely to fund companies building in core HR, payroll or benefits. For every dollar invested in pre-hire applications, $2.60 was invested into post-hire applications.

Series A Financings

• Series A capital increased year-over-year after 3 straight years of decreases, with median valuations at an 8-year high

• Marketplaces, which have historically taken ~10% of Series A venture dollars, took more than 25% in the last 2 years

• Series A venture dollars going into post-hire apps hit a 7-year low, with the largest deals going toward vertical platforms like Miter

• Series A venture dollars going into pre-hire apps rebounded after practically taking 2024 off. Agentic recruiting companies such as Juicebox, Alex AI, and ConverzAI all raised large rounds in 2025

Late Stage Financings (Series B and beyond)

• Growth capital going into marketplaces skyrocketed, growing 10x year-over-year. 90% of all growth capital invested in marketplaces went towards AI hiring marketplaces for AI labs

• Growth capital going into post-hire apps almost doubled year-over-year but half of all growth capital invested in post-hire apps in 2025 went into Rippling and Deel

• While median valuations are at an 8-year high, just 30% of companies are disclosing their valuation. Median round sizes are at 2021 levels

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If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

Footnotes & Disclosures:

*Indeed is an investor through their HR Tech Investments affiliate

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

• Weekly rides grew 2.6x from 175,000 to 400,000

• Active markets rose from 2 (Phoenix and San Francisco) to 6, with testing underway in 11 additional US cities and London

• Waymo launched freeway service in November and, in December, opened up rides to SFO for Waymo employees and designated airport staff

Additionally, Waymo reportedly discussed raising billions of dollars at a valuation over $100B, according to The Information. Freda Duan, an investor at Altimeter, estimated Waymo’s current GMV run-rate to be ~$375M and their forward-looking annual GMV to be $1-2B. At that level, a $100B valuation implies a 50-100x forward GMV multiple, which is aggressive by any standard. Still, $100B is less than 3% of Alphabet’s $3.8T market cap, and Waymo’s strategic importance far exceeds that.

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Looking at the entire AV ecosystem, here are a few predictions I have for 2026:

1. Airport rides launch - but they’re imperfect and expensive

AVs will begin airport service, but the rollout will be messy. Expect blocked lanes, awkward pickups, toll-road confusion, and poor signage handling. Additionally, Waymos are often 30-50% more expensive than traditional rideshare, meaning a Waymo to/from SFO or LAX could be hundreds of dollars.

2. AVs stay below the 40th parallel

Snow introduces a lot of new edge cases (hidden lane markings, degraded sensors, unpredictable traction) that will add significant complexity. Waymo is testing in three cities that regularly see snow (Denver, Detroit, and Washington, DC) but any launches there will be seasonal. Avoiding snow won’t constrain short-term growth, as there is more than enough demand in warm, sun-belt metros.

3. Commercial AVs go from one to many

Waymo’s near-monopoly won’t last. AVs are harder to scale than software (capital intensity, hardware, and regulation matter) but competitors are coming. Tesla, Zoox, Nuro, Wayve and others are planning public launches in 2026. By year-end, there could be 10+ AV providers offering rides across North America and Europe.

4. More Mag7 companies enter (or re-enter?) the space

Microsoft wrote down its 2021 Cruise investment in 2024 after GM shut down the program. Apple abandoned its decade-long car project in early 2024. Meta hasn't decided if autonomous vehicles qualify as superintelligence, probably because they're still figuring out what “superintelligence” means. Still, Zuck isn’t afraid to throw money at big ideas, proven or unproven. I expect two of these three companies to make a splashy AV investment in 2026.

Closing thoughts on 2026

2024 was the year AVs proved that they worked

2025 was the year that people began trusting and preferring them

2026 will be the year that competition hits escape velocity

Multiple operators will enter the market and AVs will compete on cost, brand, coverage, and reliability. Consumers will treat them more like a default option and tolerance for price gouging, limited availability, or awkward experiences will disappear.

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Robots and autonomy vibe check

The below table is a “vibe check” on each segment of robots and autonomy, distilling management commentary on key milestones and hurdles.

Charts and Graphs

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

Footnotes & Disclosures:

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

In labor marketplaces, success is defined by liquidity (can a match happen quickly and reliably) and by friction (the work, time, and risk required to form a match). The best companies maximize liquidity while intentionally reducing or selectively adding friction to improve match quality and retention.

How platforms respond to these forces explains the widening gap between full-time hiring marketplaces and gig platforms.

Full time hiring for office roles: Reducing friction to mitigate low liquidity

When workers feel confident, they switch jobs (voluntary exits / quits); when employers are stressed, layoffs rise and hiring freezes. The Labor Mobility Spread (Quit rate - layoff rate) captures the difference between the two, showing whether job movement is being driven more by worker choice (high spread) or employer pressure (low spread).

Aside from a short rise in late 2023 and 2024, this spread has steadily decreased for 3 years. Office workers are increasingly staying put, while layoffs have risen. The result is a stickier labor market with fewer people actively testing new opportunities and fewer employers hiring, reducing marketplace liquidity.

One way to mitigate a decrease in liquidity is to reduce friction, which is where AI comes in: Almost every notable agentic company or product launched by a legacy HR player is aimed at reducing friction in the recruiting process. Source candidates faster, automate interviews, reduce time to hire.

Mature marketplaces like LinkedIn and ZipRecruiter aren’t making big pushes into new markets, which leaves marketplace efficiency as their core growth lever. By upselling AI tools to employers, they can reduce marketplace friction and effectively increase their “take rate” on hiring spend.

Still, essential friction points can be beneficial, and removing them threatens the trust and efficiency of the marketplace. Using interviews as an example, most jobseekers want to meet their potential coworkers a few times before making a 3-5 year work commitment. When I ask friends and colleagues with office jobs if they would take an AI interview, I rarely hear a yes.

Gig marketplaces are still looking to reduce friction

Full-time marketplaces aren’t the only ones using AI to reduce marketplace friction. Instacart launched their ChatGPT app earlier this month, which can generate and order a shopping cart all within ChatGPT. Uber and DoorDash were also named as initial partners in the original ChatGPT app announcement but have yet to release an app.

In contrast to full-time hiring, gig marketplaces are seeing healthy supply and demand. While consumers are shifting from dining out to home-cooked meals, they are still using Uber and DoorDash to get those groceries delivered. Both companies called out grocery and retail as key growth drivers, with DoorDash reporting an all-time high in orders per user. Their continued expansion into retail and grocery is unlocking more consumer wallet share while giving workers more tasks to do, increasing marketplace size and liquidity.

Navigating Autonomy

Autonomous vehicles are seeing exponential growth (which I’ll be covering in depth in a future post) and gig marketplaces are trying to be the demand layer on top. Uber is positioning itself as a demand aggregator after divesting its autonomy group in 2021, having partnered with a broad ecosystem of AV technology leaders like Waymo, Nuro, and Nvidia. Lyft is adopting a similar approach, announcing partnerships with May Mobility and Tensor.

DoorDash is notably building their own delivery robots alongside partner efforts. DoorDash partners with Serve Robotics in a few metro areas and is building a similar-looking robot called Dot for small deliveries over short distances. Dot uses cameras, lidar, and radar sensors to navigate and has a max speed of 20 MPH.

DoorDash’s CEO Tony Xu shared his autonomy vision in their most recent earnings call:

The most important thing is understanding that the vision for autonomy is really going to be a multimodal world where you’re going to see different fulfillment methods. Some of those fulfillment methods will happen with Dashers. Some of them will happen with vehicles on land. Some of them will happen with vehicles in the air. Some of them will happen with vehicles built by DoorDash. Some of them will be filled by vehicles built in partnerships.

What’s important is DoorDash has the luxury to create our own autonomous delivery platform where, depending on whatever the use case is or whatever the customer need is, we can solve it with the highest quality, the lowest cost and the best service, and that’s really the goal. And so we’ll be fairly pragmatic on how we do this. So a lot of it, we’ll be testing within one market, getting that right, figuring out the go-to-market motion because there’s many parts you kind of have to get right, everything from the manufacturing when it comes to making these products in-house to partnerships, to integrating the hardware and the software, to making sure you get the repair and the teleoperations correct, to making sure that you work well with city governments.

Dot may not be the most inspiring approach to autonomous transportation, but it’s clear that Tony and the DoorDash team sees autonomy as a key building block of their future.

Labor marketplace vibe check

The below table is a “vibe check” of each segment of labor marketplaces, distilling management commentary on marketplace supply and demand, as well as how they are navigating AI and autonomy.

Charts and Graphs

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

Footnotes & Disclosures:

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

When I started this blog, my focus was HR Tech: the software systems that manage hiring and talent. But the future of work is being shaped by far more than HR platforms. Gig platforms have evolved into large-scale labor networks, coordinating who does the work, how tasks are dispatched, and how value flows through the system. Autonomous vehicles and robotics push this even further, introducing machine labor that could upend both traditional employment and today’s gig models. I’ve been tracking all of these areas for years, so I’m excited to finally bring that full scope into what I share here.

Moving forward, I’ll be publishing a separate report on each space: HR SaaS, labor marketplaces, and autonomy & robots. They may seem like disconnected spaces, but together they’re redefining what work looks like, how it’s organized, and how it gets done.

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On to my first report on HR SaaS!

AI pushes forward, HR SaaS lags behind

HR SaaS is in the early days of its AI platform shift and just 1 public company, Workday, has seen a meaningful lift in revenue growth from agentic products. While HCM and learning platforms are deploying agentic workflows and generative content, these features are not yet translating into significant growth. Instead, AI is being leveraged to increase product stickiness, drive competitive differentiation, and create future upsell opportunities, with direct monetization largely planned for premium tiers or deferred entirely.

Workday has taken a highly inorganic approach to adding AI capabilities, spending $3B+ across 18 months on acquisitions. This was the first quarter that those investments translated into meaningful ARR impact. Management attributed 1.5 points of ARR growth to AI products (not including Paradox), with Workday’s CEO calling out $2.50 in agent sales for every $1 of recruiting sales.

Paycom is taking a different route, spending $100M directly on GPUs to run open-source models in-house. As a result, CapEx was 20% of revenue this quarter, twice as much as their historical average. Management framed this as a one-time investment to control long-term AI unit economics. Investors, however, largely viewed it as poor capital allocation.

HR SaaS “Vibe check”

The below table is a “vibe check” of the HR SaaS sector, distilling management commentary on the macro environment and buyer behavior. A volatile macro environment is elongating sales cycles and tightening budgets, especially in the education and SMB-focused payroll segments. Still, mission-critical platforms are showing resilience.

Charts and Graphs

The below chart shows the EV / NTM revenue multiple divided by NTM consensus growth expectations. So a company trading at 10x NTM revenue that is projected to grow 25% would be trading at a 0.4x growth-adjusted revenue multiple. The goal of this graph is to show how relatively cheap / expensive a stock is relative to its growth expectations.

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

Footnotes & Disclosures:

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

Deel on Thursday (October 16th) announced that it has raised a $300 million Series E round at a $17.3B valuation co-led by A-list fintech VC firm Ribbit Capital and Andreessen Horowitz, with participation from existing investors like Coatue Management and General Catalyst.

Deel says it’s been profitable for three years and surpassed $1 billion in ARR, including having a month — September — that hit $100 million in revenue (implying a $1.2 billion revenue run-rate). Deel’s business model focuses on serving global companies, handling the complexities of currency and employment regulation for far-flung international teams. It says it’s now grown to 35,000+ customers with more than 1.5 million workers in over 150 countries.

Deel’s new valuation puts it ahead of its fierce competitor Rippling, making it the most valuable privately held HR Tech startup despite raising half as much capital ($1.0B raised to date for Deel versus $1.9B for Rippling). While the new valuation is a step-up from their 2022 raise, it was done at a dramatically lower ARR multiple of ~14x that falls in line with that of public high-growth SaaS. Zooming in on HR Tech comps, Workday and ADP both trade at ~6x NTM revenue with 16% and 6% projected growth, respectively.

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Based on estimated financials from Sacra, Deel is growing rapidly and profitably. Even if ARR growth were flat for the remainder of 2025, it would still achieve 50% growth for the full year. To match last year’s 70% growth, ARR would need to reach $1.36 billion. The company’s gross margins are estimated to be 85%, with EBITDA margins around 15%.

What separates Deel from traditional venture success stories is that it is already using M&A as a growth lever. Deel acquired 12 businesses over the last four years, including five acquisitions in the last 12 months. As part of this fundraise announcement, Co-Founder and CEO Alex Bouaziz went on the 20VC podcast to walk through Deel’s acquisition and integration strategy:

Our acquisition playbook is pretty unique - I think that’s what most of our investors point to. We do two types of acquisitions. First, we buy companies that are core to our existing market. In those cases, we basically buy the team and the revenue, bring them in-house, and ‘rip and replace’ their infrastructure with ours very quickly. We’re actually quite good at that. Second, we acquire smaller companies in adjacent domains we want to enter. In those cases, we don’t try to patch systems together. Instead, we keep the founders and rebuild the full infrastructure from the ground up.

Normally, salespeople take nine to twelve months to get comfortable selling a new product. What we do is bring in the acquired product, rebuild the entire frontend within Deel - connected to the existing backend - and that takes about two months. That means we can start selling it almost immediately. Our sales team starts learning and selling while we rebuild the backend natively inside Deel, which usually takes between three and twelve months depending on complexity. So, by the time the backend is ready, the sales org already knows how to sell it. Sometimes it’s rough early on - bugs, learning curves, sales frustration — but you iterate fast. Over time, you close the gap and end up with a far better product, ready for scale about nine months later. If we didn’t operate this way, it would take a year to integrate and then another year to ramp sales.

Deel runs two distinct playbooks depending on the type of acquisition. For core-market deals, the focus is speed and standardization: acquire the team and revenue, migrate infrastructure into Deel’s stack, and start selling within weeks. For adjacent-market entries, the emphasis shifts to keeping founders in place while rebuilding infrastructure from the ground up. Their unique, two-step integration model is built to accelerate cross-sell momentum while minimizing long-term technical debt. This approach blends the discipline of enterprise integration with the speed of startup iteration. The challenge, of course, is consistency: if execution lags, Deel risks the very technical debt it’s trying to avoid.

Well-executed or not, Deel has evolved from a single-product company to a multi-product platform. In a LinkedIn post announcing the raise, Alex gave some insight into Deel’s run-rate revenue by product. The original product, contractor payments, is now less than 10% of total revenue. Revenue is split almost evenly between HR SaaS and EOR/worker payments.

Deel’s growth exemplifies the broad consolidation trend happening in HR Tech. As I covered in a previous post, employers want full-suite deals that simplify operations. Deel’s management team saw this trend years ago and used M&A to accelerate their transition from point solution to platform, a practice not many startups do well. While this could become a case study on venture-backed rollups, it could just as easily become a cautionary tale of what happens when expansion outpaces proper integration.

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

Footnotes & Disclosures:

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook, Sacra, and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

As AI systems have become smarter, faster, and cheaper, adoption has spread across new segments and verticals. Coding was one of the earliest and clearest applications, with legal, customer service, and medical scribing quickly following. As each use case gained traction, venture dollars flooded in to scale products and bring them to market.

Agentic HR Tech is entering its breakout moment. Several companies in the space have raised $10M+ rounds in the past three months, suggesting that companies have found product market fit and are beginning to scale. Enterprise software startups are developing AI interviewers and coaches, autonomous candidate sourcing and outreach tools, and other self-directed HR capabilities. Marketplaces are developing AI-native workflows to drive faster and more efficient job matching, often serving the same AI labs whose models power their agentic features. Job-seeker applications are notably absent, despite many raising seed rounds.

What’s happening in HR Tech mirrors a broader shift in how and where value is being created in enterprise software. As Derek Xiao at Menlo Ventures explained, enterprise software has evolved through three eras: Storing data (systems of record), then building interfaces (systems of engagement), and now automating complex tasks (systems of work). Value is now created through autonomous workflows and interactions, which improve with additional data and context. In a previous article of mine, I described these next-gen systems as systems of action. I think Derek’s framework captures these ideas in a clearer way.

As I think about how lasting value will be created and captured, I’m drawn to unique data and the powerful flywheels that they can create. Data is unique if it has either never been captured before or never been made usable after being captured. In HR Tech, this can be candidate data, interview notes, or performance reviews.

With interview copilots (*Ashby’s AI notetaker is one example), there is an immediate productivity lift for recruiters and hiring managers: detailed interview notes that are automatically posted to the ATS. But the real potential comes later. When every interview across a company’s history is recorded, structured, and searchable, hiring shifts from intuition to intelligence. Companies can analyze how top performers were evaluated, identify consistent signals of success, and even train future interviewers (or AI interview agents!) on what great judgment looks like.

While we’re still in the early days of agentic HR Tech, funding is accelerating and new use cases are taking shape. Over time, value will accrue to the systems that not only automate work, but learn from it.

On to the regular funding highlights!

Seed Stage Financings

• Seed stage capital is concentrating into fewer companies, with overall capital below 2023 levels while median deal sizes are the highest in the last few years

• Venture dollars are shifting more toward early-stage. Typically, ⅔ of all venture dollars go into growth rounds. This quarter, it was about 50/50

• Every company that raised a $6M+ seed round is building AI-native or core-HR products

Series A Financings

• Total capital invested and median deal sizes have been elevated for two straight quarters, while median valuations have been elevated for three

• All but one Series A this quarter (Teamsense being the outlier) went toward companies building AI-native or core-HR products

• Micro1’s $35M Series A was done at a valuation 6x higher than its raise just 1 year ago

Late Stage Financings (Series B and beyond)

• Growth capital was down, even after stripping out Rippling’s Series G last quarter

• Companies are waiting longer than ever to raise growth rounds, with the median time between rounds being more than three years

• Two leave management companies raised growth rounds this quarter: Tilt and Sparrow

• The largest rounds this quarter were in *Ashby, an AI-native ATS, and Valence, an AI coaching platform

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

Footnotes & Disclosures:

*Indeed is an investor through their HR Tech Investments affiliate

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

I want to highlight 2 acquisitions that were announced in late August:

• Workday acquired AI recruiting platform Paradox for $1B (press release)

• Thoma Bravo acquired HCM platform Dayforce for $12B (press release)

Josh Bersin covered the Paradox acquisition well and perfectly described what makes them so special: “At McDonald's, you can stand in line to get a burger, then apply for and accept a job before your burger is even ready”. Leveraging conversational AI, Paradox simplifies and automates every step of the application process. As an investor in Paradox*, I won’t comment on company-specific details. Still, congrats to Aaron, Adam, and the rest of the team!

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Paradox’s success illustrates a broader shift underway in enterprise software: A rise in systems of action (SOA) that sit on top of legacy systems of record (SOR). Systems of action don’t just store data; they build powerful workflows that automate routine tasks and enable faster, more informed decisions.

While the first wave of cloud created value by digitizing records, the next wave will create value by automating actions. Historically, point solutions sat on top of SORs as narrow add-ons; with GenAI, they have the agency to gather, interpret, and act on data directly. Candidate outreach, application review, AI interviews, and recruiter copilots have all become flagship features of venture-backed startups. As these automations become more sophisticated, I expect the value in enterprise software to shift from systems of record to systems of action, with some healthy consolidation along the way.

The recent acquisitions by Workday and Thoma Bravo are not isolated events, as incumbent SORs will have to buy or build core automations to maintain relevance. Workday’s purchase of Paradox signals a decision to buy a workflow layer they likely couldn’t build fast enough. Thoma Bravo’s buyout of Dayforce is likely the beginning of a next-gen HR Tech roll up. I wouldn’t be surprised to see them make 2-3 bolt-on acquisitions over the next few years.

Shifting from buyers to builders, companies like Ashby* and Rippling saw this SOA trend years ago and chose to build fully integrated systems of record and action. Deel, Remote, Lattice, and Gem all started as systems of action before building their own HRIS/ATS platforms, competing directly with the systems of record they sit on top of.

Consolidation is likely to continue, but the real question is whether integrated challengers can replace incumbents before they finish buying their way in. The outcome won’t just determine the winners in HR Tech; if incumbents stumble, challengers will set the template for how enterprise software evolves in the AI era.

On to the regular earnings highlights!

Human Capital Management (HCM)

• Continued demand for AI and platform consolidation: Another strong quarter for HCM platforms, with all companies attributing growth to tech stack consolidation and AI investments. Dayforce’s CEO David Ossip cited that Dayforce replaces 12 systems on average. While each company isn’t going into detail as to what “full suite” includes or doesn’t include, they all highlight these as the norm. Every company also reaffirmed their full year guidance, with Workday slightly raising revenue and operating income guidance.

• Market uncertainty starting to show: SAP and Workday both noted headwinds in government and education verticals, with SAP calling out longer sales cycles and extended approval workflows. SAP also saw growth in their cloud backlog decelerate.

Payroll

• Mixed macro signals: While Paylocity and Paycom described demand as healthy and stable, ADP noted a softer finish in HR outsourcing and called out delayed decision making due to macro concerns. The segment targets upper mid-market and enterprise clients, signaling a possible slowdown in enterprise hiring. Both Paylocity and ADP are predicting net hires to be flat across their overall customer base in 2026. Despite this, ADP and paylocity are continuing to invest in sales headcount.

• Inorganic growth: Over half of Paylocity’s growth was driven from product upsells, with ACVs growing 8% YoY to $35K. Paylocity recently acquired Airbase, a mid-market spend management platform that had raised $90M in venture funding. ADP also attributed some of their growth to Workforce, a workforce management platform they purchased from private equity last year.

Online Employment marketplace (OEM)

• Slow growth driven by macro uncertainty: All companies called out low hiring numbers from SMBs. Recruit Holdings (owners of Indeed & Glassdoor) called out a particular weakness in March and April among SMBs but also highlighted resilience in healthcare & construction. Upwork is supposedly seeing strong growth in its Business Plus offering despite overall revenue growth being flat YoY. Upwork raised their 2026 guidance but is still projecting <10% annual growth.

• AI driving more margin than growth: While multiple companies are citing large growth numbers in AI products and usage, few are seeing an acceleration in revenue growth. However, Fiverr and Upwork have recognized higher margins from successfully deployed AI investments across their own business. They both deployed customer service agents, with Upwork noting 80% adoption of its “Upwork assist” agent. Upwork also rolled out AI agents for forecasting financial performance and analyzing employee feedback.

Talent Management

• Growth acceleration driven by AI upskilling: After ~5 straight quarters of decelerating growth, Docebo, Coursera and Udemy all saw a reacceleration this quarter. Udemy, after moving upmarket, has seen an increase in win rates and an increasing pipeline, with growth being slightly dragged down by a roll off of smaller customers. They also saw their highest net retention in Japan in 3 years. All companies cited AI upskilling as growth drivers.

• Docebo moves downmarket: Docebo, the least scaled but fastest growing talent management platform, hasn’t seen the same success as Udemy in the enterprise space. They recently pivoted their sales process to a mid-market land & expand motion (as opposed to pushing for full-suite deals upfront).

Other Charts and Graphs

The below chart shows the EV / NTM revenue multiple divided by NTM consensus growth expectations. So a company trading at 10x NTM revenue that is projected to grow 25% would be trading at a 0.4x growth-adjusted revenue multiple. The goal of this graph is to show how relatively cheap / expensive a stock is relative to its growth expectations.

If you’re an operator or investor in the space, I’d love to hear from you. Please reach out at kgetsiv@indeed.com

Thanks for reading!

Footnotes & Disclosures:

*Indeed is an investor through their HR Tech Investments affiliate

**Paychex’s acquisition of Paycor is driving their 20% forward growth projections

***Announced intention to be acquired by Thoma Bravo

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

Platforms that embed AI at the core of their value proposition are thriving, seeing no slowdown in demand or change in buying behavior. Others, particularly those offering modular or discretionary tools, are seeing longer sales cycles and heightened scrutiny. The macro picture hasn’t improved much, but buyers are moving towards platform consolidation and adding AI wherever possible. Efficiency is no longer just an aspiration; it’s the strategy.

In this first-ever quarterly review of public HR Tech trends, I’ll be breaking down how this new buyer psychology is impacting each segment and which strategies are winning.

Human Capital Management (HCM) Segment

Strong demand for AI and platform consolidation: Buying behavior has not been materially impacted by the macro environment, with vendors like Dayforce reporting their best first quarter for sales. Customers are looking for “full suite” deals that simplify operations. AI has become a tangible revenue driver, with Dayforce and SAP seeing ~50% of new deals include AI offerings and Workday's AI product revenue doubling YoY. AI products are analyzing legal contracts, reviewing job applicants to boost recruiter capacity, accelerating software development, simulating financial scenarios, and automating tasks across HR and finance.

Resilient value proposition and bullish outlook: All 3 management teams expressed confidence that their platforms are well-positioned for the current climate. They stated that customers are investing in their solutions to drive efficiency, consolidate costs, and gain agility, making HCMs a strategic, not discretionary, purchase. Strong Q1 performance, healthy pipelines, and growing backlogs have led management to reiterate their forward-looking guidance.

Payroll Segment

Cautious but stable macro environment: Unlike HCMs, payroll companies are seeing longer buying cycles and increased uncertainty around issues like inflation and tariffs. Paychex noted an "increase in bankruptcies and financial distress in the micro end of the market," while Paylocity and ADP reported stability.

Turning to AI and product expansion for growth: All four companies highlighted significant investments in AI, not as standalone products but as a deeply integrated layer to enhance efficiency, both for their clients and their own internal operations. AI is being used to automate tasks, generate smarter insights, answer policy questions, and improve sales productivity, with some reporting measurable ROI. Payroll companies are also attempting to expand into adjacent areas. Paylocity is moving into the "Office of the CFO" with its recent acquisition of spend management platform Airbase, while ADP is embedding accounts payable/receivable features into its SMB platform through a partnership with Fiserv. ADP and Paycom are expanding their global HR and payroll capabilities.

*Paychex’s acquisition of Paycor is driving their 20% forward growth projections

Online Employment Marketplaces (OEM) Segment

Cautious macro environment driving a move upmarket: All OEMs cited stable or decelerating demand, with macro uncertainty being the main driver. ZipRecruiter management stated that employers don't feel the pressure to move as fast as they once did and may take longer to fill a job. Fiverr and Upwork are focused on attracting larger clients, with Upwork noting that its largest clients grew their per-client GSV 11% YoY.

Transformative push into AI seeing results: All four companies are seeing the benefits of AI in their products or workforce, building features like screening & interviewing and contract proposal & negotiation. Upwork is seeing increased usage of their AI product Uma, which has helped drive up hiring and GSV metrics. Recruit Holdings, owners of Indeed and Glassdoor, reported that ~33% of the new product code is being written by Al. All companies plan to continue aggressive investment and innovation in AI as a core part of their strategy.

Talent Management Segment

Uncertainty driving consolidation and spending slowdown: All companies noted a cautious macro environment leading to elongated decision-making, increased deal scrutiny, and pressure on discretionary spending. Customers are consolidating learning vendors and favoring broad platforms. Vendors are increasingly focused on moving upmarket to larger enterprise customers and must demonstrate tangible business outcomes like increased employee retention or productivity to win deals.

Diverging vertical performance: Tech, retail, and manufacturing verticals are experiencing some spending slowdowns due to macro pressures. The public sector shows a mixed outlook, with Skillsoft seeing a decline in U.S. federal discretionary spending, while Docebo is optimistic about the U.S. federal market after receiving ATO status (cleared for use by the Federal Government).

Customers want to upskill their workforce: Customers are urgently seeking to upskill their entire workforces in AI, from technical teams to frontline workers. Vendors are rapidly embedding AI into their platforms to offer personalized learning paths, AI-powered coaching, and automated content creation. All companies cited AI driving learner engagement, program creation, or productivity gains.

Other Charts and Graphs

The below chart shows the EV / NTM revenue multiple divided by NTM consensus growth expectations. So a company trading at 10x NTM revenue that is projected to grow 25% would be trading at a 0.4x growth-adjusted revenue multiple. The goal of this graph is to show how relatively cheap / expensive each stock is relative to its growth expectations.

*Paychex’s acquisition of Paycor is driving their 20% forward growth projections

*Paychex’s acquisition of Paycor is driving their 20% forward growth projections

Thanks for reading!

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.

For every venture dollar put into HR Tech since 2023, Rippling took about 25 cents. Additionally, global HCM platform Deel logged a $300M secondary sale and hit a $1B annual revenue run rate. While these fast-growing companies have a lot of disagreements, they do agree on platform superiority. Early stage investment in the sector has been decreasing, suggesting that investors may be prioritizing proven platforms over new point solutions. The few companies that have raised large Series As have been in core HR segments such as payroll or benefits.

Despite an overall slowdown in deals, valuations and round sizes have been stable across all stages. Time between rounds and markups are also stable.

Seed Stage Financings

• Capital and deal volume have been steadily decreasing over time

• 2 companies raised at >$40M post-money valuations this quarter: Lace, an onboarding & productivity tool for CSMs, and Logistiview, a productivity platform for frontline workers

• Deal sizes have stayed at about $3M with dilution in the mid teens. Few companies disclose seed valuations, making the dilution numbers less reliable

Series A Financings

• 4 companies raised $20M+ series A’s in Q2, a quarterly record in this time frame

• Miter, a payroll & HCM platform for construction workers and contractors, raised a $23M series A extension just 5 months after their $6M series A in January 2025

• Markups at this stage are typically 1-2x. The median markup was pulled up in Q1 by AI recruiting platform ConverzAI (3x markup) and Miter’s first series A (4x markup)

Late Stage Financings (Series B and beyond)

• Rippling's Series G funding round accounted for half of all venture capital invested into HR Tech this quarter. The round included $250M of primary capital and $200M of secondary sales

• Employee recognition platform Awardco raised a $165M Series B and became HR Tech’s newest unicorn

• Growth funding in the sector, even after excluding Rippling, has been seasonal, peaking in Q1 or Q2 each year

• Last quarter, 2 LLM-training marketplaces Turing and Mercor raised at >$1B valuations and publicly reported huge growth numbers

Thanks for reading!

The information presented in this publication is for informational purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The views expressed are solely those of the author and do not represent the views of any company, employer, or affiliated party.

All data and analysis are derived from public sources, including Pitchbook and company websites. No confidential or proprietary information has been used or referenced. While the information is believed to be accurate and from reliable sources, no warranty is made as to its completeness or accuracy, and no liability is accepted for errors or omissions. Past performance is not indicative of future results.