As tempting as it was to sort through some of the increasingly surreal Big AI news ($100mm signing bonuses, billion-dollar-a-month burn rates, $1 trillion robotics/AI facilities, etc.), we felt compelled to finally scratch an early-stage venture itch that’s been bothering us for awhile with respect to expectations, conventions, and a related (and wholly unfair) stigma that needs to go.
One quick aside on the Big AI news, though – this line from an article on xAI’s new fundraising efforts made us chuckle:
“Backers include heavyweights like Andreessen Horowitz, Sequoia, and VY Capital—but even they have reportedly asked for more detailed financials.”
So… better $14-billion-late than never?
Okay, enough on that, we have more important fish to fry here. So, while we all try to recover from just how not-far-fetched Mountainhead was, let’s take a look at the state of Seed, Series A, and the vast gulf in the middle, notably, the increasingly common, generally prudent, and unfairly maligned “Seed Bridge” round.
We’ll get into the “why” of all this shortly, but first, the high-level.
The Delta Round
Post-Seed, Bridge rounds have long been considered by VCdom to be a rare and unseemly last resort, reserved for companies who mismanaged their budgets or had to undergo substantial pivots. While that may have been a mostly fair characterization in the past, today, we’re seeing the majority of perfectly healthy companies raising these Seed+ rounds simply because expectations for startups at Seed and Series A have widened so much that the gap has become extremely difficult to cross without a pit stop in the middle.
In other words, these Seed+/Pre-A rounds today are much more about topping off the gas tank to make sure companies don’t run out of fuel before they reach Series A than they are a recap of a pivoting company or a lifeline to a failing one.
Yet, despite the overwhelming body of evidence we’re seeing that confirms this to be the case (both in our own portfolios and in the broader market data), a heavy stigma remains attached to these Seed Bridge rounds within VC circles. We’ve been racking our brains for years trying to figure out why, and we think we have the answer.
This round needs its own name.
Does that sound kind of, well, dumb? You bet. But, notwithstanding that “dumb” isn’t necessarily an automatic disqualifier in this space, there are real reasons for VCs’ natural aversion to writing their first checks into something called a “bridge” round.
In large part, this is an inertia issue. Given enough time, stigmas take on a life of their own (going viral, as the kids say), and this creates a fear of being seen as the VC who fell into the bridge trap that everyone else was savvy enough to avoid.
On top of this, there’s also a strategy/branding issue. Look at how nearly all VCs describe their strategies. You will pretty much always see something like, “We invest in [list of generic adjectives that mean either “bold” or “innovative”] Pre-Seed and Seed companies”, “We’re a Seed & Series A fund”, “We look for [same generic adjectives] startups raising their Series A & B rounds”, etc..
This adds a related fear that if your first check into a couple companies are at “bridge” rounds and those are among the deals in your fund that eventually go south, they won’t just go into the bucket of expected zeroes, but will rather look like you went off script and blew it (and are therefore irresponsible/untrustworthy/etc). This is not without reason, either (i.e., LPs aren’t entirely innocent in all of this).
But give this baby its own name, and now it’s “We invest in [adjectives] Seed, [NewlyNamedRound], and Series A companies”, and we’re off and running. Sayonara Seed-Plus, Pre-A, Seed-Bridge, and all the rest.
So, what to call it? We propose:
The “Delta” Round
We like this one for a variety of reasons, including:
It stays within the 2-syllable limit of the current rounds
It’s easy to remember and a familiar word (i.e., no debate on pronunciation)
In the Greek alphabet, Delta follows Alpha, Beta & Gamma, the first two of which are already well-established stages of tech development, which generally coincide with (in our view, anyway) early and late Pre-Seed companies, respectively. Leaving an additional spot for the Seed round (i.e., skipping over “Gamma”) that brings us to Delta.
Lastly, Delta is both the mathematical term for the change in a variable and widely used in finance to mean the difference between two numerical terms, both of which nicely mirror where a healthy startup sits at this point in its lifecycle, relative to where it was at Seed.
Back to the “why”, with some supporting macro and C2V data.
Defending the Delta Round
Frequency
Per Carta’s most recent quarterly State of Private Markets, there are now nearly as many Seed Bridge rounds being raised as primary Seed rounds (46% to 54% in Q1). This is remarkable in itself, but we think it actually understates how frequent these rounds are, as Carta’s breakdown (as seen in the chart above) is among rounds that are being raised concurrently (i.e., the companies raising primaries are not the same ones raising bridges).
If you instead look at each quarter’s bridge rounds relative to primary rounds from prior quarters (using just about any Seed-to-Bridge time horizon), the number is closer to 55-60%. Add in the companies that failed without raising a bridge and those whose funding docs say “Series A” but are really a bridge, you’re likely pushing 65-70%, maybe higher.
Most notably, these percentages have increased dramatically over just the past four years (as indicated by the above chart).
Why This is Happening
As we alluded to above, it’s simply that the bar for traction/revenue run rates that most Series A VCs are looking for has moved incrementally higher over the years, while the general criteria for Seed is more or less the same.
That might lead one to think Seed standards should just move proportionally higher as well, but that would just create a similar gap between Seed and Pre-Seed. Plus, there’s already a round that’s proportionally (and achievably) closer to Series A — it’s called the Delta Round. See where we’re going with this?
The Unfair Stigma
While Carta’s broad market data can’t tell us anything about the quality of these companies raising Seed+ rounds, our portfolio can shed some light on this (albeit with a much smaller sample size). Of our companies at Series A or later (so, by definition, companies that are successfully executing), ~60% raised a post-Seed bridge round before their Series A.
Furthermore, we’re not seeing anything to suggest that companies that go directly from Seed to A will have better long-term outcomes than those that took a bridge pitstop along the way (and perhaps that the opposite is true?). Of our companies currently running at a $5 million or greater ARR run rate, those who raised a bridge round took an average of 40 months to go from Seed $5mm ARR, while those who skipped the bridge round took an average of 56 months.
Again, this is a relatively small sample size, and we’ll see how this evolves as our portfolios grow and current companies further mature, but it would certainly make sense if companies who raised more money between Seed and A were able to grow faster than those who ran leaner, trying to get to A without doing so. And if this is the case, that’s even more reason to drop the Seed+ stigma and just make it a regular funding stage.
Plus-Sized Seed Rounds
One last bit on this. It’s not news to the smarter VCs out there that the Seed to A leap has become considerably harder over the years, but we want to address the one “solution” to this challenge that you will most commonly hear – raise bigger Seed rounds. This one probably deserves its own newsletter, so we’ll do the short version here, but suffice it to say, we both fundamentally disagree with this premise and believe it to be a bit cynically self-serving.
In our experience, the average company that raises a $5mm Seed round will run out of money with exactly the same level of progress as the average company that raises a $2.5mm Seed. There are a bunch of more specific reasons why this is the case, but suffice it to say, founders who raise twice as much at Seed will do all the same experimenting and make all of the same mistakes; they’ll just spend twice as much on them (sometimes more). This is not a knock on these founders, at Seed, you just don’t know enough about how and where you should be spending money, and being looser with spending when you have more to spend is just basic human nature.
Furthermore, we don’t believe that the vast majority of VCs pushing outsized Seed rounds on companies are doing so because of the increased Seed-to-A challenges (though that does make for a convenient rationale), they’re doing so because they’ve raised funds that are too big for a Seed strategy and need to write bigger checks to make the math work.
Lastly, some data to support our postulate that Seed round sizes have rapidly diminishing returns over a certain size.
Looking at our portfolio companies, more than a year removed from our initial Seed round investment:
Those currently at $5 million or greater ARR raised a median Seed round of $2.6 million, and 80% raised less than $3.5 million
Those currently at $2 million or greater ARR raised a median Seed round of $2.5 million, and 69% raised less than $3.5 million
The Seed Bridge is dead, long live the Delta.
NYC Tech Week
Visiting long-time friends while running around NYC for tech week!
Gripp Raises $1.5M Pre-Seed to Expand Mobile-First Farm Operations Platform
Gripp, a mobile-first farm operations platform launched through Purdue University's DIAL Ventures, has secured $1.5 million in a pre‑seed funding round led by Ag Ventures Alliance and backed by Two Ravens, Infinity Holding Ventures, C2 Ventures, Tundra Angels, Countryside Angels, DMM Holdings, and Glen Haven Farms (prweb.com). Since rolling out in 2023, the QR-based system has been adopted by over 70 farms with zero customer churn and helps digitize daily tasks, equipment tracking, and operational knowledge. The funds will accelerate product development, sales, and marketing initiatives, and strategic partnerships to grow its market presence.
Breaking Boundaries: Expanding HTM into the Dental Field
At AAMI eXchange 2025 in New Orleans, UptimeHealth co-founder and CEO Jinesh Patel delivered a keynote titled “Expanding Horizons: How Biomedical Technicians Can Diversify and Capture New Markets.” He encouraged HTM professionals to leverage their existing skills to enter high-growth sectors such as dental, veterinary, and med-spa environments. Patel also announced a new partnership between AAMI and UptimeHealth to introduce the Dental Fix Summit at the 2026 eXchange. He emphasized the critical role of HTM experts in enhancing patient care and pioneering equipment-driven experiences, reminding attendees that “healthcare doesn’t exist without us.”
Eskuad’s "Map Forms" brings climate-smart forestry to the forefront by enabling field teams to collect geolocated data via mobile forms—no paper, no manual entry. This empowers real-time tracking of tree planting, carbon sequestration, fire risk, and biodiversity metrics. By integrating GIS layers, customizable workflows, and device-level precision, forestry operations can monitor climate impacts, ensure compliance, and adapt practices more effectively. The result is streamlined workflows, improved data accuracy, and actionable insights, supporting sustainable, resilient forest management in the era of climate change.
Ash and Impilo Partner to Deliver Seamless At-Home Health Monitoring and Testing Nationwide
Ash and Impilo have joined forces to offer a seamless, end-to-end at-home health solution across the U.S. Ash brings a wide range of customizable at-home test kits (e.g., cancer, chronic condition, infectious disease, and HEDIS measures), while Impilo supplies technology-driven logistics and remote patient monitoring devices like BP cuffs, EKGs, glucometers, scales, and wearables. Their unified platform delivers kits and devices in a single shipment, enabling healthcare organizations to close care gaps and support proactive, data-driven care, with no in-person visits required. The service is now available nationwide to enterprise partners, digital health firms, payors, and health plans.
A manufacturer struggled with scattered waste data, including over 20 waste streams, 100+ annual invoices, and 20 different vendors, which delayed annual reporting by more than a year and consumed significant manual effort. By implementing WATS’s specialized waste data platform, they quickly ingested and standardized a full year of data in just days, automated calculations, and converted disparate vendor reports into a unified format. This created a single source of truth, enabled real-time tracking, and transformed what had been months of reporting into a streamlined process taking mere days, freeing the team to focus on waste reduction and proactive management.
Excited to see leaders like Maret Thatcher spotlighting the future of connected construction at ASHRAE 2025. From BIM to digital twins to augmented reality, buildings are no longer static—they’re becoming dynamic, data-rich environments from the ground up. Looking forward to the conversation with Aaron Sorrell and Daniel Kolimar on how these technologies are reshaping how we design, build, and operate.
Founder Story
This is the kind of founder story that sticks with you. Rooted in real pain, real dirt, and real grit. What started as two recent graduates frustrated by outdated surveying tools has grown into Civ Robotics, with over 100 robots now deployed globally, transforming how infrastructure is built. Liav and Tomer didn’t chase trends—they solved a problem they lived firsthand. From dirt, for dirt… and just getting started.
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Welcome friends! We had our annual LP & Founder Day earlier this month. As always, it was a great few hours, very much made so by our incredible LPs and founders. We have more from that event below, but first…
As we were putting together our panels for the event – both of which were, of course, AI-focused (what else does a VC talk about in 2025, after all) – we were frankly blown away by just how many companies we now have using generative AI in their products, including those adding genAI-powered features to products built well before ChatGPT’s debut, and how many we have who built entirely new products that would not even have been possible before the advent of generative AI.
We also got some really interesting industry commentary from our panelists, so we figured it was worth a quick rundown on both (quick by our standards, anyway).
At a time when generative AI is seemingly all anyone in this industry talks about (and where nearly all of the money seems to be going), it may seem a bit strange that we’re not counting our AI companies daily. Still, we don’t think it is when you consider that:
We have not “pivoted to AI” like so (so) many of our fellow VCs have recently announced (this is the same crowd that previously pivoted to “future of work”, “web3”, etc. and generally speaking, have never seen a hot trend they didn’t love “from the beginning”… of the day they pivoted, after seeing some other VC’s LinkedIn post about it the day before).
We view the concept of a “pivot to AI” as a fundamental misunderstanding of what makes this software so valuable to an enterprise customer in the first place.
Which is to say:
We view generative AI in the same way we have long viewed predictive AI – as a tool, not a product. AI is, in both forms, an incredibly powerful tool and often a core component (if not the core component) of a product, but still just a component, not a complete product in and of itself.
In other words, without deviating one bit from our core thesis (investing in B2B SaaS and robotics productivity tools for “dirty, dull, and dangerous” industries) and without specifically targeting, nor limiting ourselves to “AI companies” or “AI products” they are all over our portfolio at this point (and increasingly so over time).
More on this below, but first, a quick refresher on recent AI developments, it’s history thus far, and how to read the various terminology you see all over the startup press.
Background & Terminology
First, we think it’s important to understand that algorithms capable of learning, evolving, and generating novel insights based on the data sets available to them have been in widespread use for at least two decades (often called “predictive AI” to distinguish it from the natural language version, but essentially what all AI is at its core). So, it’s not that AI is brand new, but rather the successful application of these concepts to human language (called “generative AI”) is new.
The primary reason we think it seems to most people that AI just started showing up in software applications two and a half years ago when ChatGPT launched, is precisely this ability we now have to interface with software applications using natural human language, and (via ChatGPT and others) the fact that literally anyone with an internet connection can try out a version of it.
By contrast, the value added by the predictive AI that has been around for years has generally happened in the background of applications, so while an end user who upgraded their data-parsing app from one using an old rules-based engine to one using predictive AI would surely notice an improvement in outputs, they wouldn’t otherwise know that those improvements were the result of the software teaching itself to produce better results (versus just a better rules-based engine).
It is also this language-based, generative AI that nearly everyone refers to when they use the generic term “AI” these days, because, again, this version is far more tangible to the average person.
We further believe it is this natural language interface that has fooled a lot of people (including many who should know better) into thinking this technology is far more advanced than it actually is, but we’ll come back to that.
First, a look at where we see the value in AI and the extent to which it has made its way into our portfolio (in particular, the new generative AI).
The True Value of AI & Our Portfolio
We have previously written about our view of how the generative AI stack will shake out over time (including in both our 2024 and 2025 annual predictions pieces), with a handful of foundational large language model (“LLM”) companies training basic models upon which another layer of products built for specific industries and use cases will sit, and that seems to be how things have more or less gone so far.
While we have shared some skepticism around the valuations and extreme concentration of capital being thrown at these foundational LLMs, they are sufficiently capital intensive that neither we nor really any of the VCs as much as 25x our size (i.e., most of the industry) really has the wherewithal to play in that space (and better Andruyoshi Sonawitz’s money than ours).
So, when we talk about the counterpoint to our view/approach to investing in generative AI applications, it’s not the LLMs we’re referring to, but rather those companies within the application layer that are AI-first, use-case-later (of which there are many and with substantial funding).
We alluded earlier to how much of our portfolio now incorporates AI as a core piece of its products, and here are those numbers:
Fully 60% of our investments since the beginning of 2023 (a few weeks after ChatGPT’s public debut) use generative AI as a core component of their product.
40% of our post-ChatGPT investments fall into the aforementioned category of companies whose products weren’t even possible prior to the advent of generative AI
55% of our pre-ChatGPT companies have now added new generative AI-powered features to their existing products
80% of our companies added since 2023 use some element(s) of predictive AI (predictive + generative totals more than 100% because several companies use elements of both)
100% of our post-2023 companies incorporate at least one or the other as a core component of their products’ functionality.
Clearly, we think extremely highly of AI’s capabilities; let there be no question about that. We very much share the general market sentiment that both established predictive AI and newer generative AI are powerful tools with the potential to dramatically enhance existing SaaS platform capabilities, while also powering entirely new solutions that were not previously possible, and expanding companies’ competitive advantages exponentially over time as AI engines are trained on increasing quantities of proprietary, industry- and application-specific data sets.
To reiterate what we noted above, however, these numbers are what they are because the products we believe provide maximum utility and ROI to their enterprise users are overwhelmingly those employing AI, not because we’ve suddenly decided everything must be AI. It might not sound like much of a distinction, but we believe it is and will be a material differentiator between companies that succeed and those that do not.
This ultimately comes down to our belief that founders and investors alike should be viewing these AI tools as features rather than standalone products. In our experience, the most powerful technology in the world will not be of much use (nor sell particularly well) to enterprise companies unless it comes off the shelf capable of solving customers’ most pressing problems (i.e., without further customization), and non-technical personnel can quickly grasp a product’s capabilities and easily incorporate that technology into their daily workflows.
This means that even in cases where AI functionality may be the core foundation for an enterprise SaaS platform, the most effective, best-selling, and highest ROI AI products are still those that sit within a traditional SaaS framework, and conversely, powerful products that require high-levels of customization and user-sophistication don’t sell, or don’t stick (or some combination of both).
This is why our best-performing, AI-powered enterprise SaaS portfolio companies are always those whose products:
Solve specific, high-leverage problems for target customers
Have UIs that are both intuitive to a non-technical user and designed to align as closely as possible with those users’ existing workflows
Have equally intuitive reporting tools, similarly designed to mirror existing reporting and approval processes for both daily users and management teams.
Can deliver AI-generated intelligence and recommendations in an easily digestible format that is directly tied to each customer’s business goals (and ultimately, their bottom line).
If the backends of these products are juiced with powerful AI tools, great; if AI allows end users to do orders magnitude more via those same UIs without sacrificing their intuitive usability, even better.
Coming back to where AI is headed, as you will have gathered by now, we firmly believe that generative AI (and its predictive predecessor) is truly groundbreaking technology, in no need of any artificially inflated hype or hyperbole. But of course, Silicon Valley is going to Silicon Valley, so we feel compelled to do our civic duty as level-headed, candid realists and provide a quick reality check on some recent claims floating around the venture-verse.
What AI Can/Can’t/Will Be Able to Do
To be clear, we believe the capabilities and utility of this new technology are incredible, full stop. Software capable of reading, parsing, summarizing, and even replicating human writing, as well as supporting free-form, conversational user interfaces, is incredibly powerful. As we noted above, we’re not only seeing the majority of our portfolio companies put generative AI to great use, we have several whose products couldn’t even exist in a pre-generative AI world (it is THAT powerful).
That part of the hype is real and, for the most part, justified. What we’re talking about are the constant, evermore grandiose claims about what AI can already do, and proclamations about how quickly we’ll achieve things like artificial general intelligence (“AGI”) and a widespread replacement of humans in all fields and occupations.
Eric Schmidt (former Google CEO) was kind enough to give us a concrete example of just how overheated the hype machine has already gotten in a recent interview, where he said:
AI will have replaced “the vast majority of engineers” within one year (one year!)
We will have achieved AGI within 3 - 5 years.
Even for the bros who cry wolf in Silicon Valley, these are absolutely outlandish predictions.
(Incidentally, if one of these $100 billion LLMs ever goes under and takes down most of the valley with it, as it probably would, “The Bros Who Cried Wolf” would be a great title for a retrospective on the whole affair. Someone get Michael Lewis on the phone.)
Before we look at each of these claims, we thought it was worth pointing out that with respect to the AGI comment, Mr. Schmidt said “I call this the ‘San Francisco consensus’ because everyone who believes this is in San Francisco. It may be the water.” Pretty sure it’s not the water, but just as a thought experiment, maybe try talking to someone outside the metro area once in awhile (or even just once)? Just something to consider.
Anyway, taking these one at a time:
The End of Human Software Engineers
We asked one of our event’s AI panels to opine on this one, and suffice it say, they too thought it was preposterous (it actually elicited audible scoffs from a couple of panelists). Part of the issue here is that Schmidt and others in this camp are starting from a premise that seems to have been manufactured from whole cloth, that GenAI-based coding assistant apps are already producing 10x productivity gains (the implication being that they are already capable of writing extremely advanced code with minimal human oversight).
Well, nearly all of our companies are now using one or more of these coding-assistance apps. While we haven’t spoken with every one of them about their specific productivity gains, the consensus from those we have talked to seems to be that they’re seeing around a 3x bump. This is unquestionably a huge deal, especially for thinly capitalized early-stage startups, but 3x is a long (long) way from 10x, especially in this context.
More importantly though, per these founders with whom we’ve spoken, as well as all three of our panelists, GenAI coders are still making a lot of mistakes and/or just making things up (“hallucinating” in industry parlance), such that on average, 15-20% of the code produced is junk. Furthermore, it’s not that the AI coder will produce a finished product that's 80-85% correct and just needs some fine-tuning after the fact. These coding assistants must be consistently prompted and their output monitored in real-time by human users, lest the AI engine continue to build new code on top of any junk or even go completely off-script and write an entirely different application from what was intended. Our panelists suggested that today’s AI-coding apps could maybe build a very basic website more or less unsupervised, but anything more complicated than that still needs consistent, active human involvement and oversight.
In fact, per our panelists (and consistent with our view as well), these apps, regardless of how much they improve from here, won’t even reduce the number of engineers employed by tech companies, let alone replace them entirely; they will simply increase the amount of software that is produced. This indeed tracks with where we see the end-user market as well. We can confidently report that not only is the customer base in our corner of the tech world nowhere near saturated in terms of their automation needs, but it’s unlikely they ever will be, and the software industry will need to continue scaling to meet that ever-rising demand.
Again, we don’t mean to downplay this tech in an absolute sense; 80-85% success here is still a huge leap forward. It’s just that, well, this isn’t like one of those past fads (e.g., Web3) that did in fact need manufactured hype to be saleable. Here, what’s actually happening is plenty incredible on its own; we don’t need to make stuff up.
AGI Part I: Are We Actually That Close?
Whether GenAI is “smarter” than predictive AI is something for people brighter than us to opine on, but as far as we’re concerned, neither better approximates human-like intelligence than the other, it’s just that GenAI appears to be more human-like because it communicates with its users in those users’ native (human) languages. As we mentioned above, we think it’s this trait that is fooling people (again, including those who should know better) into mistaking this for true, human-level intelligence.
Algorithms capable of learning, evolving, and generating better and better outputs over time have been in widespread use for at least two decades (that predictive AI we mentioned earlier) and we haven’t heard anyone suggest that any of these platforms are showing signs of “general intelligence”, but now we’re supposed to believe that because we’re feeding these algorithms human language instead of statistical data, they’re suddenly going to become human? One VC’s opinion, but we think that is an incredibly simplistic view of what constitutes true human intelligence.
We would also note that our AI panelists were generally of the opinion that, having already been trained on all publicly available written language, these LLMs are going to start seeing rapidly diminishing returns with each successive release (with one even speculating that this might be the reason ChatGPT’s latest release keeps getting delayed). So, perhaps the SF crowd is simply extrapolating earlier gains out to infinity? Certainly possible but again, they should know better.
AGI Part II: Who Cares?
If we were actually close to achieving AGI, what exactly would that do for us? It sounds cool, but as we discussed in our March newsletter (in relation to our investment thesis), “cool” and “useful” are not always the same thing, so perhaps the more important question is, should AGI even be the goal?
As you may have guessed, we don’t think so. In fact, we think this whole endeavor misses the point entirely and will almost certainly lead to a huge misallocation of resources chasing something that is purely performative. To us, the point of building software is not to replicate human thought and capabilities and just make more of it; software is really (really) good at a lot of things humans are mediocre-to-terrible at (and vice versa), so to us, the most effective use of software development resources is and will always be to focus on its strengths and leave the rest of human intelligence to the abundance of intelligent humans we already have.
This is no different for us than the folly of the current race to build humanoid robots, which we discussed a couple of newsletters ago. Again, one VC’s opinion, but if you ask us, it makes a lot more sense to lean into the strengths of software (and robotics) than it does to try and make a bunch of poor-man’s versions of humans.
2025 C2V LP & Founder Day
What a day. What a community. What a reminder of why we do this.
Last week, we gathered our extended C2V family, founders, LPs, and friends for our 2025LP & Founder Day. It was a celebration, a strategy session, and a reunion all in one. And yes, it stretched well into the night.
It was a moment to reflect on how far we’ve come, from a personal investment vehicle in 2014 to a mission-driven early-stage venture firm with:
60+ portfolio companies
160+ LPs
$32M+ (and counting) under management
A shared thesis that’s as gritty as it is grounded: investing in the “dirty, dull, and dangerous”
We’ve never chased hype. We’ve chased impact. Backing B2B SaaS and robotics companies using AI to solve real-world problems in sectors most VCs overlook. And it’s working.
To our founders: thank you for building the future one hard-earned customer at a time. To our LPs: thank you for betting on a different kind of venture model. To our sponsors, Brex, Goodwin, Carta, and Weaver, thank you for backing the vision.
We’re just getting started.
AI Gone Wrong? Now There's Insurance For That
As AI risks like hallucinations, model drift, and misleading outputs grow, the insurance industry is racing to keep up.
Portfolio company Armilla AI is leading the charge. In partnership with global reinsurer Chaucer Group, Armilla co-developed a groundbreaking AI liability insurance product, offering coverage for AI underperformance, false outputs, and legal claims tied to model failures.
It’s a significant step toward safer and more accountable AI adoption.
UptimeHealth Recognized as a Top Innovator in Enterprise Tech
UptimeHealth has been named one of Fast Company's Most Innovative Companies of 2025, ranking No. 7 in the Enterprise category. This recognition highlights the company's significant impact on healthcare operations through its advanced equipment management solutions.
Boostr Launches Agent IQ Series: A Purpose-Built AI Workforce for Media Sales, Planning, and Ad Ops.
Boostr has unveiled the Agent IQ Series, a purpose-built AI workforce designed to transform media sales, planning, and ad operations. This suite of specialized AI agents automates manual tasks, reduces errors, and enhances performance across the media lifecycle.
Bolt South Africa has partnered with Driver Technologies
Bolt South Africa has partnered with Driver Technologies to offer its drivers a smartphone-based dashcam app aimed at enhancing safety and security. This initiative allows drivers to transform their smartphones into dual-facing dashcams, recording both the road ahead and the vehicle's interior. The app operates in the background, utilizing a picture-in-picture display to confirm active recording without disrupting the Bolt app's functionality.
Well, that escalated quickly. We had a nice venture recovery going here, particularly in exit markets, where M&A and PE buyouts had been steadily gaining steam for a couple of quarters, with venture-backed IPOs recently joining the party as well, and while the Q1 numbers show more of the same… we now find ourselves engulfed in some light economic chaos so as far as what the future looks like, well, who knows.
So, while we’d much rather be contemplating more fun questions like, “Given this liquidity thaw after a multi-year deep freeze, will our next exit result in the two of us collapsing in a puddle of tears like Rory on hole-19 at Augusta a few weeks ago?”, we felt like it would be irresponsible of us not to delve into what’s going on in the economy as a whole and where that might leave those of us in ventureland.
Quick caveat – we have long adhered to a “no politics” rule in our newsletters (plenty of places to find that in other print and West Coast VC podcasting realms), and this newsletter is no different.
In other words, the discussion that follows is not political commentary. We are not attempting to make an argument for any political party, nor any candidate (frankly, we’re increasingly frustrated with all of them these days). Additionally, nothing we say here is in any way meant to be a personal affront to anyone, so we’d respectfully request that you please try not to take it as such.
Okay, enough hedging, we’ve done all we can do here. Let’s get into the discussion…
Trade, Tariffs & Prosperity
We’ll get into why protectionism is not, nor ever has been, the right path to prosperity, but first, a look at the rationale for doing this in the first place.
Why Do We Need This?
Short answer, we don’t. At least not to anywhere near this degree. The “bring manufacturing back” line as a campaign tool is not remotely new. It’s been such a staple of American campaign platforms for so long that we wouldn’t be surprised if half of our elected officials had it tattooed somewhere on their bodies.
That said, the general sentiment is a fallacy in and of itself, and a dangerous one at that. For something to be brought back, it must have been absent in the first place, or at the very least, in persistent decline. This is simply not the case for American manufacturing, now or really at any point in the past 100+ years.
Of course, there are always improvements that can be made, but when you’re starting in as a good a position as we are in the U.S., completely upending the system rather than making targeted improvements on the margins (i.e., using a sledgehammer instead of scalpel) risks doing far more harm than good (and unfortunately, that’s what appears to be playing out right now).
As for that starting point…
1) We have consistently grown our manufacturing base for 150 years and counting
Other than in recessions (when manufacturing declines along with everything else), US manufacturing output has grown every single year without fail, including at a 4.8% annual rate since the end of the Covid recession (the same as GDP overall), a pace that every developed nation on earth (and quite a few emerging ones) would be delighted with.
2) We’re the second-largest overall manufacturer in the world and the most efficient by a mile
While China leads in total manufacturing output (by just under 2x the U.S.), the U.S. manufacturing sector is almost 7.5 times more efficient than China’s, and 1.5 times more efficient than the next closest nation, a testament to both the sophistication of the goods we produce and our relentless commitment to innovation.
In fact, we are so far ahead of the rest of the world in manufacturing efficiency that the gap between the US and the number two country on this list is almost twice as wide as the gap between number two and number seven.
It is also important to remember that manufacturing is just one component of a developed economy in 2025, and relative to these same global manufacturing leaders, we are equally dominant in overall wealth per citizen.
3) Are trade deficits even a problem?
As far as the US running a net trade deficit overall, economists are divided on whether it is immaterial or detrimental. Still, as far as balancing trade with every individual country, you will not find support from a single credible economist, and with good reason.
Let’s look at Vietnam as an example. We run a $123.5 billion trade deficit in goods with Vietnam, our fourth-largest trading partner. Does this mean we’re “losing” to Vietnam? Leaving aside that you’d be hard pressed to find many Americans who would trade their non-manufacturing job for a one-way ticket to Vietnam to work in a sneaker factory, short of stopping nearly all purchases of goods from Vietnam, balancing trade with a country where the average household makes $2,500 – 3,000 a year (~1/40th of the US) is just not possible (can we realistically expect the average Vietnamese family to buy an American car that costs 20-years-worth of their gross income?)
Furthermore, the $123.5 billion of net goods we buy from Vietnam each year is not a permanent condition and if it serves to raise that average household income (as it surely will), trade with Vietnam will start to balance itself with no intervention at all (somewhere Adam Smith is pumping his invisible fist right now).
Our trade history with China is a useful comp here (having 40 years worth of data to work with), but before we do that, a quick note that we are not ignoring the very real facts that a) a wealthier China comes with other material issues (though not ones we should necessarily expect from other emerging markets), and b) they didn’t get here purely through hard work and innovation (there’s also been a fair bit of lying, cheating, and stealing along the way), but as one of our longest-standing emerging market trade partners, it also happens to provide the most robust dataset with which to illustrate this point, so please try to ignore the name and just focus on the data.
From 1985 to 2008, US imports from China grew 87x vs 18x for exports, while China’s GDP per capita grew 15x. As we’ve seen above, China’s GDP per capita remains a small fraction of the US's even today, and in 2008 it was only a third of its current size. But even reaching that meager level of wealth was enough to flip the trend to where the annual percentage growth in US exports to China (on a rolling 10-year basis, to eliminate year-to-year noise) has outpaced the percentage growth in imports every year since.
The result? Since peaking in 2018, our trade deficit with China is down 29%, and last year’s deficit was our lowest since 2011.
Again, we still have a ways to go, this relationship is far from perfect, and we are not defending China’s trade practices here (nor its geopolitical shenanigans), just pointing out that emerging market wealth increasing over time is generally a net positive for US exports and overall balance of trade (and free markets can eventually find a natural equilibrium all on their own).
So, manufacturing in the US is far from dead, nor is it in decline; yet, the idea of a manufacturing rebirth still appears to resonate with many Americans.
Which leads to our next question…
Who Wanted This?
Short answer, almost everyone… and almost no one.
The Cato Institute conducted a survey in July 2024 on globalization, and the results are truly fascinating. We’d encourage everyone to check it out as we’re barely going to scratch the surface here. Still, for our purposes, the most important result is Americans’ response to the following two statements:
1) “America would be better off if more Americans worked in manufacturing than they do today.”
Agree: 80%
2) “I would be better off if I worked in a factory instead of my current field of work.”
Agree: 25%
This is quite a paradox indeed. Whether the average American is aware of just how exceptional we are at making things is hard to know from this (though it seems like many don’t), but it does seem clear that Americans are A) aware that, notwithstanding overall growth, manufacturing jobs have shrunk, both overall (having peaked all the way back in 1979) and as a percentage of the overall workforce, and B) seem to think this is a problem overall.
Yet, very few Americans want a manufacturing job. In fact, given the perpetual problem American manufacturers have filling their open roles, we suspect that even the bulk of that 25% is imagining a manufacturing job that simply doesn’t exist, in any country.
For the most part, the manufacturing jobs that have moved offshore over the years, and those that have proved challenging to fill here, are the dirtiest, dullest, and most dangerous (before you complete that eye-roll, please understand that we’re not suggesting this is true because it’s our core investment thesis, instead it’s our core investment thesis because it is true).
In other words, while much of this offshoring may have initially been driven by profit, those lost jobs have since been replaced with better ones, and almost no one wants to trade back.
As we covered in our prior newsletter, one of the key reasons we are bullish on robotics is that a significant proportion of the roles robots are best suited for are jobs that companies simply cannot fill, and those openings have been on a steady, upward trajectory for 25 years.
Obviously, there’s a fair bit of year-to-year noise and economic cycle impact here, but as you can see, the trend in manufacturing job openings that can’t be filled is only going up over time.
Why is this?
To start with, outside of recessions, the US has been consistently at or above what economists consider “full employment” for decades (essentially the point at which the unemployed are individuals intentionally changing jobs or taking time off to upskill). So, taking a manufacturing job doesn’t mean finding gainful employment; it means trading out of a service industry job, which the average American does not seem to be interested in doing.
But wait, don’t manufacturing jobs pay well? In an absolute sense, yes. Relative to service industry jobs, not really.
According to the U.S. Bureau of Labor Statistics, the only two service industry job categories that average materially lower weekly incomes than manufacturing are the “retail trade” and “hospitality and leisure” sectors. The rest of the service industry earns the same or more per week as manufacturing workers.
Compounding this is that service industry employees work substantially fewer hours on average. Excluding retail and hospitality jobs, the average service industry employee works 10-11% fewer hours each week, resulting in an average hourly wage that is 23-24% higher.
As for those retail and hospitality workers, they earn about half the weekly income of manufacturing workers, but they work even fewer hours (33% fewer hours per week than manufacturing workers).
Perhaps there are some of these service workers who would prefer higher-paying manufacturing jobs, but on the whole, it seems they prefer the lower hours to higher pay. After all, manufacturing jobs are available, and the US has a relatively efficient labor market. Therefore, if individuals truly desire higher-paying manufacturing jobs, they would likely be working in manufacturing (or the retail and hospitality sectors would be paying more to retain them).
In other words, quality of life is also a significant factor here, which leads to our next point on globalization…
Standard of Living
Of course, the metric that matters most to citizens of any country is standard of living, which is not absolute income levels, but rather income minus cost of living (plus a bunch of externalities, one of which we’ll touch on below), and this is where globalization has benefited Americans arguably more than income growth over the past 80+ years.
Back to that Cato Institute survey, when respondents were asked to choose the three political issues most important to them from a list of sixteen (e.g., economy, taxes, immigration, etc.), the number one response was “Inflation/prices” at 40%. So, if the average American believes that globalization and/or offshoring of manufacturing is bad for the country, it seems safe to say that a few months of tariff-driven price hikes will dispel that notion in short order.
We should touch on one additional externality that (if people knew what they were signing up for) would put an additional, and likely significant, damper on the enthusiasm for more low-end manufacturing jobs.
One major impediment we already have to commercial investment in general (x100 for heavy industry) is the “not in my backyard” phenomenon. This is a cousin to the manufacturing job duality above – even if people want more factories, they want them built in someone else’s neighborhood – and in this case, with a very good reason: pollution.
Manufacturing & Pollution – The Quick and Dirty
(Hey, just because we’re off the economic rails doesn’t mean we can’t still have a little fun with this)
Before anyone decides that we should all aspire to China’s current manufacturing base, we’d strongly encourage them to spend a little time there (and if you’ve been to Beijing on even an average air quality day, you know exactly what we’re talking about). It’s not uncommon to look out your Beijing hotel room window in the morning and not be able to see the buildings across the street through the thick yellowish haze in the air (which may or may not also make the entire city smell like burning diapers).
Not only is this no way to live, it also comes with huge public health costs, and we have enough problems with healthcare cost inflation (incidentally, the number two response on that Cato Institute “most important issues” list), without onshoring a few billion tons of burning diaper smog.
As we noted earlier, none of this is to say that we can’t improve in the realms of both manufacturing and trade, but this is a scalpel problem and big, sweeping import tariffs are very much a sledgehammer solution (honestly, they might be closer to an ICBM solution; in all sorts of ways).
Complexity, Efficiency, Price & Choice
If widely applied tariffs on substantially all imports didn’t work 100 years ago, they have no chance whatsoever today. Over that period, the global supply chain has become immensely more complex and interconnected, and there is no putting that horse back in the barn, even if it made sense to do so (and if it made sense, supply chains wouldn’t be what they are in the first place).
Complexity
Just how impossibly hard it is to try and determine what parts of global trade and production are good for America versus bad, and how arbitrary any line drawn between “American made” and “foreign made” will end up being?
1) 10 million cars are manufactured in the US every year. Some by US companies, some by foreign companies (with labor employed here, but higher-paying white-collar jobs mainly in their home countries), and as much as half of what goes into these US-made cars comes from somewhere else. Is this good or bad for the US?
2) US and foreign car companies manufacture tens of millions of cars in dozens of other countries. The labor is local, but (for the US companies) the management jobs are here, and both US and foreign makers use components imported from the US ($93.5 billion worth in 2024). Is this good or bad for the US?
And this is just for the auto sector. Suffice it to say, a material portion of what goes into US-manufactured goods (not to mention the facilities in which they are manufactured) comes from somewhere else, and a material share of what goes into foreign-manufactured goods comes from the US as well.
Self-Sufficient Market Efficiencies
Furthermore, even when we are self-sufficient in an industry (i.e., U.S. supply matches U.S. demand), we’re still better off with freely moving goods, as unencumbered trade flows create huge cost and logistical efficiencies; and in the case of fully (or nearly) self-sufficient sectors, we’d be throwing away those advantages with quite literally no offsetting gain.
For example, the US produces nearly as much steel as we consume (speaking of bipartisan political boogeymen that are entirely fabricated, I’ll bet even our highly educated reader base didn’t know that one); however, it’s not as if every ton of steel produced in the U.S. is consumed in the U.S., nor can the same be said of the iron ore or scrap steel that go into making steel (both of which are also fully self-sufficient sectors).
In 2024, we produced 79.5mm tons of steel and consumed 86.1mm tons. Does that mean we imported 6.6mm tons? No, we imported 26mm tons, but we also exported 10 million tons (any remainder is due to changes in inventory levels). Same for scrap steel, where we consumed 63mm tons, imported 158mm tons and exported 392mm tons.
While perhaps this sounds contradictory to someone who isn’t buried in this stuff every day, we promise you, this is just smart, free-market capitalism at its highly efficient finest.
If we have matching tonnages of scrap steel supply on the west coast and demand on the east coast, but each party can get a better price by selling to Asia and buying from Europe, respectively, would we really want them to trade with each other, when this serves only to increase the cost for themselves and everyone up the production chain from them?
Does it make more sense to sell steel produced in Texas to an auto plant 275 miles away in Monterrey or ship it 1,500 miles to an auto plant in Detroit, when that Detroit plant could get its steel from a producer 250 miles away in Toronto? You end up with the same net-zero trade balance, but at a much lower cost to everyone involved.
Price
Aggregate Demand
Economists talk about how supply and demand react to changes in price in terms of “elasticity”, with “nice to have” goods and services being more elastic (e.g., if Disney World raises ticket prices 30%, they’ll see a substantial drop in visits) and essential goods being highly inelastic (e.g., it’s hard to stop buying groceries, regardless of price hikes). Incidentally, this is also why inflation-griping is always about the price of things like eggs and not the price of things like Disney World tickets.
Well, the literal purpose of tariffs is to raise prices on goods, and while the degree to which higher prices will reduce demand will vary, no product is completely inelastic, and you will see a reduction in aggregate demand for every single tariffed product.
This means that even if tariffs serve to make marginal, less efficient domestic production viable, it will come with an offsetting reduction in demand, and there is a level at which tariffs will end up cutting into demand for the domestic production we already have, never mind anything new.
Household Costs
As we noted above, standard of living is a combination of how much one makes and what that income can buy. Even if tariffs raised incomes in the aggregate, they will both reduce the items consumers are able to buy and make the things they continue to buy more expensive, so the average American is likely to break even at best.
Choice
Sticking with the auto sector example, last year, Americans bought ~371,000 BMWs. BMW actually produced more cars than this (~396,000) in its South Carolina factory, but the US also imported ~200,000 BMWs while exporting 225,000. Why? Because BMW only makes SUVs in this facility and Americans also want to buy BMW sedans. As with the steel market example above, net trade here is already balanced (actually a small surplus), so the disruption that taxing the import leg would cause here doesn’t even have a theoretical upside, never mind a practical one.
This is obviously less pressing than the other impacts of tariffs, but it’s not nothing.
Efficiency
Even if broad tariffs work as advertised, andwe build a bunch of new production capacity, and somehow there is no demand impact, this would still be bad for US manufacturing in the long run.
Yes, you may open the door for marginal, lower-efficiency production capacity to come online in the short-term, but in medium-term, you’ll inevitably invite the current, high-efficiency producers to become complacent and lose that efficiency edge (it’s just basic human nature; if you don’t need to scratch and claw for every little edge, you won’t). So in the longer term, this will not only make us uncompetitive globally and thereby reduce exports (not ideal if your goal is to balance trade), but you’ll also eventually make the whole sector dependent on tariffs (versus just the marginal producers they were meant to help in the first place).
Last thing on this – did we just waste a couple thousand words on a policy that’s looking like it may be entirely scrapped in the next month or two? Possibly, but the 10% across-the-board tariff increases remain in place, and we’re still north of 100% on Chinese goods. We would additionally argue that it’s still useful to examine all of this regardless of where we eventually land, lest we make the same mistake again in the future.
Even more concerning, we also think that going down this road in the first place, and perhaps more importantly, the clumsy implementation and subsequent on-again, off-again whiplash is likely to cause longer-term damage regardless of where we end up once the dust settles (if it settles?).
Why do we believe this to be the case?
Why has America Attracted So Much Investment for So Long?
This part is pretty simple. America has been the best place on the planet to invest for the better part of a century (if not longer) for two primary reasons:
1) Consistent, free-market economic policies.
2) The rule of law.
Do not be fooled by the snap reactions in the stock markets to every bit of tariff-on, tariff-off news. While it’s considerably easier to try and re-price risk in highly liquid public equity markets, it’s extremely difficult to do so when making strategic, capital investment decisions. For CEOs and financiers, these decisions are based on multiyear assessments of opportunity and risk, and the more uncertainty in these assessments, the less the assessors will be willing to invest.
We’re already running way too long here and still need to hit the venture market impacts, but if you’re looking for evidence that the damage done to both of these pillars of American economic success has badly shaken the investor class (both here and abroad) just look at what’s happened to treasuries and the dollar. Unlike equity investors, bond and currency traders generally do not change their long/short biases based on what they believe are short-term disruptions, and they certainly do not make buy/sell decisions based on political biases.
If this all sounds troubling to you, it should.
Venture Impact
On the one hand, we have to think that the current level of upheaval and overall uncertainty is a negative for everyone to one degree or another, and it also seems that we may be headed for a recession sooner rather than later.
On the other hand:
1) Startup and VC markets in general (especially early-stage VC) are about as close to immune as one can get to short-term volatility. We have long investment time horizons (so long in fact, that even the potential tail on this mess should be sufficiently negligible by the time companies we invest in today are fully scaled) and it would take something a lot worse than even a severe recession to materially disrupt the steady upward march of technological innovation.
2) We were overdue for a recession anyway. It’s been 17 years since we had a real one (we’re not counting the pandemic shock, which was kind of its own animal), which is an exceptionally long period by historical standards. So, if it comes sooner than it otherwise would have, it’s not the biggest deal, at least for us.
3) Assuming that at some point here, we come to our collective senses on the right way to optimize the US economy (manufacturing and otherwise), there is no scenario, at least that we see, where technological innovation is not the biggest piece of that optimization.
4) Specific to C2V, as you may recall from last month’s newsletter, a core component of our investment thesis is our postulate that our companies, by virtue of their productivity-enhancing ROI (i.e., allowing customers to do more with less) should be relatively recession-resistant (even more so if you throw higher input prices in the mix as well)
5) A corollary to this for the venture market as a whole is the potential impact on exits and liquidity. Certainly, any recession and accompanying bear market will be a negative for IPOs, but while they tend to get the most press, they generally make up a very small portion of overall venture exits (7% over the past 10 years).
Furthermore, we’ve witnessed what we believe to be a tremendous amount of pent up M&A and PE buyout demand that is not finding enough supply, as evidenced by exit values in both markets going trough the roof in recent quarters (including Q1 of this year), so even if this demand comes down in a recession, there should still be plenty of demand for that limited supply (which we don’t see becoming much less limited anytime soon).
We’ll see how this all shakes out in the coming months, but until then, chin up; we’ll get through this.
C2 Ventures is excited to announce our investment in EDEN, a Seattle-based startup transforming the $400B home services market. EDEN enables HVAC and home-service contractors to close more deals with instant quotes, transparent pricing, and deep home-specific insights, addressing an outdated sales process with a digital-first approach.
Launched in 2024, EDEN has already driven $9M+ in installation sales through its Instant Quote platform, powered by a proprietary building-stock database. Contractors benefit from precision—load calculations, equipment matching, rebate transparency, and dramatically higher conversion rates.
In addition to really nailing the “dirty” and “dull” parts of the thesis, this sector is huge and about as antiquated as they come (i.e., nearly unlimited green space for new software). We believe EDEN is well-positioned to become the go-to customer conversion funnel for contractors across the country.
NYC in Spring Never Misses
Chris had a great week in NYC; always a special time to be in the city. He caught up with some of our favorite founders and a stellar crew of longtime friends and top-tier VCs at the ERA demo day. The lineup included:
Last month, Civ Robotics hit the field with the team at Cupertino Electric, Inc. to demo and train on their latest tech, CivDot+, with spray paint. The result? A masterclass in precision, productivity, and on-the-ground impact.
From the first dot to full deployment, the CEI crew picked it up fast and started transforming their workflow on the spot.
Magellan AI Pokes Fun at Podcast Naming Trends with April Fools' Rebrand to "Podgellan AI"
In the spirit of April Fools' Day, Magellan AI, known for leading the way in podcast advertising intelligence, jokingly announced a "rebrand" to Podgellan AI. The playful stunt pokes fun at the industry’s love for all things "pod," while reinforcing Magellan’s commitment to the podcasting space. No need to update your bookmarks—their powerful suite of tools for measurement, attribution, and competitive insights remains the same, and so does the name.
The joke was timed with major industry moments like Podcast Movement Evolutions and their sponsorship of Podnews, showing Magellan AI is not just serious about podcasting—they're also serious about having a little fun.
Paladin's CEO Kristen Sonday On Streamlining Pro Bono
In a recent interview with Law360 Pulse, Kristen Sonday, co-founder and CEO of Paladin, discusses how the platform is transforming pro bono legal work. By leveraging technology, Paladin aims to make it easier for legal professionals to find and engage in volunteer opportunities, thereby addressing the justice gap. Sonday shares insights into the platform's origins, its mission to increase access to justice, and the impact it's making in the legal community.
“Big moment for the team! 🎉 Phalanx was just named one of Virginia’s most innovative startups. Grateful to be building something meaningful with people I admire.”
Phalanx has been selected to participate in the Virginia’s Most Innovative Startups showcase at the 13th Annual Tom Tom Festival, as part of the #EVOLVEConference Technology Track, under the leadership of CEO Ian Garrett.
Tarform CEO Taras Kravtchouk recently joined CNN to discuss the challenges of running a clean tech startup in today’s unpredictable policy environment.
From navigating global supply chains to managing the added complexity of tariffs, building sustainable vehicles is no small feat.
Our portfolio company WATS is helping commercial real estate owners prepare for New York City's upcoming Commercial Waste Zones (CWZ) policy. In a new case study, WATS demonstrates how their waste operations platform enabled a major client to analyze 12 months of data, identify significant savings, and stay ahead of new compliance requirements. Their insights revealed a path to cutting waste costs by 27% per site, proving that more innovative waste management isn't just better for the environment, it's better for the bottom line.
Welcome friends! Well, despite being right in the middle of March Madness, we decided to go entirely the other direction and dive into the what and why of our core investment thesis this month (which as most of you know, will never have “madness” anywhere near its description).
Some of you long time readers may vaguely remember our having done this once before, but it’s been close to three years (and a few thousand new readers) since then, so we figured it was worth a refresh. Plus, quite a bit has happened in that intervening time: the macro trends that partly informed our original decision have actually gotten more supportive, the thesis seems to be working (lots of companies added in those three years that are doing extremely well, and for the reasons we thought they would, which is nice), and while we were pretty sure we were doing the right thing at the time by sticking with boring-but-steady despite quite a lot of buzz over some hot-but-definitely-a-little-suspect options, we now know for sure (though we’re still waiting for Meta to change their name back).
All that and a new portfolio company that we’re excited about despite it being all over Matt’s corner (named Alivo, also from Maine… it’s definitely a little awkward, but we’re working through it).
But first, a quick trip into the decidedly mundane (but at the same time, enthralling?) world of old-economy productivity tools.
Automating the…
It would be an oversimplification to try and lump VCs into two, three, or even twenty different buckets, but for illustrative purposes, we’ll look at our thesis as compared to arguably the biggest (or at least the most recognizable) of these buckets, which also happens to be the polar opposite of ours. We’ll call this the “Shiny New Toy” strategy (or “SNT”).
Anyone who has ever thought being a VC sounded cool was almost certainly picturing a SNT fund — being in on the ground floor of the next consumer-facing household name (Uber, Spotify, Rivian, etc.) or some brand new tech that becomes the next big breakthrough (one of the companies mining asteroids, training an LLM, building a nuclear fusion reactor, etc.). Could be some other version of this, but whatever they were picturing definitely did not include, for example, SaaS that tracks garbage collection and disposal.
So why did our venture dreams take a sharp turn away from all the cool stuff and make a b-line for the world of freight hauling, waste management, utility maintenance, plating & coating manufacturers, etc.? Glad you asked.
Why This?
There are several reasons (in no particular order):
A one-step sales process
As one of our esteemed, OG (first fund, first close) LPs once put it, a SNT founder aims to build the world’s coolest hammer and then go looking for nails, while our founders get so tired of they and their coworkers being hindered by the same loose nails day after day, they eventually get fed up enough to quit that job and build a hammer for it.
What this means in practice is that before a SNT founder even starts selling their product as the solution to a particular customer problem, they have convince the customer that the problem even exists in the first place.
This isn’t impossible, of course (circa 2010, people who really wished they had access to a ridesharing service was pretty close to zero; today, people who are willing to live without Uber is pretty close to zero), but it significantly increases the degree of difficulty for something that isn’t especially easy to begin with (i.e., for every Uber, there are hundreds of products that it turns out no one actually does want).
On the other hand, when a DDD founder walks into a sales meeting, everyone in the room is already well aware of the problem(s) that founder is addressing (often it’s the bane of that potential customer's professional existence), the DDD founder just needs show that their product will solve it.
Clear, Immediate, & Quantifiable ROI (or, Products That Sell Themselves)
Because the customer is already well aware of the problem(s) a DDD product is built to solve, they’re also aware (at least ballpark) of the cost of that problem, and therefore can immediately appreciate both the scale of the products’ potential ROI and the immediacy with which that ROI can start to accrue.
This not only improves the speed and success rate of initial sales, it also does the same for long-term retention (as once customers have seen the better way of doing things, they generally have zero interest in going back).
Huge TAMs & Essential Industries
These old-economy sectors generally start at “massive” and only get bigger from there. In most cases, SaaS companies can hit 9-digit revenue levels without cracking 1% market share. The upside is, for all intents and purposes, unlimited.
These sectors have also been around for decades (if not centuries), have been through dozens of market cycles, and are essential to the US and global economies. In other words, they aren’t going anywhere, almost regardless of what happens on a macro level. Web3 might be a considerably more exciting space than, say, medical imaging, but if we’re in a recession and you have to cut something from your budget, is it your crypto monkey NFT or your kid’s x-ray?
In addition, the nature of these products themselves make them in some ways more appealing in an economic downturn, as they are specifically built to allow companies to do more with less.
The Entire US Economy Needs This; Badly
Annual productivity growth, once a hallmark of the US economy, is now approaching the end of its second decade of nearly uninterrupted decline. On top of this, several major structural trends that were more or less productivity-neutral during much of this time are now actively working against it to a steadily increasing degree. We’re certainly not economists, but from where we’re sitting, there’s no halting this decline (never mind seeing a rebound) without massive (and we mean, massive) new tech investment across just about every major sector (on a related note: the answer to “how do your target customers do this today?” from two different companies we talked to just this week was “by fax”, so there’s plenty of green space here).
Annual productivity growth for all Non-Farm Businesses (government speak for “more or less the whole economy”) seems to have at least stopped declining (for now, anyway), but still sits around half what it was 20 years ago. Meanwhile manufacturing continues to crater, now having seen negative productivity changes in 5 of the past 10 years, plus another 3 that were essentially flat.
In fact, since the 2008 financial crisis, annual manufacturing productivity growth has averaged 0.25% (vs. +3.5% a year in the 20 prior years, including 2008).
Source: U.S. Bureau of Labor Statistics
You’ll find a similar story, pretty much across the board.
For example, the five industries below that we definitely selected at random and not because they’re buying software from C2V portfolio companies like, say, Medmo, Digital Iron, Steelhead, Driver Tech, and Noteworthy...
Source: U.S. Bureau of Labor Statistics
Improving Jobs & Replacing Openings
Contrary to popular belief, almost none of these productivity-enhancing automation products are replacing jobs. On the software front, they’re generally improving jobs, allowing overworked employees to do more with less, while taking away their least-rewarding, most mind-numbing tasks. There are even some studies out there (of both the probably-unbiased and grain-of-salt varieties), based on worker surveys, showing that automation generally has a positive impact on job satisfaction.
Even those robotics platforms that may do all or nearly all of the tasks of what would traditionally have been a full-time human role are generally not replacing jobs; they’re replacing perpetual job openings, a relatively underappreciated and increasingly problematic phenomena of the past 15 – 20 years.
Source: FRED (St. Louis Federal Reserve)
Obviously, there is some natural ebb and flow with economic cycles and more recently, some Covid-related noise, but even accounting for shorter-term volatility there remains a very clear long-term trend. In fact, the smoothed trend line has increased at roughly 5.1% per year since 2003 (2.8x overall), accelerating to 6.6% annually over the past decade. This despite current unemployment rates sitting just above 70-year lows, and a 17% increase in the working age population over this time period. In fact, job openings as a share of working age population have increased from 1.7% to 4.1% over the past 20 years.
Some of this is due to a shortage of qualified candidates for high-skill positions, but the lion’s share are jobs that Americans simply don’t want, because they are too… wait for it… dirty, dull, or dangerous.
(Either that or we've become a nation of hopelessly lazy, selfish, entitled prima donnas. But at least we have robots?)
This has also had the secondary effect of pushing up wages, as some employers become increasingly desperate to fill these persistently open roles, which in turn further increases productivity headwinds.
Source: U.S. Bureau of Labor Statistics
So yes, building and selling a novel software or robotics product is still among the harder things you can set out to do in your career, but we would continue to argue that focusing on a glaring customer need that’s often bordering on desperation and isn’t going away anytime soon lowers that degree of difficulty about as much as it can be lowered.
As a last note on this, we offer a quick salute to the unwavering discipline of our founders in sticking with the practical over the splashy. It’s not easy for a talented, bright, creative entrepreneur to resist the allure of the SNT ethos, even after they’ve chosen to swim in the automation-of-the-mundane pool. Take, for example, some of companies we’ve recently seen showing off humanoid robot prototypes for commercial/industrial automation applications.
They may look cool, but it’s hard to more perfectly miss the entire point of robotics. If the goal is to build something that performs tasks for which humans are most poorly suited, why would you start with a replica of a human? Given that these folks are plenty smart enough to know better, we’d hazard a guess that it’s because the press and public will be far more enthralled by something that looks like this (a shiny new toy):
Than by something that looks like this (a super sophisticated software suite, wrapped in a Dr. Seuss machine):
It may not be pretty, but it can do all of this (fully autonomously), which is ultimately the only thing that actually matters.
We’re pleased to announce our latest in investment in Alivo, from our pre-seed, Tributary Fund. Alivo’s custom AI agents act as a seamless 24/7 virtual customer support and sales team for home service businesses, handling inbound calls, follow-ups, and appointment bookings.
In a business where sales leads are dominated by third-party platforms who will sell each lead to multiple providers simultaneously, delays of even a few minutes in responding to new leads can drop close rates to near zero. With 24/7 human coverage simply not a realistic option for service providers, Alivo’s ability to fully automate the entire lead responses and follow-up sales process is a massive advantage for its customers, often resulting in an immediate 30-50% uptick in monthly sales.
By The Numbers
Dirty, Dull, and Dangerous
Chris offers his perspective on our thesis sweet spot: where legacy meets opportunity. Dirty, dull, dangerous, and ripe for reinvention.
Developing Intuitive Agtech with Gripp CEO Tracey Wiedmeyer
In an interview on Agency29's blog, Gripp CEO Tracey Wiedmeyer discusses the development of intuitive agricultural technology (AgTech). He emphasizes the importance of creating user-friendly solutions that address the specific needs of farmers and agricultural professionals. Wiedmeyer also highlights the role of data analytics in enhancing decision-making processes within the agricultural sector. Additionally, he shares insights into Gripp's approach to innovation and their commitment to improving efficiency and productivity in agriculture.
BriefCatch 4 Launches with Revolutionary AI Bluebook Citation Tech and an AI Advice-and-Examples Chatbot
BriefCatch has launched BriefCatch 4, introducing groundbreaking AI features for legal writing. The update includes an AI-powered Bluebook citation tool that automates legal citation formatting, enhancing accuracy and efficiency. Additionally, it offers an AI advice-and-examples chatbot, providing users with tailored writing guidance and examples. These innovations aim to streamline legal writing processes and improve document quality.
The Tarform Luna EV Motorcycle, winner of the iF DESIGN AWARD 2025, seamlessly fuses art, craftsmanship, and technology into a striking electric ride. Designed with a modular approach for lasting durability, it redefines sustainability by challenging mass consumption in favor of longevity. Tarform is dedicated to zero-emission manufacturing, integrating cutting-edge technology with ecological responsibility to move towards a zero-waste future. Every element is thoughtfully crafted to celebrate the essence of motorcycling while embracing the evolution of modern mobility.
The most innovative companies in enterprise for 2025
Fast Company highlights the most innovative enterprise companies of 2025, with AI playing a central role in many of their advancements. Companies like UptimeHealth ensure that medical, dental, and veterinary practices keep their equipment running smoothly. In 2024, it launched a medical equipment management system integrating various devices to predict maintenance needs and prevent downtime. The system, liken to Google Home for medical practices, uses IoT technology to collect equipment data and automatically dispatch technicians or guide staff through simple fixes. The company grew rapidly, increasing revenue from $1M in 2023 to an expected $10M in 2024, expanding to over 6,000 locations. UptimeHealth also acquired Dental Whale and partnered with Darby Dental Supply to enhance its service offerings.
Wats attended the Future Food-Tech meeting with industry leaders, hearing fresh perspectives and diving into the challenges facing food manufacturers today. One theme stood out: waste isn’t just waste—it’s a resource. Companies are eager to turn byproducts into valuable inputs.
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Welcome Friends! With February shorting us a couple of days to get the newsletter finished, we’re getting right to it.
In putting together our January newsletter, where we took a high-level look at the 2024 performance of the broader venture and exit markets, we came across a couple of strange and unique sign-of-times phenomena that we felt warranted a closer look.
Startup Funding Concentration
Let’s start with startup funding and, more specifically, where those funds are going.
As discussed in our prior newsletter, total startup investment in 2024 increased for the first time in three years. Every funding stage from Series A to however far into the alphabet we’re going these days showed a year-over-year increase in funding, that Series A+ contingent overall increased 33%, and while Pre-A declined, it was only by 6.5%.
On its face, this would appear consistent with a market on its way back up after a couple of post-bubble reset years (which we’re seeing signs of elsewhere as well). In other words, it aligns with normal market ebbs and flows. But what’s not normal is that a truly incredible 20% ($41 billion) of all startup funding in 2024, and 44% of late-stage investment, went to just five AI-related companies (across seven funding rounds) – Databricks, OpenAI, xAI, Waymo, and Anthropic. If you remove those seven rounds (out of 15,260 on the year), overall startup funding goes from +29% to essentially flat, and Series C+ funding goes from +32% to -7%.
We don’t highlight this to pour cold water on the recovery – the market has improved considerably in the past 12 months – we bring it up because one-fifth of all venture money deployed over 12 months going to 0.05% of funding rounds is a statistical outlier of crazy proportions.
Yes, there is an outsized round or two in nearly every 12-month period, but not like this. In fact, the only year that comes close is last year, when two of these same companies (Anthropic and OpenAI) accounted for 10% of all startup funding.
Bubble 2: AI Boogaloo?
(For the eight of you who got that reference - and maybe chuckled despite yourselves - obviously, this writer-reader relationship was just meant to be).
Of course, this begs the question (which has been the subject of much industry debate for the better part of the past two years): Is this the next big thing or another bubble? And the answer most likely is… yes.
First, a quick look at the aforementioned big AI companies, why they might need such huge sums of money, and why they may or may not warrant the incredible valuations attached.
Do the Dollar Amounts Make Sense?
Yes, more or less. Unlike most (all?) of the SaaS companies who dominated late-stage funding in the 2019/20/21 bubble years, the business models of foundation-layer GenAI companies are inherently capital-intensive, requiring massive amounts of data, computing power, and engineering/data science staff. Could they be more efficient with all this money? Probably, but we’re at least in the ballpark of plausibility this go-around.
That said, the concentration in these companies within VC portfolios looks like it may be a departure from prior years/strategies (potentially in a big way). While ~30% of the funding for these outlier rounds came from Google, Microsoft, and Amazon (for whom these kinds of numbers are basically rounding errors), the remaining ~$30 billion came from VCs, and while these late-stage funds have gotten really big in recent years, this still seems like an awful lot of eggs in a basket that could be anywhere from the greatest basket ever made to one whose bottom dissolves the first time it rains. More on that below.
Do the Valuations Make Sense?
This is a bit trickier to answer. In addition to the huge range of revenue multiples among companies, the quality/stickiness of this revenue and the sustainability of current growth trajectories are harder to gauge from here in the cheap seats. With the caveat that specifics on these rounds and company financials are sparse, all second-hand (press releases and journalists citing sources), and occasionally conflicting (so every number below comes with a “according to what we’ve read” qualifier) here’s what we found:
As you can see, these multiples are all inflated to some degree (public SaaS companies are currently averaging around 7x revenue), but is that justifiable? Well, here’s our quick and dirty take, based on minimal information and 10 minutes of analysis (i.e., a grain of salt might not be enough):
Databricks
This valuation actually seems pretty reasonable:
While above market at 20x, it’s in line or below most other high-growth companies and way below some of the names out there that still seem to be trading purely on FOMO
80%+ gross margins (way above most public SaaS companies of recent IPO vintages)
At or very near positive cash flows while still growing at more than 60% annually
Waymo
Maybe a little excessive, but we get it:
10x TTM revenue growth; TBD if that’s sustainable, but even at half that pace, this revenue multiple is in the 30s by December
Massive, obvious, and easily quantifiable upside, based on years of rideshare demand, pricing, and price elasticity data courtesy of Uber
Unlike Uber, which keeps around 20 – 25% of what its customers spend, Waymo gets to keep all of it (eventually; in theory).
Anthropic, OpenAI, xAI
This is where we start to get a little into fantasy land
Compared to the others on this list (and the overall SaaS market), OpenAI’s multiple doesn’t seem crazy…
… except that it’s not a SaaS company. Sure, they charge a subscription, but the economics are entirely different, annual losses continue to exceed revenues and don’t seem to be improving even as revenues scale rapidly, and whether that will ever change remains a big unknown (see above re: how expensive it is to run these current LLM models).
And if rumors are true, they’re on the cusp of raising a $40 billion round ($40 billion!) at a $300 billion valuation (double where they were priced 3 months ago)
Anthropic – ditto all of the above, but with a worse multiple
xAI – possibly a cheaper to build/maintain model (using large amounts of synthetic data for training), but at an Elon-cult premium even less defensible than Tesla’s. Also, it’s apparently trained on Twitter data, so pretty good chance it ends up being the absolute worst fake human on the planet.
We have no doubt the VCs in these companies took an extremely thorough, measured, reasoned approach to these investments, and they could very well turn out to be big winners. At the same time, though, this is a brand new market that is barely understood at this point (not just use cases and monetization, but the tech itself, which is very much still at the “throw a bunch of stuff and the wall and see what sticks” stage), and the market will almost certainly look dramatically different in 5 - 10 years, if not sooner.
We’ve previously compared the LLM space to cloud computing (suggesting it may also evolve into an oligopoly) due mainly to the similarly large amounts of capital needed to reach and sustain the necessary scale, as well as the potential of being the foundation on which all GenAI software is built (hence being called “foundation models”). But given the relative complexity and newness of the tech involved, it may also behave more like search; not in the sense that we expect to end up with one dominant player, but that the version of the tech that ultimately emerges as the winner could take quite a while to sort itself out.
The first ten years or so of the search market were incredibly fluid, with market leaders changing constantly and no one grabbing a particularly meaningful share and holding it for long. Of the top five companies in 1994 (equivalent timeframe to LLMs today), only three remained five years later, and only one made it ten years (this is a great visualization of that period and beyond). We wouldn’t be surprised to see something similar (or perhaps even more dramatic) play out here. In fact, we’d be more surprised if the current LLM leaderboard had the same players on it in three years than if it had none of them.
This is not to say that VCs shouldn’t be making bets on how they think this will play out, that’s what we do, after all. But what we also do is make asymmetric bets (i.e., small downside, huge upside), and that’s what VCs did with search companies in the mid-90s. Even adjusted for inflation, these companies raised money in the tens of millions (in total, across all rounds), with private valuations low enough that companies could IPO at valuations well under $1 billion and still generate double-digit multiples for investors at all stages.
Not only are these current AI companies priced to absolute perfection, the pace at which they continue to raise new capital means that even investors getting in at relative “bargains” (e.g., Anthropic at $15 billion a year ago vs $60 billion today) will be so diluted by the time they exit, they’ll need trillion-dollar-plus IPOs just to generate 10 - 20x returns, never mind the kind of homerun you need to return a fund where some of these 9- and 10-figure bets end up at zero. For context, there are currently 18 companies on the entire planet trading at $1 trillion-plus market caps, and none of them (excluding state-owned oil companies) IPO’d anywhere close to those levels.
If you don’t believe us, look no further than the continuing phenomenon of multi-billion-dollar IPOs, in which late-stage investors are lucky to break even.
Dear LPs: Good News & Bad News…
There are definitely some low moments for every VC, but it’s hard to imagine anything worse than having to write an email to LPs explaining that your portfolio company just had one of the 45 largest tech IPOs ever... and you lost money on it (as happened to late-stage investors in Instacart a year and half ago, which we wrote about at the time), and a close second would be breaking even on such an IPO, which is what happened with Service Titan’s Series F – H investors.
That IPO came in well below the private valuations at all three rounds, but all three included a “rachet” that effectively allowed them to be bought out at cost if the subsequent round (or IPO) came in below the price they paid. A smart inclusion by these investors to be sure, but only something you can generally get away with in distressed markets, and in any event, a 1x return in venture might as well be a loss (x1,000 if that 1x comes on a multibillion-dollar IPO).
There are quite a few more of these single-digit IRR or worse returns on huge exits in the past couple of years, which we won’t go into (C2V Research is a little understaffed at the moment), but suffice it to say that this is what happens when you indiscriminately overpay for deals, as happened with reckless abandon in 2019 – 2021 (even if you win, you still lose), and those losing funding round valuations sound positively quaint compared with what’s now being paid for these hot AI names.
Our advice? Wait for the initial crash and then sift through the wreckage for the companies that will eventually be the long-term winners. So maybe the historical comp is actually satellite and fiber optic infrastructure in the late 90s? We’ll get this sorted eventually. In the meantime…
We are pleased to introduce Gripp as the newest investment from our Tributary Fund, reinforcing our commitment to backing transformative solutions in industries often overlooked by traditional venture capital.
Agriculture is a prime example of a dirty, dull, and dangerous industry ripe for technological disruption. Gripp’s platform addresses a critical gap: preserving operational knowledge and improving efficiency in an industry that still relies heavily on fragmented, manual processes.
The funding will support Gripp’s product development, sales expansion, and strategic partnerships, ensuring more farmers benefit from its simple, cost-effective approach to equipment and operations management.
Given agriculture's increasing labor shortages and efficiency demands, we believe Gripp has the potential to become an essential tool on farms across America.
Gripp has already received multiple industry recognitions, including Top Producer Summit’s Farmer’s Choice Award, CropLife’s Best Ag Apps of 2025, and the $100K top prize at the Farm Bureau Ag Innovation Challenge—a testament to its immediate impact.
We’re excited to support Gripp’s mission and look forward to seeing its continued growth in the agricultural sector.
Sales Continue to be Underappreciated for Early-Stage Startups.
While everyone talks about AI and product fit, the real game-changer in startups is a robust sales structure. Many overlook this critical component—but it’s crucial for driving scalable revenue. Watch Chris’s latest video.
Establishing a reliable waste data baseline
WATS worked with a real estate owner and developer with over 70 million ft² of office space across 158 sites in the US and Canada.
Waste management and sustainability are important to them, and they have been working to improve the accuracy and management of their waste data.
Phalanx.io is asking for support in voting for their product, SendTurtle, on Product Hunt. Please help by sharing and casting your vote here.
5 Best Autonomous Robots for Construction Sites
In a recent article by Unite.AI, Civ Robotics is highlighted for its innovative autonomous solutions in the construction industry. Their flagship products—CivDot, CivDot+, and CivDot Mini—are designed to enhance precision in construction layouts. Notably, the CivDot Mini can autonomously mark up to 17 kilometers of continuous and dashed lines daily with sub-inch accuracy, significantly streamlining tasks like parking lot layouts. These advancements position Civ Robotics as a leader in automating and improving efficiency on construction sites.
A new milestone for #CivDot! Robot #21 has officially marked 4,147 points in a single day—an incredible achievement for autonomous land surveying technology.
Here’s to setting new records and continuing to build the future!
Driver Technologies Becomes the Preferred Safety Technology Provider of Brazos
Driver Technologies has partnered with Brazos Specialty Risk Insurance (BSR) to provide its AI-powered video safety solution to commercial fleet drivers, offering insurance discounts and enhanced road safety. The partnership enables full HD video recording, cloud storage, and AI-driven driver scoring to improve fleet management.
How Computer Vision Enhances Grid Reliability and Resilience
In this IoT For All Podcast episode, Chris Ricciuti, Founder & CEO of Noteworthy AI, discusses how computer vision and AIoT are transforming utility grid management. He explores utilities' challenges, such as aging infrastructure and extreme weather, and how AI-powered smart cameras mounted on fleet vehicles help improve grid reliability, safety, and efficiency. The episode also offers insights into the future of AIoT and practical advice for organizations looking to adopt these technologies.
Cracking the Code: How Startups Can Win with MSP Partnerships
In Episode 57 of The Pair Program, Tim Winkler and Mike Gruen chat with Brian Luckey (CIO at Integris) and Ian Y. Garrett (CEO of Phalanx) about how startups can successfully partner with Managed Service Providers (MSPs) to scale. They discuss what MSPs look for in vendor solutions, the role of automation and AI in cybersecurity, and common mistakes startups make when pitching to MSPs. If you're in tech or cybersecurity, this episode is a must-listen!
