Perspectives

Built on Bedrock: Doubling Down on the Autonomy Platform for Heavy Construction

October 26, 2026

This August, on a water treatment site in Nevada, an excavator dug to grade and loaded dump trucks for a full shift with nobody in the cab. Not a demo lot. Not a fenced-off test. It was the first time an excavator had done real commercial work entirely on its own. Since then, Bedrock has scaled rapidly, providing autonomous excavation services building data centers, manufacturing facilities, and more across the country. Today we're doubling down. Emergence is co-leading Bedrock's $600 million Series C alongside CapitalG. When we first backed Bedrock in 2025, we bet that autonomy could change the economics of heavy construction. In February we wrote that once autonomy delivers throughput, safety, and schedule certainty at the same time, it stops being experimental. This summer it stopped being experimental. This round is about what comes next. Why construction can't wait Industries representing more than 20% of global GDP face critical skilled labor shortages, and construction is exhibit A. Demand for housing, factories, data centers, and infrastructure keeps climbing while the workforce to build them shrinks, and productivity has been flat for decades. The operator seat is where it bites hardest. The U.S. needs to fill roughly 46,000 construction equipment operator jobs every year through 2034, most of them replacing people who retire, and it takes about five years to train a competent replacement. Nearly nine in ten contractors have open craft positions right now, and worker shortages are the single biggest reason projects run late. The industry doesn't have five years. In February we argued that construction needs a new operating model, not better software. Bedrock's answer is autonomy that reasons about the work, on the machines contractors already own. Proven, not promised Bedrock started with the hardest machine on the site. An excavator is an articulated hydraulic beast that has to read the soil, the trucks, and the people around it and decide what to do next. Solve that first and the dozer, the loader, and the haul truck arrive later as easier versions of a problem already beaten. The team's lineage shows: Boris Sofman, Kevin Peterson, and much of the engineering bench came from Waymo, where they helped build the systems that put driverless cars on public roads, and from Caterpillar. While the founding team had unparalleled domain expertise in autonomy and construction, they, nor anyone else, had ever priced an autonomous excavator. This is the definition of an emerging business model, exactly where Emergence loves to partner. Across a series of working sessions, we pressure-tested pricing and packaging against what we'd watched contractors actually pay for: utilization, throughput, safety, and schedule certainty. The result is a model built around the economics of the job site rather than the robot. And because Bedrock retrofits the fleet a contractor already runs, adoption doesn't start with a capital purchase. This model is now translating into rapid commercial traction. "Construction has spent decades trying to squeeze more out of the same two constraints: people and machines. Bedrock changes the math on both. Autonomy that runs on the fleet contractors already own doesn't just make a job site more productive. It changes what the industry can afford to build." Jake Saper, General Partner Why we're doubling down In 2015 we led DroneDeploy's Series A and spent the better part of a decade alongside that team, learning what it takes to get robotics into contractor workflows and build a commercial model around how contractors actually operate. This July, Procore acquired DroneDeploy for $845 million. Those lessons shaped how we underwrote Bedrock, and they're why we moved to double down to co-lead the Series C. "We’ve worked closely with the leaders in construction robotics since 2015, and the pattern is consistent. The hardware was never the hard part. The company that wins is the one that fits the machine into the customer's existing workflow and owns the deployment data. Bedrock chose to be a neutral autonomy layer across fleets contractors already run, instead of asking them to buy new iron." Kevin Spain, General Partner One autonomous excavator is a productivity story. A coordinated fleet of excavators and haul trucks working alongside a human crew is a different way to plan a project, staff it, and decide what gets built at all. Dump trucks, dozers, and loaders are next on Bedrock's roadmap, and the Series C is the capital to get there. We said in February that Bedrock could become the default autonomy platform for heavy construction. Eight months of deployments later, we’re millions of cubic tons of dirt moved closer to that goal. Congratulations, Boris, Kevin, Laurent and the whole Bedrock team. We're thrilled to deepen our partnership. Read why we first backed Bedrock here, and Kevin’s piece on why assemblers win in robotics.

Arcee AI on Why Open-Weights Are a National Bet, Not a Business Model

October 9, 2026

conversation Mark McQuade CEO Lucas Atkins CTO Arcee AI Emergence General Partner Joe Floyd companies solely focused building open-weight models U.S culturally different labs call blue-collar industry tends run elitist think every-person company Look OpenAI Anthropic others actually ship rarely UI top model custom variant trained tuned alongside harness built specifically product rest access capability end three holding disproportionate control shortage opinions right now whether AI's future runs open closed backs choice market builders enterprises locked single architecture handful deciding capabilities exist gets meanwhile made specific bet matters moved post-training people's pre-training foundation scratch capital-intensive pivot people around thought mistake sat Series anyway leading revolution investment glad start driving commitment background early Hugging Face traces back mission democratizing putting hands making customizable ways typically allow gives ownership asset instead renting lab year ago customizing little decided move training bad idea assumption couple big players keep releasing competitive indefinitely relying point failure sit later talked anyone enterprise government consumer venture level thinks wrong mentioned customizability core advantage umbrella motivates broader ecosystem decisions behind API matter optimizing every eventually starts running backbone Markets tolerate long profit political economic safety genuinely transformational say kind concentration small number real risk builder's side practical advantages working weights lets serve customers lower price larger Google raise enormous amounts capital goes compute serving traffic massive scale put toward work directly signatories letter Microsoft Nvidia enough Arcee's name Signing giants vouching support mattered entire business catch surpass China Chinese started arriving months West ahead places sent shock especially fear government's response restrict generally statement startups restricting answer American Western vibe shift since leadership lead overall need xAI gap taken approach state-supported mutually reinforcing pushing deliberate strategy project needs treat something lucrative adoption hobby Shifting makes distinct space staffed team alumni traditional pedigreed resumes skilled scrappy thirty total grounded outcomes credentials able capitalize wave hungry comes nontraditional backgrounds mostly engineering rather research development discipline well-defined steps execute structured dark art world understand complexity architectures pipelines process require original isolate researchers narrow slices pipeline often sixteen across lifecycle data curation mid-training context extension SFT RL Everyone understands full fast develop phase nuance siloed organization ambitions post-Series next eight want DeepSeek Moonshot frontier achievable tiptoeing anymore

Our Open-Weight Bet on American AI Leadership

September 16, 2026

Arcee Emergence years ago backed belief enterprises needed AI built around data goals economics without sacrificing control Since question moved enterprise frontier Open weight models become matter American competitiveness Chinese labs move quickly put capable developers hands domestic remains closed building another path U.S Now 1B+ valuation scaling doubling first company helping build smaller specialized terms happened expanded scope original bet entire 2025 model lineup including 400-billion-parameter Trinity Large approximately 20 million serves evidence frontier-scale open-weight domestically radically different level capital efficiency working Department Energy 17 national laboratories scientific computing extends Arcee's beyond productivity tasks watching Whether ecosystem competes adopting frontier-lab recently joined leaders across industry arguing weights critical leadership expand access competition ownership showing argument looks practice started proving choose performance openness capability Joe Floyd General Partner Read start

Mirage Product Market Fit: The Silent Killer of AI-Native Services Companies

August 13, 2026

Mirage PMF failure mode seeing often AINS companies right now fast revenue growth strong customer retention majority service delivered humans AI happens every time go market faster better cheaper incumbent buyers say yes inflects hire delivery staff meet demand feels call keeps growing platform waiting another human deliver instead investing compounding looks software company gross margins look staffing firm built AI-native services business wrong kind funding founders end make single bad decision made hundred reasonable ones core tension unique simultaneously professional bound clients urgent needs technology must invest building eventually tractable imperatives direct conflict side wins almost paying demanding result relentless pull toward important always responding never curve great leverage flat Venture capital compounds Investors celebrate full picture diagnose Ask question tell getting granular component speed quality macro level automating 40 workflow task specific steps individual handoffs answer danger North Star metrics fundamentally different SaaS product usage proxy value customers ideally touch employees irrelevant need Map step track complete bar finish review needed assign metric person duo businesses doer builder pairing drive report board quarterly nobody owns move ARR per FTE service-delivering generating relative industry baseline least 2.5x creating real watch trend number climb quarter means coming hiring margin calculated honestly lagging indicator 70 command multiples 50 legacy priced cap table catch calculate Include salaries everyone work inference spend goes towards seen exclude first leading third outcome find problem 12 months late takes best worked counterintuitive deliberately stop build portfolio operating rhythm CEO stops selling entirely team builds trains new operators sit top deploys reopens sales staircase ramp uncomfortable maintained low 70s scaling quickly difference yellow line chart depicts path true stakes raise Series good investor recurring double-click want know moved measuring unable questions answered years earlier numbers reveal actually competitive risk crack early dramatically undercut price aggressively advantage mover matter less wait diagnostic infrastructure Define bandwidth ownership visible internally headcount alongside honest fix requires hardest slowing funnel makes goal grow possible enduring high-margin require opposite actions Take updated assessment see currently sits

The Small-Team Advantage

August 6, 2026

AI made teams faster Now design org match startups adopting redesigning company around tools working problem spend days helping best technical founders Bay Area build constraint keep running talent architecture limitation longer writing code feature deployment Instead directing work distributing context making decisions creating feedback loops allow humans operate together effectively shift subtle treat job afterthought going outbuild competitors twice size Talented people differentiator anymore Everyone's hiring pool matters clear direction fast distribution shared tight designed staffed Across portfolio engineers shipping pace possible 18 months ago hard part building software changed verifying correct easier produce real becomes reviewing trusting Agentic handling tasks used require PRs planning sprints reworking large parts codebase starting see comes short common pattern coordination failure decision stalls behind lives someone's head somewhere agent teammate find baton gets dropped system pass cleanly friction compounds everyone moving AI-native services companies face challenge sharper form SaaS exists sell product AINS Every hire workflow handoff either strengthens data flywheel breaks slow sprint shows outcome promised customer go deeper playbook clean case study twenty years spotting patterns early conviction obvious enough act makes individuals productive coordinates productivity leverage exceptional operator finds 5x improvement loses gain ambiguous priorities missing take minutes backs currently generate less revenue per employee non-AI peers organizations built direct mostly tool output environment optimize actually absorb Harper insurance brokerage clearest version seen team brokers underwriting placement end Roughly 11 entire reference average Series today runs 48 trajectory remarkable small stood several worth systems inside CRM support transactional platform voice call-center agents 2 000-carrier matching engine long-range coding unlock integrated operating default concentrated 10 needs judgment in-house labelers feeding validation back looks look show whoever's fastest explicit accessible Strategy live founder's quarterly all-hands exist artifacts anyone written specs structured goals logs neither break units rather big handoffs scoped picked human single sitting acceptance criteria splits smaller overall throughput multiplies changes ongoing projects without asking move infrastructure core side project need measured Quality checks evals learning mechanisms close loop goal behaves self-improving senses decides acts measures adjusts chart shape cheaper role management evolves already staffing differently put percentage points headcount engineering broader tech market structurally sales finance lower translating routing status gathering relaying absorbed new higher coaching prioritization designing become Managers disappear shifts stack Hanover Park instinct applied structure Chris Hladczuk CEO described owning metrics acting product-plus-engineering pod organized functional scale bloat proving illustration bolting onto old profile shifting valuable hires improve raise else Operators ship think different bar great deliberate write track know ones tightening Invest memory alignment protocols office concerns scales ownership targets ceiling floor make everything searchable day access signal operational state probably right feel impossibly producing heavy lifting last scaled adding next leading compound startup employees soon sound outdated servers owns ask turns action