Worth a Look — Belridge Partners

Robotics July 2, 2026

The $2/hr Robot vs. the $40/hr Worker

"The economic incentive for adoption becomes almost impossible to ignore." — Andrew Kang, Robo Strategy

"A robot doing the work of three humans at roughly $2 an hour versus $35 to $40 for a US worker. The economic incentive for adoption becomes almost impossible to ignore."

Andrew Kang, CEO & Co-Founder of Robo Strategy · The Compound and Friends, June 29, 2026

01Andrew Kang invested $1M in Figure AI in early 2024 when his entire VC network told him not to. He is now running a publicly listed robotics fund.
02The most exciting robotics innovation is happening in private markets, locked away from retail investors. Robo Strategy (BOT) was built specifically to give public market investors access to pre-IPO robotics companies.
03Private valuations anchor to the last round and do not update until the next financing. That gap creates alpha for investors who understand the business.
04Global humanoid and quadruped robot shipments grew 250% last year to 53,000 units.
05Goldman Sachs projects 250,000 shipped by 2030 and a $38B total addressable market by 2035 — a number they just revised up sixfold.
06The total physical labor market is $50 trillion annually. That is the addressable market for humanoid robots working 24/7, no health insurance, no 401k, no breaks, no turnover.
07Morgan Stanley is forecasting 54% compound annual growth rate for humanoid revenue over the next decade. Jensen Huang has called it a $40 trillion market.
08Kang says intelligence gets solved in 1 to 2 years. The 3 to 4 year timeline is about building factories capable of producing millions of robots per year.
09The leasing model is where real commercial adoption happens first. Selling a six-figure robot is one conversation. Leasing one that pays for itself in months is a completely different conversation.
10Most robotics companies are moving toward lease-first.

AI Infrastructure July 2, 2026

Where Is the Moat in Foundational Models?

"The moat is not the model. It never was." — Marc Andreessen

"The moat is not the model. It never was."

Marc Andreessen · The a16z Show, January 7, 2026

01The model itself is no longer where value lives. Once a capability is proven possible, replication happens fast.
02Technological moats that used to last a decade now last a quarter. Speed of replication has permanently changed the economics of being first in AI.
03Andreessen's formula: product, integration, distribution, and captured value. None of those four words is "model."
04Distribution is the moat most people undervalue. Google did not win search because it had the best algorithm. It won because it became the default.
05In AI, whoever controls the default prompt interface controls the margin.
06Proprietary data is the only moat that does not commoditize. A competitor can replicate your model. They cannot replicate two years of your customers' workflow data fed back into your system.
07There is a well-defined intellectual pyramid in AI: god models at the top, small specialized models extending to embedded systems.
08Most enterprise value will be built not at the top of that pyramid but at the edges, where proprietary context compounds.
09In consumer AI, momentum is the moat. The model that gets used daily builds context, memory, and stickiness. The best model on a benchmark that nobody opens every day loses.

AI Economics July 2, 2026

Will Token Prices Fall 90%?

"The long-term token pricing should be 1/10 of what it is today." — Nikesh Arora, Palo Alto Networks

"I think the long-term token pricing should be 1/10 of what it is today. When that happens, you will see that people will consume more."

Nikesh Arora, CEO Palo Alto Networks · 20VC, June 22, 2026

01Token prices today are 10x where they should ultimately land. Nikesh expects dramatic reductions over the next 3–5 years as compute supply catches up and frontier models build sustainable business models.
02Free consumer AI is loss-making and consumes half of all global compute, pushing the cost burden onto enterprise and coding workloads that actually pay.
03Enterprise token budgets are a "use judiciously" model at Palo Alto — they monitor usage and only constrain employees who go off the rails, not the power users.
04The smartest employees will use 20x the tokens of average employees. Token-capping to control costs will harm your best AI talent first.
05Token spend as a % of developer salary is the wrong lens. The more interesting question: when tokens get cheap enough, does that number move from 3.8% to 15% or parity with salary?
06Frontier model companies are currently "value maxing, not token maxing" — at $1T+ valuations they need to show gross margin progress, so they're charging what the market bears.
07Ad-funded AI won't solve the consumer profitability problem. The total global advertising pie hasn't grown meaningfully in years, and 60–70% of it is already online.
08Physical AI is a pure depth play with no consumer use case. Models for planes, cars, and robotics will be separate, highly specialized, and deeply proprietary — potentially the most durable moats in all of AI.

AI June 25, 2026

Why Perplexity May Be Undervalued at $20B

"If Dario produces a better model, it's great for us. We benefit from every person's progress at every layer of the stack." — Aravind Srinivas

"If Jensen produces a better chip, it's great for us. If Dario produces a better model, it's great for us. If Apple produces a better device, it's great for us. We benefit from every person's progress at every layer of the stack."

Aravind Srinivas, CEO Perplexity · 20VC, 2026

01Perplexity is the only major AI product that orchestrates across competing models — GPT-5 and Claude Opus run inside the same product harness. OpenAI and Anthropic cannot say that about each other.
02Perplexity Computer is an orchestration system: a model paired with an agent harness that routes each task to the right model, tool, sub-agent, or local chip.
03Without a world-class harness, even a frontier model produces no business value.
04Every time any part of the AI stack improves — chips, models, harnesses — Perplexity's product gets better without Perplexity writing a line of model code.
05Perplexity's revenue tripled since early 2026, driven in part by Anthropic's model improvements. A competitor's R&D spend directly improved Perplexity's product.
06As enterprises fine-tune open-source models for specific domains, Perplexity can route to cheaper specialized models and compress inference costs further.
07"The model is no longer the product" (Greg Brockman). The orchestration layer above models is where economic value concentrates as models commoditize.
08Perplexity Computer's hybrid local-cloud architecture routes simple tasks to on-device models (zero token cost) and complex tasks to frontier cloud models.
09The $20B valuation was set in September 2025 on older revenue metrics. Revenue is now "far far more than before, growing really fast." IPO targeted sooner than 2028.

Robotics June 25, 2026

The ChatGPT Moment for Robotics Has a Higher Bar

"There is no internet you can scrape for the physical world." — Peter Chen, Covariant

"There is no data set to build a robotics foundation model. There is no internet you can scrape for the physical world. In order to have data, you have to build autonomously working systems for real customers."

Peter Chen, CEO Covariant · No Priors Ep. 48

01The ChatGPT moment for robotics has a higher bar than language: you need generality AND reliability, not 70% success with 30% catastrophic failure.
0299% of robots deployed in the world today are pre-programmed dumb machines — they repeat the same motion and cannot adapt to anything new.
03Covariant's foundation model is deployed across 10+ countries on 3 continents, all feeding one central brain.
04A single warehouse can hold 100,000 distinct items a robot must identify, grasp, and route without pre-programming.
05Covariant was founded in 2017 when no AI was good enough to make robots commercially useful.
06Every robot on Covariant's platform shares one model — everything each robot learns in the field improves all the others.
07The data flywheel mirrors Tesla: ship good-enough autonomy, collect real-world data at scale, build progressively smarter models.
08Simulation breaks down for manipulation because contact physics is too complex to model accurately.
09Language models need next-token prediction accuracy; robots need sub-millimeter spatial precision — a fundamentally harder grounding problem.
10The $500B+ global logistics and warehousing market is the first beachhead, driven by e-commerce growth and demographic collapse in warehouse labor supply.

Robotics June 18, 2026

One Brain, Any Robot: The $14B Bet on Skild AI

"There is no internet of robot data." — Deepak Pathak, Skild AI

"Robotics is a data problem. Unlike language or vision, there is not much data in robotics. There is no internet of robot data."

Deepak Pathak, Co-Founder Skild AI · NVIDIA AI Podcast Ep. 295

01There is no internet of robot data — the core reason physical AI is a harder problem than building LLMs.
02Skild raised $1.4 billion to build one brain that runs on any robot, any task, any form factor.
03$14 billion valuation for a company most investors have never heard of.
04Skild's omni-brain runs on humanoids, quadrupeds, robotic arms, and mobile robots from a single shared model.
0590% performance is easy in robotics. The last 10% is why robots have never gone mainstream.
06The digital world is less than 50 years old. Physical intelligence has existed for millions of years.
07The same pre-training and post-training separation that powers ChatGPT is now being applied to robots.
08Simulation can generate trillions of training examples per day but cannot fully replicate the real world.
09A classical factory robot requires a cage, precise measurements, and a setup that costs several times more than the robot itself.
10Edge compute on the robot itself is required because a robot that is falling cannot wait for a server response.