🔥RTN20: From Big Data to Big Lender

SunRun earnings, DeepMind's new robotic model, new Tesla acquisition

Evolution of capex financing?

Coreweave, this week announced a $2.3bn debt facility, collateralised against its Nvidia chips. Coreweave is a cloud HPC provider, that rents access, predominantly, to Nvidia GPUs (you can see their pricing here). As you know, AI companies need an obscene amount of GPUs to train SOTA models;

Source: ‘Nvidia: Supply and Demand’

This has created a ‘GPU arms race’. Nvidia chips are selling for 1.5x times their MSRP. This inflation is driven by huge AI demand and supply bottlenecks at TSMC. Large funding announcements make a point to highlight GPU acquisitions:

Coreweave’s debt financing (secured against recoverable assets) is the evident form of financing scaled, CAPEX-intensive businesses. Whilst I don’t know the details of the deal, I would speculate at a 25% to 50% Loan-to-Value + equity, secured against cashflows, then Nvidia GPUs.

Buying ‘commodity’ hardware is not a great use of risk equity capital for a scaled business.

Yet this deal has been viewed as novel in the tech world. Why?

There has been a relative lack of sophistication within early-stage venture funding. Primarily driven by low-interest rates, cheap and abundant equity across all stages and a lack of understanding of cap structures by VCs. Though, this is going to change.

My Prediction

1. Venture Capital will be concentrated on investing earlier on the risk curve (pre-Product Market Fit), companies will drive to profitability earlier and maintain higher funding optionality

2. As we enter a hardware and capex-intensive cycle, there will be an increase in non-equity capital available to startups to fund growth such as;

   1. Asset-backed lending: borrowers will lend against hard assets that have recoverability value, where they can understand and underwrite depreciation schedules. These might be chips, plant facilities, AR, net operating losses, inventory etc.

   2. Sale and leaseback: Prevalent in RaaS (robotics) - vendors sell their hardware to a lender to generate cash upfront, the company leases the hardware, taking a spread

   3. Project financing: The company contracts with a customer or government to deliver the project under a concession or offtake agreement that commits future revenue

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*Superconductor Update*

• As we covered last week, there has been a claim around a new superconductor

• There has been a race to replicate the experiment in order to validate its claims

• This week, saw a number of claimed replications of the experiment, with some being successful at -167 Celsius at ambient pressure.

  • Chinese researchers with the Huazhong University of Science and Technology (HUST) have claimed to have successfully replicated the superconductor's manufacturing process, posting a video on Twitter

  • Andrew McCalip at Varda Space, posted this video

  • Lawrence Berkeley National Laboratory (LBNL) replicated the synthesis in simulation, as did multiple other labs

• Physicist Andrew Cote’s Twitter is likely the best source to stay tuned to developements

• What is clear, is that there is a palpable excitement from the scientific and startup community about the implications of LK99

• Which, could, affect every industry that uses electricity

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SunRun Q2 2023 Earnings

SunRun is a leader in US home solar. They sell solar panel systems to homeowners, typically through a long-term lease or power purchase agreement

• Q2 '23:

  • Total revenue was $584 million, up 30% year-over-year.

  • Customer agreements and incentives revenue was $233 million, up 20% year-over-year.

  • Solar energy capacity installed was 296.6 megawatts, up 20% year-over-year.

  

• Net subscriber value was $12,321 in Q2, up from $12,000 in Q1. This metric excludes the impact of IRA tax credit adders which will benefit margins in the future

• Leading in grid services, with the expansion of virtual power plant (VPP) program with PG&E to 8,500 customers

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DeepMind improves its robotic model with RT-2

Why it matters: RT-2 combines the benefits of web-scale pre-training, efficient fine-tuning, and a powerful VLA backbone to achieve stronger generalization, emergent skills, and sample efficiency compared to prior work. This shows that LLMs can be used for training where there is sparse data available, such as robotics

• Google DeepMind has been busy recently putting out numerous new robotic control models, like RT-1 and RoboCat

• RT-2 is a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into vastly improved generalisation for robotic control

  

• Leverage a pre-trained Vision-Language Model (VLM) like PaLM-E as a backbone model. The output tokens represent discrete robotic actions instead of text

• RT-2 shows impressive emergent skills like symbolic reasoning and human recognition not seen in RT-1 or RoboCat

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Best of the Rest

• DeepTech deep dive by Koko Xu

• German chip maker Infineon is building a Fab in Malaysia (!? not sure why they don’t build it in Germany, EU sovereignty?)

• AI Chiplet startups Tenstorrent raises another $100m

• eVTOL Archer Aviation lands US Air Force contract

• Tesla acquires wireless charging company