Why You Should Join: Inside Edition
Why You Should Join: Inside Edition
Suggestions from George Robson (Sequoia), Vineeta Agarwala (Andreessen Horowitz), Shahin Farshchi (Lux), and Rak Garg (Bain Capital Ventures).
Some opportunities to consider:
Modal has grown revenue >50x in the past year and has taken less than 18 months to reach an eight figure run rate. They’re currently hiring product and systems focused software engineers in New York City.
Etched has built a chip that can run transformers >10x faster than GPUs. They’re currently hiring Machine Learning Researchers, Compilers Engineers, Verification Engineers, Firmware Engineers, RL Design Engineers, interns, and more in Cupertino.
Partnership: our friend Billy joined Rippling ($13B+) before its Series A. The best advice he ever got was to think like a VC when looking for a startup job, so he built a free tool to help others do the same. Prospect uses the same structures and data sources as the best data-driven VCs (i.e. Coatue, Tribe) to help candidates find the highest potential startups to join.
If you’re looking for more promising companies to join, we highly recommend checking them out.
Welcome to “Why You Should Join,” a monthly newsletter highlighting early-stage startups on track to becoming generational companies. On the first Monday of each month, we cut through the noise by recommending one startup based on thorough research and inside information we’ve received from venture firms we work with. We go deeper than any other source to help ambitious new grads, FAANG veterans, and experienced operators find the right company to join. Sound interesting? Join the family and subscribe here:
Why You Should Join: Inside Edition
July 2024
As you know, the goal of this newsletter is to highlight promising early-stage companies to join, and to do so in a way that’s thorough, unbiased, and (occasionally) entertaining.
We do this because startups are risky business, and we think people deserve a quality resource to help figure out which ones are actually worth joining — one that’s less shallow, biased, and hype-driven than most of what’s out there.
Our main format is a series of monthly deep dives. By picking and deeply analyzing just one company each month (all with inflecting/breakout traction), we hope to provide a better starting point than traditional resources like the LinkedIn Top Startups list — to us, it’s important to understand why a company might succeed before joining it, and we felt most resources recommending companies didn’t go deep enough to be helpful with that.
In a similar spirit, since the start of this year we’ve been running a secondary series where once a quarter, we invite a few investors we respect to share a piece on a portfolio company they’re excited about. So far, it’s been a good way to share more opportunities — and perspectives beyond our own.
You all enjoyed the first two editions, so now we’re back with a third.
This entry features four companies in total, each backed by a different firm and each operating in a different industry. All are at an early stage, and all are growing quickly. At the bottom of each piece, we’ve added a few editorial notes on the company and why we respect the investor who’s recommending it.
This took a good amount of work from everyone involved, and we’d like to thank our contributors for participating.
We hope you enjoy :)
Why You Should Join Anterior
Copilot for health insurance approvals
When your doctor orders a scan, prescribes a new medication, or tells you you need a surgery, you usually want to move as quickly as possible. But you also want to know it will be paid for, which for many patients in the U.S. means first getting your insurance company to sign off. In this “prior authorization” process, the insurer’s clinicians review your medical records - often a massive amount of different kinds of information, from test results to doctors notes, PDFs to imaging files - and compare them with a set of care guidelines to make a decision, often taking hours to tackle.Meanwhile, those patients have to wait for even the simplest approvals; in some cases, care is delayed as much as a month. And the problem is not just about speed, but also cost: One-quarter, or $950 billion, of U.S. health care spending goes to administrative expenses, with as much as $570 billion lost to inefficiencies that don’t improve patient outcomes. We at Sequoia have seen again and again, across industries, that AI can be a powerful tool for solving just such challenges. But in this case, the trouble has been that while people with expertise in health care or AI are relatively easy to find, people with both are quite rare. Luckily for us, though, Anterior (fka, Co:Helm) co-founders Abdel Mahmoud and Zahid Mahmood applied to Arc, Sequoia’s company building immersion for early stage companies.
Born in Libya, Abdel came to the U.K. as a refugee at 8 years old. By 15, he had his own software business—and by 18, he had joined the prestigious Royal Military Academy Sandhurst, where he became the youngest infantry officer in the U.K. Then Abdel became a doctor. He loved helping patients, but grew frustrated with the mountains of paperwork that kept him from spending more time with them, and eventually left medicine, returning to University College London for a master’s in computer science and going on to work in product at Facebook and Google. Zahid, too, founded a company as a teenager—the first of his now-four founder journeys followed by a period building payment platforms for large insurance companies, which gave him deep expertise in the industry. Anterior is an LLM-powered co-pilot for administration, handling all the work that comes before the clinical decision-making. Where it might take a nurse several hours to track down, organize and summarize hundreds of pages of records they need, Anterior can do it in seconds, allowing clinicians to increase their output tenfold and focus on what matters most: helping patients. With partners including one of the top U.S. health plan administrators, Abdel, Zahid and their team will soon be making a difference for millions of patients, and the waitlist is growing quickly. And while they are starting with prior authorization, there is a long list of pain points Anterior will ultimately be able to address, from case management to underwriting and beyond.
— George Robson, Partner at Sequoia
Anterior is hiring talented AI engineers, product engineers, QA automation engineers, and more in New York and London - if you’re interested, reach out to their team here. We also recommend checking out the rest of George’s awesome portfolio at Sequoia.
Why You Should Join Valar Labs
Building AI to identify the right cancer treatment
There are nearly ~2M new cancer diagnoses annually in the US, many more globally. To select a therapy, oncologists rely on clinical trials and treatment guidelines, which typically provide data on an overall response rate (ORR), or the fraction of all patients with a given cancer diagnosis who respond to a specific therapy. But this data is still hard to translate to individual patients: for a therapy approved with an ORR of 35%, there are unfortunately two patients who won’t respond for every one patient who will. Identifying the patients who are unlikely to respond could save them valuable time (to try other therapies)—and spare them harsh side effects.
In order to make every single one of these cancer diagnoses, a biopsy of the patient’s tumor is laid onto a slide, stained in standard ways, and histologically classified as cancer upon review under a microscope by a pathologist. During the COVID pandemic, a huge number of these slides were digitized – meaning that every cancer patient already has images of their tumor histology readily accessible for their care team.
What if these same histology images contained far more information than what they are telling us today? What if the slides have hidden insights buried within them, invisible to human pathologists, but accessible to AI? What if these signatures could be used to predict how a patient’s disease would progress? Or whether he or she would respond well to a particular therapeutic regimen?
To answer these questions, the Valar team has partnered with oncologists, cancer centers, and research teams all over the world to build several large registries of thousands of patient specimen images, carefully paired with clinical response data. They have rigorously trained AI algorithms on these retrospective data—and published results showing that AI can indeed learn what humans simply cannot see. Valar’s AI diagnostic tests can predict how aggressive a bladder cancer patient’s individual cancer will be—or whether a pancreatic cancer patient is likely to respond to the FOLFIRINOX treatment regimen—or if an ovarian cancer patient is a high- vs low-likelihood responder to platinum-based chemotherapy.
The strength of these early data have made clinical research partners excited to engage in an early access program for Valar’s first clinical-grade, CLIA-validated diagnostic product (Vesta) in bladder cancer. There is still a lot more to learn, but we’ve heard the feedback again and again from oncologists: if this information was knowable, every patient and provider would want to know.
The Valar co-founders—Anirudh Joshi, Damir Vrabac, and Viswesh Krishna—met as researchers in Andrew Ng’s AI Lab at Stanford. They were intensely motivated to use AI to do something big for patients, and were moving fast to learn everything they could about cancer care.
Perhaps even more impressive than their technical speed has been their extraordinary depth of understanding of the clinical scenarios that they are trying to impact. To earn this understanding, they have cold-emailed and ultimately mind-melded with dozens of oncologists and pathologists. One leading oncologist we spoke with said he loved collaborating with the Valar team because of their “complete humility and willingness to learn about what we do clinically.” These are rare traits in a team that is also so cutting-edge in their own AI, software, and execution capabilities. And these are exactly the combination of skills that will be required to develop, validate, distribute, and scale AI to every oncology mind, and to every care team in the world.
— Vineeta Agarwala, General Partner at Andreessen Horowitz
Valar is hiring talented software engineers, machine learning engineers, and more in Palo Alto - if you’re interested, reach out to their team here. We also recommend checking out the rest of Vineeta’s portfolio at Andreessen Horowitz. As a former physician, researcher, and operator at tech-driven healthcare companies, Vineeta (who holds an MD/PhD from Harvard/MIT) has a uniquely deep understanding of the healthcare space.
Why You Should Join Armada
Bringing AI to the edge
Cloud computing has enabled a revolution in how businesses operate, with 65% of corporate data now in the cloud and over $35B spent on datacenter buildouts last year alone. Now, companies make their decisions with data, and AI is rapidly changing how valuable work gets done. But this wave has hit a wall: the kind of work that builds, carries and defends the world around us—physical, industrial work—has stayed largely the same.
On oil rigs, mines, and defense installations across the globe, faulty connectivity struggles to pull terabytes of data per site back to HQ, process it, send it back, and change business operations in anything close to real time. The largest companies in the world don’t even bother. Meanwhile, cloud bills stack up to the tune of $100s of millions per company, and the hardest workers in America continue business as usual.
Armada is changing that paradigm. Leveraging a deep partnership with Starlink, Armada is building the hardware and software to take AI to the edge: real work, on-site. Its modular datacenters (picture a shipping container full of GPUs) can operate anywhere on land or sea, process data, train and deploy AI models, and deliver results in real time. An oil rig or Navy ship in the Arctic can have the same speed and processing power as if they were stationed inside an AWS datacenter.
A revolution this big demands an incredible team. Founders Dan Wright and Jon Runyan have built and led multi-billion dollar businesses—Dan at AppDynamics and DataRobot, and Jon at Okta. Their CTO, Pradeep Nair, built Azure Global from a skunkworks project to a world-conquering juggernaut. And Prag Mishra, the Chief AI Officer, led the rollout of AI efforts for robotics in Amazon warehouses.
Armada has the opportunity to build a new Cloud giant from the outside-in—this is not an easy-does-it B2B SaaS company. Armada builds hardware, software, and AI applications, and lands them with the largest companies and governments in the world. Already, Fortune 500 companies, US State Agencies, and government programs have joined the armada. This could be a generational company.
— Shahin Farshchi, General Partner at Lux
Armada is hiring talented software engineers, account executives, and more - if you’re interested, reach out to their team here. We also recommend checking out the rest of Shahin’s portfolio at Lux. He has a remarkable track record and has previously partnered with MosaicML, Zoox, and Relativity Space.
Why You Should Join Prophet Security
Autonomous threat-hunting in minutes, not months
Every day, security operations teams at companies all over the world are barraged with hundreds of threat alerts, warning them of possible attacks from nation-states and malicious actors. The alert volume is far greater than any team of threat hunters could ever process on their own, so most alerts go untriaged. This is one reason why the average time to detect a breach was 204 days last year.
Even when attacks are detected, security analysts spend hours investigating: How did the bad guys get access? How long have they been in our system? What did they take? Did they leave anything behind? Security has often been described as a game of cat and mouse, but in practice, it’s trying to find the breadcrumbs that could signal an attack in an impossibly large haystack, before the haystack grows so large that it crushes you.
Automating threat hunting and response is not a new idea. For the past decade, SOAR (security orchestration, automation, and response) tools have tried to eliminate manual work and accelerate security teams. Many have fallen short, requiring analysts to manually map brittle workflows that aren’t always applicable to the unpredictable alerts that swarm a security operations center.
Prophet Security uses language models to detect, investigate, and remediate alerts in minutes instead of months, enabling security engineers and analysts to focus on higher level work. Prophet uses read-only access to other security tools like Okta, Crowdstrike, Splunk, and more to synthesize an alert graph. Because each tool formats alerts differently, the graph is normalized and embedded in vector space to feed a planning and reasoning engine.
Prophet checks contextualized alerts against investigation tools and data sources to decide if the alert is likely a true positive and assess severity. The planning agent produces a step-by-step plan, prompting an instruction-tuned action model to execute and amend the plan recursively as new information is discovered.
The human threat analyst receives a summary of the alert, key findings, a detailed timeline, and a recommendation on what to do next. In early customer deployments, Prophet has found their solution reduces the time to resolution by 10x on real-world alerts, and empowers security analysts to focus on protecting customers, users, and employees.
The Prophet team brings extensive depth in security and AI. Co-founders Kamal Shah and Vibhav Sreekanti worked together as CEO and VPE at StackRox, a container security company that was acquired by Red Hat in 2021. There, they had a key insight: giving security teams more threat visibility doesn’t actually make them more secure without them having a better way to manage threats in the first place. The broader team consists of early engineers from security unicorns like Abnormal Security, Mandiant, Expel, and more.
Prophet emerged from stealth earlier this year with a set of well-known companies as early customers, and has been receiving impressively positive feedback. As malicious actors use generative AI to attack companies more often and with greater ferocity, Prophet is becoming the crystal ball that security teams rely on to power their security operations.
— Rak Garg, Partner at Bain Capital Ventures
Prophet Security is hiring talented machine learning engineers and product designers - if you’re interested, reach out to their team here. We also recommend checking out the rest of Rak’s portfolio at BCV — he’s one of the sharpest and most thoughtful investors we know.
Conclusion
If you’re open to new roles or just curious about their products, Anterior, Valar, Armada, and Prophet Security are all worth checking out. In case you missed it, last quarter’s edition featuring suggestions from Benchmark’s Chetan Puttagunta, Greylock’s Saam Motamedi, Sequoia’s Josephine Chen, General Catalyst’s Trevor Oelschig and Vedant Suri, and A*’s Kevin Hartz is worth reading through too.
We hope you’re enjoying the start of summer. See you next month!
Thanks to George, Vineeta, Saam, Shahin, and Rak for their help with this piece.
In case you missed our previous releases, check them out here:
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Finally, if you’re a founder, employee, or investor with a company you think we should cover please reach out to us at ericzhou27@gmail.com and uhanif@stanford.edu - we’d love to hear about it :)