The principle is simple. VCs are soccer stars, but founders play basketball.
Basketball and soccer share much in common. For instance, both involve teams dribbling, passing, and shooting a round ball. But successful abilities and traits in one may not translate to the other.
Think of each profession as a different sport. Venture, growth, and value investing all differ, and all differ from founding.
VCs are all driven and highly intelligent, but so are lawyers, bankers, and consultants. Talent isn't the issue.
Capital confers authority, but not expertise.
Based on resume alone, 80% of VCs would not earn board seats at their portfolio companies. Their experience and skills, much like consultants and value investors, were honed on a field different from the basketball arena where founders compete.
Here's a quick heuristic: sans capital, would you still hire the VC to sit on the board? If yes, wonderful.
This is no slight and works in reverse: 80% of founders would not earn the right to direct VC investments.
To clarify, great VCs are absolutely worth the premium and can reshape a startup's trajectory as all great advisors can. If you find a great VC, do not haggle. Strike a deal, and return to building.
The greatest VCs exhibit the same pattern, understanding their role on the startup team as advisors, not alphas. They are often understated and work tirelessly on behalf of their clients.
The worst VCs exhibit the inverse pattern and imagine themselves as the alpha, not appreciating how a talented peer could have replaced them without changing the exit. They are loud on social media and assume accomplishments from finance or FAANG map to the startup arena. These VCs should run funds on Wall Street, not advise founders in Silicon Valley.
How do we highlight good VCs without attacking bad ones? Many good VCs, as with many good advisors, prefer subdued profiles and dislike self-promotion. This is the challenge.
The original idea was to flag bad VCs, but such a system grants founders too much power to levy unjust charges and settle feuds.
After all, many disputes are legitimate and reflect bad founders. Founders, like all professionals, sit on a spectrum. The surge of big money has spawned plenty of bad ones who, sadly enough, do not represent the best of tech and innovation but rather greed and self-aggrandizement.
The Pincus post sparked a cleaner iteration.
The proposal is a public page/spreadsheet where only founders can post, only after an outcome or a certain number of years, and only with affirmative assessments. Nothing negative, nothing anonymous. Posts must certify no quid pro quo or other VC prodding.
Topics could include responsiveness, support during dark days, absence of alpha syndrome, and other key considerations.
Over time, good VCs should reveal a clear pattern and attract new founders: founders trusting them again with repeat business and consistent high marks across the portfolio, not only unicorns. Arguably, the strongest signal will radiate from the worst outcomes.
Critically, this system won't incite mob justice or expose VCs to unfair accusations, but can still suggest who to diligence more deeply.
The purpose is to spotlight good VCs who advance innovation and startups over time, letting their body of work rise to the top and garner proper recognition.
Of course, it penalizes newer investors and is vulnerable to gaming like any system, but it plugs a small gap. Founders want to find good investors based on data, but good investors dislike boasting.
The overall dataset was very large (8,972,221 deaths), but only 1,348 ambulance drivers were included, and just 10 had Alzheimer’s disease as the underlying cause of death.
Such a small number of outcomes matter because statistical precision depends on the number of relevant events. With only 10 Alzheimer’s deaths, even a small change in the count could substantially alter the estimated association.
Taxi drivers provided more robust data, with 171 Alzheimer’s deaths among 16,658 decedents, but death-certificate Alzheimer’s mortality may not reflect how often taxi drivers develop the disease. Differences in diagnosis or cause-of-death reporting across occupations could influence the observed association.
The large overall sample does not eliminate uncertainty in the much smaller occupational subgroups, particularly among ambulance drivers.
Nonetheless, this is an interesting study and merits further investigation.
Here are more fascinating facts about caffeine and cancer.
Caffeine affects the immune system via at least two opposing mechanisms.
Mechanism 1: A2A receptor antagonism (immunostimulatory)
Tumors and damaged tissues release adenosine, which engages the A2A receptor on immune cells and signals them to stand down. Caffeine antagonizes (i.e., blocks) this receptor.
Mechanism 2: Raising intracellular cAMP (immunosuppressive)
Caffeine also inhibits phosphodiesterase, the enzyme that hydrolyzes (i.e., breaks down) cAMP. cAMP accumulates inside immune cells, which acts as a "calm down" signal.
Note: both mechanisms are dose-dependent. At dietary caffeine levels, A2A antagonism likely dominates, whereas PDE inhibition is weak and mainly relevant at higher concentrations. However, the net immune effect in the tumor microenvironment remains unproven.
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If you would like to learn more, I can outline a framework for technical folks to ease in and become more informed on cancer. Gaps abound. The more people who understand cancer, the faster we get to cures. Moreover, personalized cancer treatment is the obvious future. Knowledge acquired now may pay off later (but hopefully not needed).
On second thought, I will publish something regardless of interest.
It will be an "Cancer for Engineers" framework, delivered via free, open-source Custom GPTs and Claude Skills. (Gemini gems are less reliable in our experience.)
The goal: to ease engineers into cancer via AI personalized introductory curriculums with varying time commitments to enable deeper independent investigation or fast exits if interest wanes: 4 hours, 8 hours, 12 hours.
Basically 1-3 hours per week for a month.
The reason I think some engineers may find cancer interesting, aside from the societal impact:
The human body is like a complex operating system. Cancer is a severe runtime error. Tracing root causes -- like genetic mutations, signaling errors, or immune evasion -- has many parallels to diagnosing system failures.
BTW if anyone from Kaggle/GDM is reading this, we are having issues submitting a benchmark paper for NeurIPS based on the Kaggle Benchmark.
Google models seem to get a different scheduling priority, ironically, enough and take >20 hours to complete a benchmark task that other models like Opus 4.6 finish in <1 hour -- same code path, same task. Would love help if possible since the abstract deadline is Monday (It's last minute because we didn't originally plan to submit this, but someone suggested it.)
For people questioning why to involve GPT and AI assistants:
GPT and AI assistants cannot be fully trusted, but they can personalize learning.
The chief challenge for the framework/handbook will be resolving how to personalize guidance into cancer research while grounding knowledge in trustworthy sources.
For instance, the framework will anchor abstract, dry biological concepts in personally meaningful tracks. Imagine someone you care about is battling lung cancer — the framework may orient learning around the molecular drivers and signaling pathways at play, or perhaps how to explore the treatment landscape while respecting established practices. If you're fortunate enough to not know someone affected by cancer, GPT can help find a personal angle.
The sheer depth of information is staggering. People devote entire careers to niche specialities, and these experts still don't know everything in their niche because our understanding of human biology and disease is constantly evolving. Adapting depth should also depend on the individual and can only be achieved via AI. Static curriculums do not maximize learning in 2026.
I will aim to put together a Cancer 101 for engineers, not sure how to share. Maybe I'll post here or will post to our biomedical GitHub so it can evolve over time?
Great question. The bar for proof in biomedicine is naturally high. I only shared facts because so much is unknown.
If you can find a lab exploring the question, maybe you can support them by helping to raise money for experiments.
As a fun intellectual exercise, dive into the topic and challenge yourself to think about what kind of experiments could shed more light on the subject.
> gatekeepers directly or indirectly control research funding.
Perhaps funding like public grants could be controlled by few? Should not the case for private money?
Relatively common health issues older people tend to get fair amount of private funding after all.
Rich people tend to be older and they are lot more likely to see amongst their friends and family Alzheimer's and Parkison's or even cancer and so forth and be worried about it and thus donate money to them.
In somewhat related (i.e. old people health concerns) life extension research gets all kinds of wacky non traditional research lines get funded all the time, I don't understand why would Alzheimer's would be any different.
If you're a wealthy person lacking a neurobiology background, how do you decide which research efforts are the most promising? Which labs do you back?
Generally, you rely on experts.
Who typically became experts by adhering to the conventional wisdom set by gatekeepers.
"Science advances one funeral at a time" feels apt.
Sadly, the problem isn't confined to Alzheimer's.
Whenever only a few people decide what is "right," the same pattern of stifled innovation will generally manifest itself not by design or from malice, but because it's hard for a small group to be 100% right on what works and what doesn't -- especially on matters as inscrutable as neuroimmune diseases.
I don't think the problem is nearly as big as people claim. Experts are often right!
While there are counter examples and inefficiencies in the system (and there are idea of addressing this, by distributing some part of the money in other ways), we have far bigger societal issues because people do not believe in science, especially where there is an industry lobby sowing doubts.
So I really want to push back against the the idea that the scientific system is broken. While there are real issues, this is still very misleading.
What also happens is these gatekeepers end up being those requested to review papers. When a paper comes up for review that challenges the status quo these gatekeepers nit-pick the paper and recommend it not be published. This happened to my wife on numerous occasions. She has a few unpublished papers because of this. What she found in her research has since become the common accepted knowledge in her field after a few funerals.
Life extension seems like the kind of thing that can get private funding with relative ease specifically because they aren't trying to compete with the government. There are a lot of private foundations that give out grants too though.
Life extension in the private sector is dominated by hocus-pocus and unwarranted optimism. The genetics of mortality is amazingly complex. See this open access monster paper that came out in Nature this week—admittedly “in mice” on mortality and genetic of longevity.
In most engineering fields we don't give the monopoly to people until they have actually demonstrated success beyond a reasonable doubt. There will always be groups of people claiming that the math/methods they happen to know are the best at explaining some behavior (even now there is that learning mechanics paper on top page)
The takeaway is to stop pretending that we can do good science when the ambiguity is so high, the majority of funding should go to people working on more concrete problems. We never locked in on vacuum tubes because the downsides were so obvious and the upsides of silicon transistors (if they could be made to work) were also obvious even to people outside the field, where your talent comes from. At the very least funders can't allow shifting goalposts, make them up front answer questions about the drugs. That will give you something to estimate the value of the drug and then when they come back with study after study outside the ranges they gave, you lower their funding. E.g. This is supposed to work on someone who was stage 2 and stop progression and then 5 years later it only "works" for stage 1 patients.
Strange breakthrough ideas can't even exist in the current system structurally, so going this route is the only logical choice. Which begs the question, why aren't clinical trials a private venture already? Governments are burning billions of taxpayer dollars for either nothing or cynically to keep the boomers alive and voting even longer, while 1/5 children are obese. For the rest of us we've socialized the risk and privatized the profits.
VCs are soccer stars, but founders play basketball.
It’s easy to dunk on VCs, but the herd effect is rational after considering the typical VC’s background, the intense competition for good deals, and the job requirements — to prudently deploy capital.
Who wants to pitch their boss on investing $1-10M in a product no one uses, built by a team of anons?
This is not to defend the process, but merely explain it. It’s not so different from customer marketing. To win a VC, first understand the VC.
Once hired, VCs cannot easily get fired yet they exert immense strategic control.
Nonetheless, many founders interview summer interns harder than VCs.
Heuristic: after removing capital, would you hire the VC to be your boss?
Great VCs are worth the equity and will turbocharge startups. When you find one, don't haggle. Get a fair deal, and get right back to coding.
Bad VCs will destroy companies the same way soccer stars would destroy basketball teams if made the head coach.
The association between pathogens and cancer is under-appreciated, mostly due to limitations in detection methods.
For instance, it is not uncommon for cancer studies to design assays around non-oncogenic strains, or for assays to use primer sequences with binding sites mismatched to a large number of NCBI GenBank genomes.
Another example: studies relying on The Cancer Genome Atlas (TCGA), which is a rich database for cancer investigations. However, the TCGA made a deliberate tradeoff to standardize quantification of eukaryotic coding transcripts but at the cost of excluding non-poly(A) transcripts like EBER1/2 and other viral non-coding RNAs -- thus potentially understating viral presence.
A more accurate title: "Are Cornell Students Meritocratic and Efficiency-Seeking? Evidence from 271 MBA students and 67 Undergraduate Business Students."
This topic is important and the study interesting, but the methods exhibit the same generalizability bias as the famous Dunning-Kruger study.
The referenced MBA students -- and by extension, the elites -- only reflect 271 students across two years, all from the same university.
By analyzing biased samples, we risk misguided discourse on a sensitive subject.
Even Google DeepMind's relabeled MedQA dataset, created for MedGemini in 2024, has flaws.
Many healthcare datasets/benchmarks contain dirty data because accuracy incentives are absent and few annotators are qualified.
We had to pay Stanford MDs to annotate 900 new questions to evaluate frontier models and will release these as open source on Hugging Face for anyone to use. They cover VQA and specialties like neurology, pediatrics, and psychiatry.
If labs want early access, please reach out. (Info in profile.) We are finalizing the dataset format.
Unlike general LLMs, where noise is tolerable and sometimes even desirable, training on incorrect/outdated information may cause clinical errors, misfolded proteins, or drugs with off-target effects.
Complicating matters, shifting medical facts may invalidate training data and model knowledge. What was true last year may be false today. For instance, in April 2024 the U.S. Preventive Services Task Force reversed its longstanding advice and now urges biennial mammograms starting at age 40 -- down from the previous benchmark of 50 -- for average-risk women, citing rising breast-cancer incidence in younger patients.
This is true for every subfield I have been working on for the past 10 years. The dirty secret of ML research is that Sturgeon's law apply to datasets as well - 90% of data out there is crap. I have seen NLP datasets with hundreds of citations that were obviously worthless as soon as you put the "effort" in and actually looked at the samples.
100% agreed. I also advise you not to read many cancer papers, particularly ones investigating viruses and cancer. You would be horrified.
(To clarify: this is not the fault of scientists. This is a byproduct of a severely broken system with the wrong incentives, which encourages publication of papers and not discovery of truth. Hug cancer researchers. They have accomplished an incredible amount while being handcuffed and tasked with decoding the most complex operating system ever designed.)
> this is not the fault of scientists. This is a byproduct of a severely broken system with the wrong incentives, which encourages publication of papers and not discovery of truth
Are scientists not writing those papers? There may be bad incentives, but scientists are responding to those incentives.
That is axiomatically true, but both harsh and useless, given that (as I understand from HN articles and comments) the choice is "play the publishing game as it is" vs "don't be a scientist anymore".
I agree, but there is an important side-effect of this statement: it's possible to criticize science, without criticizing scientists. Or at least without criticizing rank and file scientists.
There are many political issues where activists claim "the science has spoken." When critics respond by saying, "the science system is broken and is spitting out garbage", we have to take those claims very seriously.
That doesn't mean the science is wrong. Even though the climate science system is far from perfect, climate change is real and human made.
On the other hand, some of the science on gender medicine is not as established medical associates would have us believe (yet, this might change in a few years). But that doesn't stop reputable science groups from making false claims.
If we’re not going to hold any other sector of the economy personally responsible for responding to incentives, I don’t know why we’d start with scientists. We’ve excused folks working for Palantir around here - is it that the scientists aren’t getting paid enough for selling out, or are we just throwing rocks in glass houses now?
Valid critique, but one addressing a problem above the ML layer at the human layer. :)
That said, your comment has an implication: in which fields can we trust data if incentives are poor?
For instance, many Alzheimer's papers were undermined after journalists unmasked foundational research as academic fraud. Which conclusions are reliable and which are questionable? Who should decide? Can we design model architectures and training to grapple with this messy reality?
These are hard questions.
ML/AI should help shield future generations of scientists from poor incentives by maximizing experimental transparency and reproducibility.
Apt quote from Supreme Court Justice Louis Brandeis: "Sunlight is the best disinfectant."
Not a answer, but contributory idea - Meta-analysis. There are plenty of strong meta-analysis out there and one of the things they tend to end up doing is weighing the methodological rigour of the papers along with the overlap they have to the combined question being analyzed. Could we use this weighting explicitly in the training process?
Thanks. This is helpful. Looking forward to more of your thoughts.
Some nuance:
What happens when the methods are outdated/biased? We highlight a potential case in breast cancer in one of our papers.
Worse, who decides?
To reiterate, this isn’t to discourage the idea. The idea is good and should be considered, but doesn’t escape (yet) the core issue of when something becomes a “fact.”
Scientists are responding to the incentives of a) wanting to do science, b) for the public benefit. There was one game in town to do this: the American public grant scheme.
This game is being undermined and destroyed by infamous anti-vaxxer, non-medical expert, non-public-policy expert RFK Jr.[1] The disastrous cuts to the NIH's public grant scheme is likely to amount to $8,200,000,000 ($8.2 trillion USD) in terms of years of life lost.[2]
So, should scientists not write those papers? Should they not do science for public benefit? These are the only ways to not respond to the structure of the American public grant scheme. It seems to me that, if we want better outcomes, then we should make incremental progress to the institutions surrounding the public grant scheme. This seems fair more sensible than installing Bobby Brainworms to burn it all down.
If you download data sets for classification from Kaggle or CIFAR or search ranking from TREC it is the same. Typically 1-2% of judgements in that kind of dataset are just wrong so if you are aiming for the last few points of AUC you have to confront that.
I still want to jump off a bridge whenever someone thinks they can use the twitter post and movie review datasets to train sentiment models for use in completely different contexts.
To elaborate, errors go beyond data and reach into model design. Two simple examples:
1. Nucleotides are a form of tokenization and encode bias. They're not as raw as people assume. For example, classic FASTA treats modified and canonical C as identical. Differences may alter gene expression -- akin to "polish" vs. "Polish".
2. Sickle-cell anemia and other diseases are linked to nucleotide differences. These single nucleotide polymorphisms (SNPs) mean hard attention for DNA matters and single-base resolution is non-negotiable for certain healthcare applications. Latent models have thrived in text-to-image and language, but researchers cannot blindly carry these assumptions into healthcare.
There are so many open questions in biomedical AI. In our experience, confronting them has prompted (pun intended) better inductive biases when designing other types of models.
We need way more people thinking about biomedical AI.
> What was true last year may be false today. For instance, ...
Good example of a medical QA dataset shifting but not a good example of a medical "fact" since it is an opinion. Another way to think about shifting medical targets over time would be things like environmental or behavioral risk factors changing.
Anyways, thank you for putting this dataset together, certainly we need more third-party benchmarks with careful annotations done. I think it would be wise if you segregate tasks between factual observations of data, population-scale opinions (guidelines/recommendations), and individual-scale opinions (prognosis/diagnosis). Ideally there would be some formal taxonomy for this eventually like OMOP CDM, maybe there is already in some dusty corner of pubmed.
What if there is significant disagreement within the medical profession itself? For example, isotretinoin is proscribed for acne in many countries, but in other countries the drug is banned or access restricted due to adverse side effects.
Would not one approach be to just ensure the system has all the data? Relevance to address systems, side effects, and legal constraints. Then when making a recommendations it can account for all factors not just prior use cases.
If you agree that ML starts with philosophy, not statistics, this is but one example highlighting how biomedicine helps model development, LLMs included.
Every fact is born an opinion.
This challenge exists in most, if not all, spheres of life.
I think an often overlooked aspect of training data curation is the value of accurate but oblique data. Much of the “emergent capabilities “ of LLMs comes from data embedded in the data, implied or inferred semantic information that is not readily obvious. Extraction of this highly useful information, in contrast to specific factoids, requires a lot of off axis images of the problem space, like a CT scan of the field of interest. The value of adjacent oblique datasets should not be underestimated.
I noticed this when adding citations to wikipedia.
You are may find a definition of what a "skyscraper" is, by some hyperfocused association, but you'll get a bias towards a definite measurement like "skyscrapers are buildings between 700m to 3500m tall", which might be useful for some data mining project, but not at all what people mean by it.
The actual definition is not in a specific source but in the way it is used in other sources like "the Manhattan skyscraper is one of the most iconic skyscrapers", on the aggregate you learn what it is, but it isn't very citable on its own, which gives WP that pedantic bias.
Synthetic data generation techniques are increasingly being paired with expert validation to scale high-quality biomedical datasets while reducing annotation burden - especially useful for rare conditions where real-world examples are limited.
I think their question is a good one, and not being taken charitably.
Lets take the medical assistant example.
> Medical assistants are unlicensed, and may only perform basic administrative, clerical and technical supportive services as permitted by law.
If they're labelling data that's "tumor" or "not tumor", with any agency of the process,does that fit within their unlicensed scope? Or, would that labelling be closer to a diagnosis?
What if the AI is eventually used to diagnose, based on data that was labeled by someone unlicensed? Should there there need to be a "chain of trust" of some sort?
I think the answer to liability will be all on the doctor agreeing/disagreeing with the AI...for now.
To answer this, I would think we should consider other cases where someone could practice medicine without legally doing so. For example, could they tutor a student and help them? Go through unknown cases and make judgement, explaining their reasoning? As long as they don't oversell their experience in a way that might be considered fraud, I don't think this would be practicing medicine.
It does open something of a loophole. Oh, I wasn't diagnosing a friend, I was helping him label a case just like his as an educational experience. My completely IANAL guess would be that judges would look on it based on how the person is doing it, primarily if they are receiving any compensation or running it like a business.
But wait... the example the OP was talking about is doing it like a business and likely doesn't have any disclaimers properly sent to the AI, so maybe that doesn't help us decide.
A bit simpler, but if they are training the AI to answer law questions or medical questions (specific to a case, and not general), then that's what I would argue is unlicensed practice.
Of course it's the org and not the individual who would be practicing, as labelling itself is not practicing.
The author is a respected voice in tech and a good proxy of investor mindset, but the LLM claims are wrong.
They are not only unsupported by recent research trends and general patterns in ML and computing, but also by emerging developments in China, which the post even mentions.
Nonetheless, the post is thoughtful and helpful for calibrating investor sentiment.
Agreed. There is deep potential for ML in healthcare. We need more contributors advancing research in this space. One opportunity as people look around: many priors merit reconsideration.
For instance, genomic data that may seem identical may not actually be identical. In classic biological representations (FASTA), canonical cytosine and methylated cytosine are both collapsed into the letter "C" even though differences may spur differential gene expression.
What's the optimal tokenization algorithm and architecture for genomic models? How about protein binding prediction? Unclear!
There are so many open questions in biomedical ML.
The openness-impact ratio is arguably as high in biomedicine as anywhere else: if you help answer some of these questions, you could save lives.
Hopefully, awesome frameworks like this lower barriers and attract more people.
I'd love to hear more of our thoughts re open questions in biomedical ML. You sound like you have a crisp, nuanced grasp the landscape, which is rare. That would be very helpful to me, as an undergrad in CS (with bio) trying to crystalize research to pursue in bio/ML/GenAI.
Thanks, but no one truly understands biomedicine, let alone biomedical ML.
Feynman's quote -- "A scientist is never certain" -- is apt for biomedical ML.
Context: imagine the human body as the most devilish operating system ever: 10b+ lines of code (more than merely genomics), tight coupling everywhere, zero comments. Oh, and one faulty line may cause death.
Are you more interested in data, ML, or biology (e.g., predicting cancerous mutations or drug toxicology)?
Biomedical data underlies everything and may be the easiest starting point because it's so bad/limited.
We had to pay Stanford doctors to annotate QA questions because existing datasets were so unreliable. (MCQ dataset partially released, full release coming).
For ML, MedGemma from Google DeepMind is open and at the frontier.
Biology mostly requires publishing, but still there are ways to help.
After sharing preferences, I can offer a more targeted path.
ML first, then Bio and Data. Of course, interconnectedness runs high (eg just read about ML for non-random missingness in med records) and that data is the foundational bottleneck/need across the board.
More like alarming anecdote. :) Google did a wonderful job relabeling MedQA, a core benchmark, but even they missed some (e.g., question 448 in the test set remains wrong according to Stanford doctors).
For ML, start with MedGemma. It's a great family. 4B is tiny and easy to experiment with. Pick an area and try finetuning.
Note the new image encoder, MedSigLIP, which leverages another cool Google model, SigLIP. It's unclear if MedSigLIP is the right approach (open question!), but it's innovative and worth studying for newcomers. Follow Lucas Beyer, SigLIP's senior author and now at Meta. He'll drop tons of computer vision knowledge (and entertaining takes).
For bio, read 10 papers in a domain of passion (e.g., lung cancer). If you (or AI) can't find one biased/outdated assumption or method, I'll gift a $20 Starbucks gift card. (Ping on Twitter.) This matters because data is downstream of study design, and of course models are downstream of data.
The principle is simple. VCs are soccer stars, but founders play basketball.
Basketball and soccer share much in common. For instance, both involve teams dribbling, passing, and shooting a round ball. But successful abilities and traits in one may not translate to the other.
Think of each profession as a different sport. Venture, growth, and value investing all differ, and all differ from founding.
VCs are all driven and highly intelligent, but so are lawyers, bankers, and consultants. Talent isn't the issue.
Capital confers authority, but not expertise.
Based on resume alone, 80% of VCs would not earn board seats at their portfolio companies. Their experience and skills, much like consultants and value investors, were honed on a field different from the basketball arena where founders compete.
Here's a quick heuristic: sans capital, would you still hire the VC to sit on the board? If yes, wonderful.
This is no slight and works in reverse: 80% of founders would not earn the right to direct VC investments.
To clarify, great VCs are absolutely worth the premium and can reshape a startup's trajectory as all great advisors can. If you find a great VC, do not haggle. Strike a deal, and return to building.
The greatest VCs exhibit the same pattern, understanding their role on the startup team as advisors, not alphas. They are often understated and work tirelessly on behalf of their clients.
The worst VCs exhibit the inverse pattern and imagine themselves as the alpha, not appreciating how a talented peer could have replaced them without changing the exit. They are loud on social media and assume accomplishments from finance or FAANG map to the startup arena. These VCs should run funds on Wall Street, not advise founders in Silicon Valley.
How do we highlight good VCs without attacking bad ones? Many good VCs, as with many good advisors, prefer subdued profiles and dislike self-promotion. This is the challenge.
The original idea was to flag bad VCs, but such a system grants founders too much power to levy unjust charges and settle feuds.
After all, many disputes are legitimate and reflect bad founders. Founders, like all professionals, sit on a spectrum. The surge of big money has spawned plenty of bad ones who, sadly enough, do not represent the best of tech and innovation but rather greed and self-aggrandizement.
The Pincus post sparked a cleaner iteration.
The proposal is a public page/spreadsheet where only founders can post, only after an outcome or a certain number of years, and only with affirmative assessments. Nothing negative, nothing anonymous. Posts must certify no quid pro quo or other VC prodding.
Topics could include responsiveness, support during dark days, absence of alpha syndrome, and other key considerations.
Over time, good VCs should reveal a clear pattern and attract new founders: founders trusting them again with repeat business and consistent high marks across the portfolio, not only unicorns. Arguably, the strongest signal will radiate from the worst outcomes.
Critically, this system won't incite mob justice or expose VCs to unfair accusations, but can still suggest who to diligence more deeply.
The purpose is to spotlight good VCs who advance innovation and startups over time, letting their body of work rise to the top and garner proper recognition.
Of course, it penalizes newer investors and is vulnerable to gaming like any system, but it plugs a small gap. Founders want to find good investors based on data, but good investors dislike boasting.