Competition concerns in the age of AI

Antitrust is the engine of free enterprise: it shapes countless lines of commerce, from tech to toilets, beer to baseball, and healthcare to hardware. Antitrust drives price, quality, variety, innovation, and opportunity.

Today, artificial intelligence is rapidly changing how businesses sense, reason, and adapt in the market. Across every industry, companies are leveraging machine learning to derive valuable insights without extensive employee involvement. But these groundbreaking capabilities are creating an upheaval in how companies engage with competitors and consumers.

Experienced competition and consumer protection lawyers can help companies capitalize on the opportunities AI presents while navigating the terra nova of regulatory and litigation risk. Although it is incorrect to approach AI as a black box, the complexity of AI systems can make reasoning opaque. This means linkages between AI outputs and rational business justifications risk being obscured or even lost entirely.

Yet regulators are unlikely to excuse consumer and competitive concerns merely because an organization cannot explain why certain actions were taken and others were not. Legal exposure exists under the Sherman Antitrust Act, Federal Trade Commission Act (FTC), Robinson-Patman Act, as well as state antitrust and consumer protection laws. By implementing policies and processes that preserve human control and accountability, organizations can minimize legal exposure and avoid unintended consequences.

A proactive and customized approach is critical. AI affects competition and consumers in countless ways, including when used for core business functions.


AI helps companies make pricing decisions by responding quickly to instantaneous changes in demand, inventory, and input costs. By synthesizing and summarizing vast amounts of complex data, it can be a significant aid in building and adapting pricing policies. But the outcomes that AI-assisted pricing generates can also be seen as facilitating per se unlawful collusion, such as price-fixing or bid-rigging. According to FTC Chair Lina Khan, AI “can facilitate collusive behavior that unfairly inflates prices.”

These concerns may arise directly or indirectly from using AI to perform a diverse array of activities such as benchmarking, disaggregating information, signaling, exchanging information, or analyzing pricing trends. Pricing algorithms, for example, may raise antitrust issues when competitors use them to enforce an advance agreement, algorithm vendors initiate or organize an agreement, companies apply algorithms to dramatically raise prices, or even when competitors independently employ algorithms that subsequently engage in collusive conduct.

The U.S. Department of Justice’s Antitrust Division highlights that “the rise of data aggregation, machine learning, and pricing algorithms . . . can increase the competitive value of historical data” and warrants “revisiting how we think about the exchange of competitively-sensitive information.”


Competition concerns in the age of AI by Walter Thompson originally published on TechCrunch

How to prepare a hardware startup for raising a Series A

The world we used to live in — the one that revolved around using cheap money to pump up ARR — is gone.

It came to a screeching halt with rising interest rates, and it’s not on its way back anytime soon. VCs responded as VCs do: by quickly shifting from a “growth-at-all-costs” mindset to focusing on instant profitability while funding metrics shifted from just revenue and growth to including costs as well.

Since the beginning of Q2, a spur of companies, including hardware companies, have come out of the gate and started raising money. The Silicon Valley Bank (and, more recently, Free Republic Bank) debacle has been relatively short-lived, but, given the multiple rollercoasters the industry has been on, one has got to wonder where the goalposts are nowadays.

There’s no debate that the SaaS game has changed, and yet a consensus on Series A funding metrics for these companies hasn’t emerged. However, it is not too difficult to guess that it would be roughly around double the revenue bar at the same or lower cost. It’s a challenging proposition, but a clear and tangible goal to strive for — and one that cannot be applied to hardware companies without revenue in their early stages.

So how can a hardware company raise a Series A amidst yet another “new normal” in this post-low-interest-rate era?

Commit to having deployable hardware

Most hardware companies barely get their product to function — and can only do so using their own engineers and technicians. Hardware in this situation is not deployable on any meaningful scale.

At the Series A stage, VCs want to know that they can pump money into a product that will start going into the market. This does not mean that the product needs to be pitch-perfect; it just means it has to be sufficiently mature to function in a more unconstrained environment outside of the startup lab.

At the Series A stage, VCs want to know that they can pump money into a product that will start going into the market.

Use your ratio of engineering support per hardware as a metric for whether your product is deployable in the way it needs to be. If you have one engineer for the hardware piece you are deploying (not to be confused with non-engineer technical support personnel for customers), you do not have a deployable product.

Now, at a one-to-four ratio, the unit economics become more reasonable. As a stretch goal, you should target to get the human out of the loop entirely, but everything eventually boils down to unit economics.

If you’re hitting 70+% gross margin at a reasonable price, then you can afford to have more support — but it is going to be exceptionally difficult to maintain as an early-stage company with an immature product.

Show tangible proof of high-quality demand

How to prepare a hardware startup for raising a Series A by Walter Thompson originally published on TechCrunch

AI-generated hate is rising: 3 things leaders should consider before adopting this new tech

When you hear the phrase “artificial intelligence,” it may be tempting to imagine the kinds of intelligent machines that are a mainstay of science fiction or extensions of the kinds of apocalyptic technophobia that have fascinated humanity since Dr. Frankenstein’s monster.

But the kinds of AI that are rapidly being integrated into businesses around the world are not of this variety — they are very real technologies that have a real impact on actual people.

While AI has already been present in business settings for years, the advancement of generative AI products such as ChatGPT, ChatSonic, Jasper AI and others will dramatically escalate the ease of use for the average person. As a result, the American public is deeply concerned about the potential for abuse of these technologies. A recent ADL survey found that 84% of Americans are worried that generative AI will increase the spread of misinformation and hate.

Leaders considering adopting this technology should ask themselves tough questions about how it may shape the future — both for good and ill — as we enter this new frontier. Here are three things I hope all leaders will consider as they integrate generative AI tools into organizations and workplaces.

Make trust and safety a top priority

While social media is used to grappling with content moderation, generative AI is being introduced into workplaces that have no previous experience dealing with these issues, such as healthcare and finance. Many industries may soon find themselves suddenly faced with difficult new challenges as they adopt these technologies. If you are a healthcare company whose frontline AI-powered chatbot is suddenly being rude or even hateful to a patient, how will you handle that?

For all of its power and potential, generative AI makes it easy, fast and accessible for bad actors to produce harmful content.

Over decades, social media platforms have developed a new discipline — trust and safety — to try to get their arms around thorny problems associated with user-generated content. Not so with other industries.

For that reason, companies will need to bring in experts on trust and safety to talk about their implementation. They’ll need to build expertise and think through ways these tools can be abused. And they’ll need to invest in staff who are responsible for addressing abuses so they are not caught flat-footed when these tools are abused by bad actors.

Establish high guardrails and insist on transparency

Especially in work or education settings, it is crucial that AI platforms have adequate guardrails to prevent the generation of hateful or harassing content.

While incredibly useful tools, AI platforms are not 100% foolproof. Within a few minutes, for example, ADL testers recently used the Expedia app, with its new ChatGPT functionality, to create an itinerary of famous anti-Jewish pogroms in Europe and a list of nearby art supply stores where one could purchase spray paint, ostensibly to engage in vandalism against those sites.

While we’ve seen some generative AIs improve their handling of questions that can lead to antisemitic and other hateful responses, we’ve seen others fall short when ensuring they will not contribute to the spread of hate, harassment, conspiracy theories and other types of harmful content.

Before adopting AI broadly, leaders should ask critical questions, such as: What kind of testing is being done to ensure that these products are not open to abuse? Which datasets are being used to construct these models? And are the experiences of communities most targeted by online hate being integrated into the creation of these tools?

Without transparency from platforms, there’s simply no guarantee these AI models don’t enable the spread of bias or bigotry.

Safeguard against weaponization

Even with robust trust and safety practices, AI still can be misused by ordinary users. As leaders, we need to encourage the designers of AI systems to build in safeguards against human weaponization.

Unfortunately, for all of their power and potential, AI tools make it easy, fast and accessible for bad actors to produce content for any of those scenarios. They can produce convincing fake news, create visually compelling deepfakes and spread hate and harassment in a matter of seconds. Generative AI-generated content could also contribute to the spread of extremist ideologies — or be used to radicalize susceptible individuals.

In response to these threats, AI platforms should incorporate robust moderation systems that can withstand the potential deluge of harmful content perpetrators might generate using these tools.

Generative AI has almost limitless potential to improve lives and revolutionize how we process the endless amount of information available online. I’m excited about the prospects for a future with AI but only with responsible leadership.

AI-generated hate is rising: 3 things leaders should consider before adopting this new tech by Walter Thompson originally published on TechCrunch

Factors to consider before pricing AI-enabled SaaS

In 2019, I wrote a post on how companies should price their AI-enabled software. I focused on SaaS companies that were developing their own AI and highlighted pricing considerations as they work to improve their models.

Since then, there’s been a meteoric rise of third-party foundational model providers like OpenAI, MosaicML and more. These “AI as a service” vendors have enabled any SaaS player to integrate powerful AI into their application. This has created a mad dash to sprinkle AI pixie dust across the SaaS ecosystem. We’ve seen this among the countless newly minted startups and more established public companies.

The proliferation of this technology raises many questions, including how to deploy it safely, who will win (focused startups or incumbents with existing distribution?) and more. One important area that hasn’t yet been discussed much: how it should be priced.

Below, I lay out a working framework on how to think about pricing the AI in your SaaS application. The space is evolving rapidly, so I’ll update this thinking in future posts.

How much differentiated value do your AI features create?

By definition, these foundational models are accessible to every SaaS provider, so how should you think about pricing what is, in effect, a commodity you’ve integrated into your product? Start with first principles: How much differentiated value does this AI feature create?

By integrating AI features into the flow of your broader platform, you are saving the user time from having to leave their flow to go to the underlying model (ChatGPT, etc). Keeping the user in context can be a powerful unlock.

However, be honest with yourself as to how much value your AI is actually creating. Many AI features in SaaS today are getting a flood of initial tire kicks from curious users but aren’t seeing meaningful sustained adoption. Start by understanding retention and value creation.

SaaS companies should be solving for simplicity and adoption in their AI feature pricing. This is a time for learning and iteration.


Then ask yourself how differentiated your AI offerings are. If the majority of the value your AI feature creates can be garnered by going directly to ChatGPT, don’t try to make a significant margin on that feature. Reselling is not a sustainable value creation strategy (nor differentiation strategy, though that’s a topic for another post).

Even if you aren’t able to charge much for your AI features today, they can create meaningful value by making your current product more valuable and perhaps stickier. They can also be used to drive upsell to higher tiers, all of which can result in increased net dollar retention.

Over time, you can leverage initial features that may today just be a thin wrapper around a third party model to build more differentiated value (more on how below). When you get to that point, you can consider a more value extractive pricing approach.

AI SaaS pricing is in its early days

Factors to consider before pricing AI-enabled SaaS by Walter Thompson originally published on TechCrunch

Six tips for getting the most out of your SIEM investment

Security information and event management (SIEM) is one of the most well-established categories of security software, having first been introduced about 20 years ago. Nevertheless, very little has been written about SIEM vendor evaluation and management.

To fill that gap, here are six top-line tips on procuring and implementing a SIEM solution for maximum value.

Evaluating and purchasing a SIEM solution

Size your spend

SIEM software solutions are priced differently: either by the number of employees in the customer organization, by the rate of events per second, or based on the log volume ingested. It’s important to figure this out early to get a rough idea of what you will pay over time. You’ll also identify the various data sources meaningful to your Security Operations Center (SOC).

Buying a SIEM is a massive commitment: you and your organization will need to live with your decision for years to come.

If you already have a SIEM in place, give the vendor your current use cases and consumption, and they should be able to replicate it. If you don’t, you’ll need to do a little leg work. A good starting point is assessing the volume of logs you’ll send to the SIEM. Measure actual daily log volume from each source by checking out the locally stored logs for a “normal” day and tallying the results.

If the SIEM vendor charges by your number of employees, be wary. This is usually a way to charge more for the SIEM by counting employees who don’t generate any relevant data.

Evaluate your vendor’s practices

The next step is to conduct a proof-of-concept (POC); this should be a starting point for an eventual implementation, not a standalone, canned exercise. During this process, your vendor should demonstrate a service level that you’ll want to maintain post-sale. Here are some key questions to consider during this process:

  • Who will staff your account? Ideally, a vendor will commit skilled technical staff to both execute your initial evaluation and conduct an implementation.
  • Who from your team will take the technical lead on the evaluation, and who’ll ultimately implement it? Ideally this will be the same person or small group of people.
  • After you buy a SIEM, what’s next on your roadmap? SOAR? CSPM? Make sure your vendor can integrate with a broad range of technologies.
  • It’s critical to fully understand the vendor’s front- and backend software architecture. Some vendors calling themselves “true SaaS” or “cloud-native” are not. Don’t lock yourself into a 12-month contract when you don’t know what’s going on under the hood.

Don’t be fooled: Know the total cost of implementation

Six tips for getting the most out of your SIEM investment by Walter Thompson originally published on TechCrunch

Ask Sophie: How long until I can travel while waiting for my green card?

Here’s another edition of “Ask Sophie,” the advice column that answers immigration-related questions about working at technology companies.

“Your questions are vital to the spread of knowledge that allows people all over the world to rise above borders and pursue their dreams,” says Sophie Alcorn, a Silicon Valley immigration attorney. “Whether you’re in people ops, a founder or seeking a job in Silicon Valley, I would love to answer your questions in my next column.”

TechCrunch+ members receive access to weekly “Ask Sophie” columns; use promo code ALCORN to purchase a one- or two-year subscription for 50% off.

Dear Sophie,

I came to the United States from Tunisia to get my master’s degree and Ph.D. I recently finished my Ph.D., and I’m working for a biotech company on OPT.

I’ve been trying to get publications and significant awards to qualify for the EB-1A green card and I need to travel internationally frequently for business.

Once I apply, will I be stuck in the U.S.? If so, for how long?

— Tenacious from Tunisia

Dear Tenacious,

Thanks for reaching out! Let me first provide an overview of the green card process and then suggest an alternative to the EB-1A green card that will likely give you a faster, less risky path forward.

Rather than waiting to add to your list of accomplishments to qualify for an EB-1A extraordinary ability green card, consider applying now for the EB-2 NIW green card. Listen to my chat with my colleague Nadia Zaidi on the EB-2 NIW and what it takes to present a strong case.

Two-step process

Applying for either an EB-1A extraordinary ability green card—or an EB-2 NIW (National Interest Waiver) green card for that matter—is typically a two-step process that involves filing to U.S. Citizenship and Immigration Services (USCIS) for individuals maintaining valid nonimmigrant status (such as F-1 OPT or H-1B) in the United States:

  • Form I-140 is the green card petition where you and your immigration attorney make the case for why you qualify for the green card type for which you’re applying.
  • Form I-485 is also called the application to register permanent residence or adjust status.

Sometimes, if your “priority date” is current, you can file these two steps concurrently – more below. Whenever we file the I-485, we usually also include Form I-765, the application for employment authorization document (EAD), and Form I-131, the application for a travel document that will enable you to reenter the U.S. with “Advance Parole” after traveling abroad.

A composite image of immigration law attorney Sophie Alcorn in front of a background with a TechCrunch logo.

Image Credits: Joanna Buniak / Sophie Alcorn (opens in a new window)

If you don’t receive Advance Parole, with many types of nonimmigrant statuses-you could accidentally abandon your I-485 if you travel internationally and even be denied reentry unless you have a specific valid status like H-1B or L-1 — also, more below. When approved for the I-765 and I-131, you’ll typically receive a “combo card” that will allow you to work and travel while you wait for your physical green card to be issued after your I-140 is approved.

Processing times

You can file your EB-1A I-140 with premium processing, which means you will either get a decision or a request for evidence from USCIS within 15 days. Without premium processing, USCIS is taking nearly 2 years to process EB-1A I-140s, according to a recent USCIS Case Processing Times page. (USCIS recently expanded premium processing to EB-2 NIW I-140s as well, but the premium processing time is longer at 45 days. Without premium processing, USCIS can take as long as 8 months to process EB-2 NIW I-140s.)

Ask Sophie: How long until I can travel while waiting for my green card? by Walter Thompson originally published on TechCrunch

Have enterprise buyers finally soured on ‘bottoms-up’ tech sales?

The last decade saw many tech companies embracing product-led growth (PLG) and bottoms-up sales strategies, as opposed to traditional enterprise sales, to drive their go-to-market strategies and overall growth.

Many software startups loved (and still love!) the bottoms-up approach. What’s not to like about designing a software product to “sell itself” through viral adoption and word-of-mouth marketing? Bottoms-up and PLG both promise a faster sales cycle at a much lower cost – no more golf and pricey steak dinners on the expense account.

PLG offers other strategic advantages, too: By shortening the feedback loop between users and product teams, it allows early- and growth-stage tech companies to land and expand use of their technology inside corporate accounts, with internal champions driving the sale.

However, as enterprise-tech buyers watch expenses more closely these days, they are also tightening restrictions on self-procurement. This means founders who’ve become highly reliant on bottoms-up need a more robust enterprise-sales strategy, fast.

It’s too soon to pronounce bottoms-up dead, but it’s looking pretty moribund. And ‘pure’ PLG needs to shift rapidly too. Today’s PLG needs to inform both the product and sales teams so they can work smoothly together and clinch the next deal.

Enterprise software spending: Slower deal cycles, more scrutiny

Some clues about this changing face of corporate tech spending can be found in our latest Battery Ventures State of Cloud Software Spending Report, which queried 100 chief technology officers, chief information officers and other large tech buyers across industries ranging from financial services to healthcare to manufacturing.

Collectively, the survey respondents represent $30 billion in annual technology spending. Our respondents include a healthy sampling of enterprises that consume software through a bottoms-up / PLG motion, as the slide below indicates.

Bottoms-up adoption buying patterns chart

Bottoms-up adoption buying patterns Image: Battery Q1 2023 Cloud Software Spending Survey

While almost half of our respondents (46%) expect to increase their total technology budgets in 2023, enterprises are getting more conservative and shifting priorities. Many plan to standardize spending, consolidating vendors to save money and optimizing SaaS licensing. Enterprises are re-examining pricing models to determine if consumption- or seat-based pricing makes the most sense, given how the software is used, and choosing vendors partly on that basis.

Today’s PLG needs to inform both the product and sales teams so they can work smoothly together and clinch the next deal.

The sometimes-bureaucratic governance systems within enterprises may function even more slowly in the coming months, as organizations across industries work to become more efficient and to increase spending oversight.

The slide below quantifies our findings that bottoms-up and PLG adoption are slowing down. One example: Only 46% of survey respondents now allow individual engineers to install tools in a “sandbox dev” environment – that’s down from 76% since our last survey in September 2022. The drop for engineer-selected tools deployed into production is significant too: Now, only 11% of enterprises allow this to happen, down 27% from September 2022.

Have enterprise buyers finally soured on ‘bottoms-up’ tech sales? by Walter Thompson originally published on TechCrunch

Startups should absolutely work with governments to support defense projects

In these times of heightened tensions and global volatility, I believe startups can play a critical role in our defense, space and national security ecosystem by bringing the very latest innovation to public institutions, some of whom lag startlingly far behind.

Startups and active investors in the sector are uniquely positioned to support the defense efforts of the West and the mission to keep our societies safe. Let’s not mince our words: Right now, we are already locked in hybrid warfare with Russia, a nuclear-armed superpower, while tensions with another, China, simmer just below the surface. Despotic regimes threaten our values and way of life, and few would predict that is set to change anytime soon.

Yet despite all this, much of the technology and venture capital industry has shown little inclination to engage with the defense establishment. Prior to Russia’s invasion of Ukraine, over dinner with friends and co-workers, you risked triggering anguished disapproval (and far worse), by stating that you believe startups should work with the likes of the Pentagon, NATO and Western governments in general. Today you largely garner a very different response: murmurs of assent.

The very latest, most powerful technologies offer an edge to those who create and possess them – as we have seen in some of the Western firepower deployed in Ukraine, alongside Ukrainian battlefield innovation. The brutal truth is that in resting on our laurels, the West has allowed those who wish us harm to catch up, and in some instances, surpass our capabilities – and the tech industry is partially to blame.

For example, in 2018, thousands of Googlers signed a letter to their boss, Sundar Pichai, declaring that “Google should not be in the business of war.” Specifically, they were protesting their employer’s involvement in a U.S. Department of Defense initiative, Project Maven, which was using Google AI tools to analyze military drone footage. “Building this technology to assist the US Government in military surveillance – and potentially lethal outcomes – is not acceptable,” they wrote.

This uncompromising and combative stance ultimately led to the decision by Google’s management not to renew its lucrative Maven contract, and soon afterwards it also withdrew from contention for the Pentagon’s cloud computing contract known as the Joint Enterprise Defense Infrastructure cloud (JEDI) – reportedly worth $10B over ten years.

Google employees were far from alone in confronting their bosses over perceived collaboration with the Trump administration, which was widely reviled in progressive-leaning tech circles. Around the same time, Microsoft employees called on CEO Satya Nadella to stop working with Immigration and Customs Enforcement (ICE), Amazon workers protested their company’s development of surveillance tech, while Salesforce employees signed a petition calling for its leaders to “re-examine” the company’s contract with US Customs and Border Protection (CBP)”.

What a difference a few years make. Fast forward to 2022 and a combination of COVID-19 and its legacy, stressed and unstable global supply chains, Russia’s war with Ukraine, the first threat of food insecurity in the U.S. or in the West since WW2, and increased tensions with China have prompted a sharp rethink from much of the tech and venture capital industry on its responsibilities towards government.

Today, in marked contrast to most other verticals, investment in aerospace and defense startups is surging. Between January and October 2022, according to PitchBook, VCs invested $7B in 114 aerospace and defense tech deals, which placed the sector on a trajectory to surpass 2021’s record $7.6B total. In 2018, VCs invested just $1.4B in those industries. (A part of this, notes PitchBook, may be due to the fact defense and aerospace are rather more recession-proof than, say, consumer or enterprise products.)

I’m immensely proud that Techstars is one of the most active investors in this category. With almost about 100 investments overall in aerospace, defense and space tech, we are one of only three VCs to have participated in more than 20 space startup deals since 2000, while 25% of the firms selected for 2022 NASA Small Business Innovation Research contracts were Techstars-backed companies. One of our portfolio companies, Slingshot Aerospace recently closed a $40.8M Series A-2 funding round. Its clients include the U.S. Air Force, the U.S. Space Force, and NASA.

Yet there is much ground to make up. A blog post from defense tech company Anduril that was cited in The Information put it this way:

“Despite spending more money than ever on defense, our military technology stays the same. There is more AI in a Tesla than in any U.S. military vehicle; better computer vision in your Snapchat app than in any system the Department of Defense owns; and, until 2019, the United States’ nuclear arsenal operated off floppy disks.”

Recent relative calm convinced us, erroneously, that we were living in a stable, post-conflict world where threats to our way of life and maneuvers by bad actors could somehow be ignored or wished away. In this scenario, much of the Valley could persuade itself that it could refuse to build products that are designed to harm and kill (even when that is not their overt aim). Such stances now seem naive and idealistic at best; posturing at worst.

Back in 2018, the hashtag #TechWontBuildIt was used to protest Big Tech’s government contracts. Not only must we build, but there is little time to waste.

Startups should absolutely work with governments to support defense projects by Walter Thompson originally published on TechCrunch

Venture leasing: The unsung hero for hardware startups struggling to raise capital

Global funding in February 2023 fell 63% from the previous year, with only $18 billion in investments. For robotics startups, it didn’t get any better: 2022 was the second worst year for funding in the past five years, and 2023 numbers are heading in the same direction.

This behavior from investors in the face of uncertainty and austerity is justified, especially when hardware companies burn cash faster than SaaS does. So, founders of robotics startups and other equipment-heavy businesses are left wondering whether they’ll be able to close their next funding round or if they’ll have to resort to acquisition.

But there’s a happy medium between costly debt loans and VC funding that works particularly well for hardware startups: venture leasing.

There’s a happy medium between costly debt loans and VC funding that works particularly well for hardware startups: venture leasing.

Hardware startups are better suited than software companies for this kind of financing because they have tangible assets, balancing the high-risk nature of the industry with a liability.

As the CEO of a robotics startup that recently got a $10 million venture leasing deal, I’ll outline the advantages of this type of agreement for hardware companies and how to land a win-win deal when closing a round isn’t an option.

Why are venture leasing deals compatible with hardware startups?

As opposed to a few developers here and there in SaaS, hardware companies require intensive Research and Development (R&D), capital expenditures (CapEx), and manual labor to manufacture their products. So, it’s no surprise that the latter’s cash burn rate is more than two and a half times higher than the former.

Hardware startups are constantly trying to avoid dilution when raising funds due to their capital-heavy operations. Therefore, venture leasing can be a relief for founders as it gives them the money they need up-front without compromising their company’s equity.

Rather than taking a piece of a company’s shares or equity, venture leasing takes the business’ physical assets as a liability to secure the loan—making it easier for startups to obtain it. It’s also a lower-risk investment and allows the company to keep 100% of their ownership.

These deals work like a car lease, where the bank technically owns the car (the manufactured product) while the startup pays a monthly installment to keep it and, in most cases, operate it however they want. Lenders are often more flexible with their agreement terms than other funders.

Beyond avoiding dilution, leasing theoretically takes a company’s equipment from its capital assets, allowing for more efficient margins in terms of profitability.

The added plus: Boosting Equipment-as-a-Service

With venture leasing, a startup can lease assets such as equipment, real estate, or even intellectual property from a specialized leasing company. They receive the assets in return for a monthly lease payment over a fixed term, typically shorter than traditional financing.

Venture leasing: The unsung hero for hardware startups struggling to raise capital by Walter Thompson originally published on TechCrunch

Beyond networking: What immigrant founders in the UK want from VC office hours

After facilitating more than 300 office hours for immigrant entrepreneurs entering the UK market, what have we learned about who these founders are and the challenges they face?

Introductions for a soft landing can be crucial, but the real value lies in gleaning substantive feedback from experienced investors.

It’s about what your network can teach you and help you obtain (e.g., an opportunity to pitch), not just the number of connections you have.

We ran the first version of the Blue Lake International Office Hours in 2022, which showed initial evidence that the trend of VCs offering office hours can deliver real value, especially to immigrant founders entering the UK ecosystem.

In the second edition, which ran in March 2023, we wanted to continue making helpful introductions, but we also wanted to more systematically understand just what, exactly, was so useful about office hours.

With this aim, we developed our application and post-meeting feedback forms to better identify two things: (1) ahead of the meeting, what do founders say they most want to get out of the office hours with investors and (2) after the meeting, what do they say they appreciate most? Said differently: do founders ultimately value the aspects of the meeting as they think they will?

First, who are the founders who participated in the second round? The 125 applicants to office hours from February 2023 came from 39 countries across six continents and varied countries in terms of language, economic development and other factors.

Countries of origin included places as diverse as Australia and Azerbaijan,as well as Ghana and Germany. The countries with the greatest numbers of applicants were Ukraine (33), India (11), and Turkey (10). There was also a mix of 12 different primary sectors identified by applicants. “other” was the largest single category (22), followed by fintech (18) marketplace (17), cleantech (15) and deep tech (14).

Here’s an illustration of just how varied the primary sector mix was:

VC office hours founder topic preferences

Image Credits: Blue Lake VC

Gender-wise, applicants were predominantly male, with 96 applicants identifying as male, 28 as female, and one as non-binary.

Back to our questions about what these diverse founders and what they say they want from office hours. Forty-five participants completed both the applications and post-office hours feedback form; we analyzed the answers for this subset to compare what they said they wanted to glean beforehand with what they covered and valued afterwards.

In the application form, when asked about the aspects they would find most valuable, 60% answered “introductions/network.” Fundraising strategy received the second highest number of responses, with just 13%. Sales and marketing, mentorship, and team all received a small number of votes.

Beyond networking: What immigrant founders in the UK want from VC office hours by Walter Thompson originally published on TechCrunch