“Most of the startups I give advice to about how to raise venture capital shouldn’t be raising venture capital,” an investor recently told me. While the idea that every startup isn’t venture-backable might run counter to the narrative to the barrage of funding news each week, I think it’s important to double click on the topic. Plus, it keeps coming up, off the record, on phone calls with investors!

As venture grows as an asset class, the access to capital has broadened from a dollar perspective, but I do think the difficulties that remain is an important dynamic to call out (and something no one talks about during an upmarket). Beyond the fact that only a small subset of startups truly can pull off scaling to the point of venture-level returns, it is still hard for even qualified founders to raise venture capital. Venture capital is still a heavily white, male-led industry, and as a result contains bias that disproportionately limits access for underrepresented founders.

Eniac founding partner Hadley Harris applied this dynamic to the current market boom in a recent tweet: A lot of people are misunderstanding this VC funding market. More money is flowing into the market but the increase is not evenly distributed. The market believes winners can be much bigger but not necessary that there will be more winners. It’s still very hard for most to raise a VC.

To say otherwise is to gaslight the early-stage or first-time founders that have spent months and months trying to raise their first institutional dollars and failed. So ask yourself: Seed rounds have indeed grown bigger, but for who? What comes at the cost of the $30 million seed round? Are the founders that can raise overnight from diverse backgrounds? Are investors backing first-time founders as much as they are backing second- or third-time entrepreneurs?

The answers might leave you debating about the boundaries, and limitations, of the upcoming hot-deal summer.

A few weeks ago, I wrote about the disconnect between due diligence and fundraising right now. Now we’ve moved onto the disconnect, and bifurcation, within first-check fundraising itself. There is so much more we can get into about the fallacy of “democratization” in venture capital, from who gets to start a rolling fund to the lack of assurance within equity crowdfunding campaigns.

We’ll get through it all together, and in the meantime make sure to follow me on Twitter @nmasc_ for more hot takes throughout the week.

In the rest of this newsletter, we will talk about fintech politics, the Affirm model with a twist, and sneakers-as-a-service.

Ex-Coinbase talks politics

The inimitable Mary Ann Azevedo has been dominating the fintech beat for us, covering everything from the latest Uruguayan unicorn to Acorn’s scoop of a debt management startup. But the story I want to focus on this week is her interview with ex-Coinbase counsel & former Treasury official, Brian Brooks.

Here’s what to know: Coinbase CEO Brian Armstrong notoriously released a memo last year denouncing political activism at work, calling it a distraction. In this exclusive interview, Brooks spoke about how blockchain is the answer to financial inclusion, and argued why politics needs to be taken out of tech.

We don’t want bank CEOs making those decisions for us as a society, in terms of who they choose to lend money to, or not. We need to take the politics out of tech. All of us do a lot of different things, and we have no idea on a given day, whether what we’re doing is popular with our neighbors or popular with our bank president or not. I don’t want the fact that I sometimes feel Republican to be a reason why my local bank president can deny me a mortgage.

The Affirm for X model

While Affirm may have popularized the “buy now, pay later” model, the consumer-friendly business strategy still has room to be niched down into specific subsectors. I ran into one such startup when covering Plaid’s inaugural cohort of startups in its accelerator program.

Here’s what to know: Walnut is a new seed-stage startup that is a point-of-sale loan company with a healthcare twist. Unlike Affirm, it doesn’t make money off of fees charged to consumers.

Everything you could ever want to know about StockX

In our latest EC-1, reporter Rae Witte has covered a startup that leads one of the most complex and culturally relevant marketplaces in the world: sneakers.

Here’s what to know: StockX, in her words, has built a stock market of hype, and her series goes into its origin story, authentication processes and a market map.

Around TechCrunch

Found, a new podcast joining the TechCrunch network, has officially launched! The Equity team got a behind-the-scenes look at what triggered the new podcast, the first guests and goals of the show. Make sure to tune into the first episode.

Also, if you run into any paywalls while browsing today’s newsletter, make sure to use discount code STARTUPSWEEKLY to get 25% off an annual or two-year Extra Crunch subscription.

Across the week

Seen on TechCrunch

Okta launches a new free developer plan

New Jersey announces $10M seed fund aimed at Black and Latinx founders

Education nonprofit Edraak ignored a student data leak for two months

6 VCs talk the future of Austin’s exploding startup ecosystem

Dear Sophie: Help! My H-1B wasn’t chosen!

Seen on Extra Crunch

5 machine learning essentials nontechnical leaders need to understand

How we dodged risks and raised millions for our open-source machine language startup

Giving EV batteries a second life for sustainability and profit

And that’s a wrap! Thanks for making it this far, and now I dare you to go make the most out of the rest of your day. And by make the most, I mean listen to Taylor’s Version.

Warmly,

N

During a seed funding round, a founder needs to convince a venture capital investor on a vision. But during a Series A fundraise, napkin-stage ideas don’t make the cut — a founder needs product progress, numbers, and revenue (or at least a plan to eventually generate some).

In many ways, the stakes are higher for a Series A — and Bucky Moore, a partner at Kleiner Perkins, joined TechCrunch Early Stage last week to give founders tactical advice on the process of raising one.

Moore spoke about storytelling over semantics, pricing, and where his firm sees itself “raising the bar” for startups.

Here are a few key points; a full video and a transcript of the entire conversation are linked at the bottom.

Explain to investors why you are raising now

More companies will raise seed rounds than Series A rounds, simply due to the fact that many startups fail, and venture only makes sense for a small fraction of businesses out there. Every check is a new cycle of convincing and proving that you, as a startup, will have venture-scale returns. Moore explained that startups looking to move to their next round need to explain to investors why now is their moment.

The way I think about “why now” is [that] it is an opportunity for you as a founder to convey a unique insight and understanding of your market opportunity, the history of the space that you’re in, why companies have succeeded or failed in that space, historically speaking, and what are the known challenges from a go-to-market perspective; what headwinds will you be up against at a macro level. These are all things that I think people like me get really excited about when hearing unique insight from founders, because it suggests that they’ve really studied their market opportunity, and they understand it. (Timestamp: 2:19)

While visual ‘no code‘ tools are helping businesses get more out of computing without the need for armies of in-house techies to configure software on behalf of other staff, access to the most powerful tech tools — at the ‘deep tech’ AI coal face — still requires some expert help (and/or costly in-house expertise).

This is where bootstrapping French startup, NLPCloud.io, is plying a trade in MLOps/AIOps — or ‘compute platform as a service’ (being as it runs the queries on its own servers) — with a focus on natural language processing (NLP), as its name suggests.

Developments in artificial intelligence have, in recent years, led to impressive advances in the field of NLP — a technology that can help businesses scale their capacity to intelligently grapple with all sorts of communications by automating tasks like Named Entity Recognition, sentiment-analysis, text classification, summarization, question answering, and Part-Of-Speech tagging, freeing up (human) staff to focus on more complex/nuanced work. (Although it’s worth emphasizing that the bulk of NLP research has focused on the English language — meaning that’s where this tech is most mature; so associated AI advances are not universally distributed.)

Production ready (pre-trained) NLP models for English are readily available ‘out of the box’. There are also dedicated open source frameworks offering help with training models. But businesses wanting to tap into NLP still need to have the DevOps resource and chops to implement NLP models.

NLPCloud.io is catering to businesses that don’t feel up to the implementation challenge themselves — offering “production-ready NLP API” with the promise of “no DevOps required”.

Its API is based on Hugging Face and spaCy open-source models. Customers can either choose to use ready-to-use pre-trained models (it selects the “best” open source models; it does not build its own); or they can upload custom models developed internally by their own data scientists — which it says is a point of differentiation vs SaaS services such as Google Natural Language (which uses Google’s ML models) or Amazon Comprehend and Monkey Learn.

NLPCloud.io says it wants to democratize NLP by helping developers and data scientists deliver these projects “in no time and at a fair price”. (It has a tiered pricing model based on requests per minute, which starts at $39pm and ranges up to $1,199pm, at the enterprise end, for one custom model running on a GPU. It does also offer a free tier so users can test models at low request velocity without incurring a charge.)

“The idea came from the fact that, as a software engineer, I saw many AI projects fail because of the deployment to production phase,” says sole founder and CTO Julien Salinas. “Companies often focus on building accurate and fast AI models but today more and more excellent open-source models are available and are doing an excellent job… so the toughest challenge now is being able to efficiently use these models in production. It takes AI skills, DevOps skills, programming skill… which is why it’s a challenge for so many companies, and which is why I decided to launch NLPCloud.io.”

The platform launched in January 2021 and now has around 500 users, including 30 who are paying for the service. While the startup, which is based in Grenoble, in the French Alps, is a team of three for now, plus a couple of independent contractors. (Salinas says he plans to hire five people by the end of the year.)

“Most of our users are tech startups but we also start having a couple of bigger companies,” he tells TechCrunch. “The biggest demand I’m seeing is both from software engineers and data scientists. Sometimes it’s from teams who have data science skills but don’t have DevOps skills (or don’t want to spend time on this). Sometimes it’s from tech teams who want to leverage NLP out-of-the-box without hiring a whole data science team.”

“We have very diverse customers, from solo startup founders to bigger companies like BBVA, Mintel, Senuto… in all sorts of sectors (banking, public relations, market research),” he adds.

Use cases of its customers include lead generation from unstructured text (such as web pages), via named entities extraction; and sorting support tickets based on urgency by conducting sentiment analysis.

Content marketers are also using its platform for headline generation (via summarization). While text classification capabilities are being used for economic intelligence and financial data extraction, per Salinas.

He says his own experience as a CTO and software engineer working on NLP projects at a number of tech companies led him to spot an opportunity in the challenge of AI implementation.

“I realized that it was quite easy to build acceptable NLP models thanks to great open-source frameworks like spaCy and Hugging Face Transformers but then I found it quite hard to use these models in production,” he explains. “It takes programming skills in order to develop an API, strong DevOps skills in order to build a robust and fast infrastructure to serve NLP models (AI models in general consume a lot of resources), and also data science skills of course.

“I tried to look for ready-to-use cloud solutions in order to save weeks of work but I couldn’t find anything satisfactory. My intuition was that such a platform would help tech teams save a lot of time, sometimes months of work for the teams who don’t have strong DevOps profiles.”

“NLP has been around for decades but until recently it took whole teams of data scientists to build acceptable NLP models. For a couple of years, we’ve made amazing progress in terms of accuracy and speed of the NLP models. More and more experts who have been working in the NLP field for decades agree that NLP is becoming a ‘commodity’,” he goes on. “Frameworks like spaCy make it extremely simple for developers to leverage NLP models without having advanced data science knowledge. And Hugging Face’s open-source repository for NLP models is also a great step in this direction.

“But having these models run in production is still hard, and maybe even harder than before as these brand new models are very demanding in terms of resources.”

The models NLPCloud.io offers are picked for performance — where “best” means it has “the best compromise between accuracy and speed”. Salinas also says they are paying mind to context, given NLP can be used for diverse user cases — hence proposing number of models so as to be able to adapt to a given use.

“Initially we started with models dedicated to entities extraction only but most of our first customers also asked for other use cases too, so we started adding other models,” he notes, adding that they will continue to add more models from the two chosen frameworks — “in order to cover more use cases, and more languages”.

SpaCy and Hugging Face, meanwhile, were chosen to be the source for the models offered via its API based on their track record as companies, the NLP libraries they offer and their focus on production-ready framework — with the combination allowing NLPCloud.io to offer a selection of models that are fast and accurate, working within the bounds of respective trade-offs, according to Salinas.

“SpaCy is developed by a solid company in Germany called Explosion.ai. This library has become one of the most used NLP libraries among companies who want to leverage NLP in production ‘for real’ (as opposed to academic research only). The reason is that it is very fast, has great accuracy in most scenarios, and is an opinionated” framework which makes it very simple to use by non-data scientists (the tradeoff is that it gives less customization possibilities),” he says.

“Hugging Face is an even more solid company that recently raised $40M for a good reason: They created a disruptive NLP library called ‘transformers’ that improves a lot the accuracy of NLP models (the tradeoff is that it is very resource intensive though). It gives the opportunity to cover more use cases like sentiment analysis, classification, summarization… In addition to that, they created an open-source repository where it is easy to select the best model you need for your use case.”

While AI is advancing at a clip within certain tracks — such as NLP for English — there are still caveats and potential pitfalls attached to automating language processing and analysis, with the risk of getting stuff wrong or worse. AI models trained on human-generated data have, for example, been shown reflecting embedded biases and prejudices of the people who produced the underlying data.

Salinas agrees NLP can sometimes face “concerning bias issues”, such as racism and misogyny. But he expresses confidence in the models they’ve selected.

“Most of the time it seems [bias in NLP] is due to the underlying data used to trained the models. It shows we should be more careful about the origin of this data,” he says. “In my opinion the best solution in order to mitigate this is that the community of NLP users should actively report something inappropriate when using a specific model so that this model can be paused and fixed.”

“Even if we doubt that such a bias exists in the models we’re proposing, we do encourage our users to report such problems to us so we can take measures,” he adds.

French startup Vybe has raised a $2.9 million funding round (€2.4 million) to build a challenger bank for teens. The company is currently testing its product with a soft launch. Users get a Mastercard payment card paired with an e-wallet.

Each Vybe account comes with its own IBAN so that users can send and receive money. If you want to open an account and you are younger than 18 years old, you have to go through the KYC process (know your identity) with your parent.

As for parents, they can set up some limits on card payments or even block the card. Parents also can view transactions. The startup plans to generate revenue from interchange fees as well as partner with brands and offer a reward system.

While Vybe isn’t technically live, the company has attracted 375,000 downloads. Overall, 260,000 teens have preordered a card already. Thousands of cards have been delivered and the first metrics are encouraging. Early adopters tend to use their card once every two days.

Today’s fund is a round extension from existing investors. Investors include Ronan Le Moal (the former CEO of Crédit Mutuel Arkéa), Kick Club and Manoel Amorim.

Banking products for teenagers are a lucrative segment. In France, there are several companies trying to position themselves on this segment, such as Kard, PixPay and Xaalys. Most of these companies charge a subscription fee to access the service.

Other fintech companies that aren’t specifically targeting young people could also work well with teenagers. For instance, young users can open a Revolut Junior or Lydia account and receive money from their parents.

In the U.S., startups offering debit cards for children are about to reach unicorn status. As The Information’s Kate Clark reported, Greenlight, Current and Step are all raising new funding rounds with a valuation between $1 billion and $2 billion.

The office shut-down at the start of the COVID-19 pandemic last year spurred huge investment in digital transformation and a wave of tech companies helping with that, but there were some distinct losers in the shift, too — specifically those whose business models were predicated on serving the very offices that disappeared overnight. Today, one of the companies that had to make an immediate pivot to keep itself afloat is announcing a round of funding, after finding itself not just growing at a clip, but making a profit, as well.

SnackMagic, a build-your-own snack box service, has raised $15 million in a Series A round of funding led by Craft Ventures, with Luxor Capital also participating.

(Both investors have an interesting track record in the food-on-demand space: Most recently, Luxor co-led a $528 million round in Glovo in Spain, while Craft backs/has backed the likes of Cloud Kitchens, Postmates and many more.)

The funding comes on the back of a strong year for the company, which hit a $20 million revenue run rate in eight months and turned profitable in December 2020.

Founder and CEO Shaunak Amin said in an interview that the plan will be to use the funding both to continue growing SnackMagic’s existing business, as well as extend into other kinds of gifting categories. Currently, you can ship snacks anywhere in the world, but the customizable boxes — recipients are gifted an amount that they can spend, and they choose what they want in the box themselves from SnackMagic’s menu, or one that a business has created and branded as a subset of that — are only available in locations in North America, serviced by SnackMagic’s primary warehouse. Other locations are given options of pre-packed boxes of snacks right now, but the plan is to slowly extend its pick-and-mix model to more geographies, starting with the U.K.

Alongside this, the company plans to continue widening the categories of items that people can gift each other beyond chocolates, chips, hot sauces and other fun food items, into areas like alcohol, meal kits and nonfood items. There’s also scope for expanding to more use cases into areas like corporate gifting, marketing and consumer services, as well as analytics coming out of its sales.

Amin calls the data that SnackMagic is amassing about customer interest in different brands and products “the hidden gem” of the platform.

“It’s one of the most interesting things,” he said. Brands that want to add their items to the wider pool of products — which today numbers between 700 and 800 items — also get access to a dashboard where they monitor what’s selling, how much stock is left of their own items, and so on. “One thing that is very opaque [in the CPG world] is good data.”

For many of the bigger companies that lack their own direct sales channels, it’s a significantly richer data set than what they typically get from selling items in the average brick and mortar store, or from a bigger online retailer like Amazon. “All these bigger brands like Pepsi and Kellogg not only want to know this about their own products more but also about the brands they are trying to buy,” Amin said. Several of them, he added, have approached his company to partner and invest, so I guess we should watch this space.

SnackMagic’s success comes from a somewhat unintended, unlikely beginning, and it’s a testament to the power of compelling, yet extensible technology that can be scaled and repurposed if necessary. In its case, there is personalization technology, logistics management, product inventory and accounting, and lots of data analytics involved.

The company started out as Stadium, a lunch delivery service in New York City that was leveraging the fact that when co-workers ordered lunch or dinner together for the office — say around a team-building event or a late-night working session, or just for a regular work day — oftentimes they found that people all hankered for different things to eat.

In many cases, people typically make separate orders for the different items, but that also means if you are ordering to all eat together, things would not arrive at the same time; if it’s being expensed, it’s more complicated on that front too; and if you’re thinking about carbon footprints, it might also mean a lot less efficiency on that front too.

Stadium’s solution was a platform that provided access to multiple restaurants’ menus, and people could pick from all of them for a single order. The business had been operating for six years and was really starting to take off.

“We were quite well known in the city, and we had plans to expand, and we were on track for March 2020 being our best month ever,” Amin said. Then, COVID-19 hit. “There was no one left in the office,” he said. Revenue disappeared overnight, since the idea of delivering many items to one place instantly stopped being a need.

Amin said that they took a look at the platform they had built to pick many options (and many different costs, and the accounting that came with that) and thought about how to use that for a different end. It turned out that even with people working remotely, companies wanted to give props to their workers, either just to say hello and thanks, or around a specific team event, in the form of food and treats — all the more so since the supply of snacks you typically come across in so many office canteens and kitchens were no longer there for workers to tap.

It’s interesting, but perhaps also unsurprising, that one of the by-products of our new way of working has been the rise of more services that cater (no pun intended) to people working in more decentralised ways, and that companies exploring how to improve rewarding people in those environments are also seeing a bump.

Just yesterday, we wrote about a company called Alyce raising $30 million for its corporate gifting platform that is also based on personalization — using AI to help understand the interests of the recipient to make better choices of items that a person might want to receive.

Alyce is taking a somewhat different approach than SnackMagic: it’s not holding any products itself, and there is no warehouse but rather a platform that links buyers with those providing products. And Alyce’s initial audience is different, too: instead of internal employees (the first, but not final, focus for SnackMagic) it is targeting corporate gifting, or presents that sales and marketing people might send to prospects or current clients as a please and thank you gesture.

But you can also see how and where the two might meet in the middle — and compete not just with each other, but the many other online retailers, Amazon and otherwise, plus the consumer goods companies themselves looking for ways of diversifying business by extending beyond the B2C channel.

“We don’t worry about Amazon. We just get better,” Amin said when I asked him about whether he worried that SnackMagic was too easy to replicate. “It might be tough anyway,” he added, since “others might have the snacks but picking and packing and doing individual customization is very different from regular e-commerce. It’s really more like scalable gifting.”

Investors are impressed with the quick turnaround and identification of a market opportunity, and how it quickly retooled its tech to make it fit for purpose.

“SnackMagic’s immediate success was due to an excellent combination of timing, innovative thinking and world-class execution,” said Bryan Rosenblatt, principal investor at Craft Ventures, in a statement. “As companies embrace the future of a flexible workplace, SnackMagic is not just a snack box delivery platform but a company culture builder.”

Corporate catering company Elior has acquired French startup Nestor for an undisclosed amount. Nestor originally started with a simple idea to differentiate itself from food delivery giants, such as Deliveroo, Uber Eats and others.

Every day, the startup offered a single menu for lunch. If you liked what was on the menu, you could order and get it delivered 10 to 20 minutes later. By offering a single menu, a delivery person could deliver several clients in a single ride. Similarly, by managing its own kitchen, Nestor could improve its margins as it didn’t have to pay third-party restaurants.

Since I first covered Nestor in 2016, the company has been capital efficient and mostly focused on this unique product offering. Elior says that Nestor managed to reach 10,000 meals per week.

Over the past few months, Nestor has tried to launch new offers. For instance, companies can switch to Nestor for their canteens. The startup delivers meals in fridges directly. It reminds me of Foodles, another French startup focused on canteen-like services.

Nestor can also deliver individually packed lunches in case you are spending the day with some clients for a big meeting. Popchef has also pivoted to focus more on that segment.

Following the acquisition, Nestor is going to focus more and more on the B2B market. While Elior is working with big companies in glass towers, it has been quite hard to convince small and medium companies to open a canteen in the office.

The sales pitch could be summed up in two sentences. Nestor clients don’t need to have their own kitchen as everything is prepared in advance. And employees don’t have to browse Deliveroo at lunch time to find something that isn’t a burger or a pizza.

Iyuno-SDI Group, a provider of translated subtitles and other media localization services, announced today it has raised $160 million in funding from SoftBank Vision Fund 2. The company said this makes the fund one of its largest shareholders.

Iyuno-SDI Group was formed after Iyuno Media Group completed its acquisition of SDI Media last month. In a recent interview with TechCrunch, Iyuno-SDI Group chief executive officer David Lee, who launched Iyuno in 2002 while he was an undergraduate in Seoul, described how the company’s proprietary cloud-based enterprise resource planning software allows it to perform localization services — including subtitles, dubbing and accessibility features — at scale.

Iyuno also built its own neural machine translation engines, trained on data from specific entertainment genres, to help its human translators work more quickly. The company’s clients have included Netflix, Apple iTunes, DreamWorks, HBO and Entertainment One.

Now that its merger is complete, Iyuno-SDI Group operates a combined 67 offices in 34 countries, and is able to perform localization services in more than 100 languages.

SoftBank Group first invested in Iyuno Media Group through SoftBank Ventures Asia, its venture capital arm, in 2018. SoftBank Vision Fund 2 will join Lee and investors Altor, Shamrock Capital Advisors and SoftBank Ventures Asia Corporation on Iyuno-SDI Group’s board of directors.

Product-led growth is all the rage in the Valley these days, and we had two leading thinkers discuss how to incorporate it into a startup at TechCrunch Early Stage 2021. Tope Awotona is the CEO and founder of Calendly, which bootstrapped for much of its existence before raising $350 million at a $3 billion valuation from OpenView and Iconiq. And on the other side of that table and this interview sat Blake Bartlett, a partner at OpenView who has been leading enterprise deals based around the principles of efficient growth.

In this interview, the two talk about bootstrapping and product-led growth, expanding internationally, when to bootstrap and when to fundraise, and how VCs approach a profitable company (carefully, and with a big stick). Oh, and how to spend $350 million.

Quotes have been edited and condensed for quality.

Bootstrapping is directly tied to product-led growth

Product-led growth is all about efficiency — spending all of a startup’s capital and time on perfecting its product to capture new users and help the most fervent customers advocate for the product with others or perhaps the managers approving their expenses. That’s directly related to bootstrapping, since by evading VC investment, a startup has to be much more tied to customers in the first place.

Tope Awotona:

With no marketing at all, Calendly began to take off. So the initial users were in higher education, and very quickly we moved to the commercial sector. And all of that was because of the virality of the product. Seeing that, we just began to invest more into virality. So the combination of self-serve, which is incredibly capital efficient, because you don’t need all of these sales people, and also the virality, instead of spending a bunch of dollars on advertising, you can really rely on the virality of the product and rely on the network of the users to really propagate and to enable distribution, just those are the two things that really allowed us to be successful. (Timestamp: 7:49)

We later discussed how the extreme focus on users can drive efficiency through product-led growth.

Blake Bartlett:

It’s the product and the distribution model, and they need to be tightly aligned. Tope spoke to some of this, but I think first and foremost, even outside of metrics, it’s just how is the business built? And on the product front, the product is built, the jobs to be done, so to speak, are oriented towards the actual user of the product, not their boss. SaaS historically was built for the boss because the boss owns the the budget for that department. So if you’re building a sales tool, build for the VP of Sales, and then hopefully the AEs will, you know, go along with it. But now with product-led growth, you’re actually building for that user. … Eventually, you can build the things on top that the boss cares about like the admin panel, and the KPIs and all that kind of stuff. (Timestamp: 29:35)

Product-led growth and international expansion

Realworld has a big vision — founder and CEO Genevieve Ryan Bellaire told me her goal is “simplifying adulthood.” And the New York startup has raised $3.4 million in seed funding to make it happen.

Apparently that’s something Bellaire struggled with herself in her early twenties. Despite being a lawyer with an MBA, she said she found herself “just totally unprepared for all these real-world things,” whether that was figuring out housing or heath insurance — something I (a non-lawyer, non-MBA) can definitely relate to.

“There’s tons of content out there out there that can tell you to fill out this form to sign up for a credit card, but you don’t know what you don’t know,” she said. “There’s not one place that defines adulthood.”

At the same time, there are online services that can make aspects of adulthood easier — whether that’s Lemonade for insurance, Betterment for investing or Zocdoc for doctor’s appointments. But again, finding these services and just knowing that you should use them can be a challenge, so Bellaire said Realworld is meant to serve as the “single point of entry.”

To do that, the startup has created more than 90 step-by-step playbooks, covering everything from budgeting to moving to salary negotiation. Bellaire said these are designed for members of Gen Z who are just leaving college and entering the workforce.

Of course, even if you focus on a specific age group, different twentysomethings will have different backgrounds, income levels and challenges. Bellaire said the playbooks will customize their instructions based on a user’s specific goals and circumstances, but she also argued that Realworld’s “starter pack” of 15 playbooks covers things that every adult will need to deal with in some form, such as creating budgets, finding an apartment and understanding income taxes.

The startup plans to release its first mobile app next month, and its goal is to become what Bellaire described as a “platform, marketplace and community.” The playbooks are a big piece of the platform, and eventually, Realworld could also include a marketplace for services that will help you accomplish those adulthood goals, as well as a community where users share their knowledge and advice.

Realworld initially charged for access to its playbooks, but they’re now available for free. Instead, Bellaire said the company could charge a subscription fee for additional features and for “concierge-oriented support.”

“This is one of those problems where if you get it right, you can make a huge impact, but you can also have huge financial success,” she added.

It sounds like investors agree. Realworld had previously raised $1.1 million, and this new seed round was led by Fitz Gate Ventures, with participation from Bezos Expeditions (Jeff Bezos’ personal investment firm), Knightsgate Ventures, The Helm, Great Oaks VC, Copper Wire Ventures, AmplifyHer Ventures, Underdog Labs, Human Ventures and Techstars.

Amplifyher partner Meghan Cross Breeden noted that Realworld could “corner the market on life milestones,” not just for Gen Z right now, but for “every future milestone … in the long-haul of adulthood, from buying a home to caring for a parent.”

Microsoft's Relic Entertainment revealed new civilizations, graphics, and campaigns for Age of Empires IV.Read More

MIT researchers say what happened to Timnit Gebru at Google is part of a centuries-old pattern that people in AI must recognize.Read More

The death of Stadia's in-house studios could help Google establish a new paradigm when it comes to cloud gaming.Read More

Microsoft released CyberBattleSim, an open source toolkit to let researchers create attack simulations and use AI to develop network defenses.Read More

Resurrected isn't just bringing back a classic. It could also help revive Blizzard's retro gaming credentials.Read More

Hideo Kojima is making a new game, and he's in talks with Microsoft about publishing the project for Xbox and Game Pass.Read More

Continual learning might offer a path toward more human-like AI, the members of nonprofit research group ContinualAI believe.Read More

IBM is releasing Qiskit Machine Learning, a new application module that's part of its open source quantum software.Read More

A series of recent works by AI researchers found serious flaws in datasets and the outputs of large language models like OpenAI's GPT-3.Read More

Tableau is making a case for business science as new discipline for putting control over AI models directly in the hands of end users.Read More

Practically every film production these days needs some kind of visual effects work, but independent creators often lack the cash or expertise to get that top-shelf CG. Wonder Dynamics, founded by VFX engineer Nikola Todorovic and actor Tye Sheridan, aims to use AI to make some of these processes more accessible for filmmakers with budgets on the tight side, and they’ve just raised $2.5 million to make it happen.

The company has its origins in 2017, after Sheridan and Todorovic met on the set of Rodrigo Garcia’s film Last Days in the Desert. They seem to have both felt that the opportunity was there to democratize the tools that they had access to in big studio films.

Wonder Dynamics is very secretive about what exactly its tools do. Deadline’s Mike Fleming Jr saw a limited demo and said he “could see where it will be of value in the area of world creation at modest budgets. The process can be done quickly and at a fraction of a traditional cost structure,” though that leaves us little closer than we started.

Sheridan and Todorovic (who jointly answered questions I sent over) described the system, called Wallace Pro, as taking over some of the grunt work of certain classes of VFX rather than a finishing touch or specific effect.

“We are building an AI platform that will significantly speed up both the production and post-production process for content involving CG characters and digital worlds. The goal of the platform is to reduce the costs associated with these productions by automating the ‘objective’ part of the process, leaving the artists with the creative, ‘subjective’ work,” they said. “By doing this, we hope to create more opportunities and empower filmmakers with visions exceeding their budget. Without saying too much, it can be applied to all three stages of filmmaking (pre-production, production and post-production), depending on the specific need of the artist.”

From this we can take that it’s an improvement to the workflow, reducing the time it takes to achieve some widely used effects, and therefore the money that needs to be set aside for them. To be clear this is distinct from another, more specific product being developed by Wonder Dynamics to create virtual interactive characters as part of the film production process — an early application of the company’s tools, no doubt.

The tech has been in some small scale tests, but the plan is to put it to work in a feature entering production later this year. “Before we release the tech to the public, we want to be very selective with the first filmmakers who use the technology to make sure the films are being produced at a high level,” they said. First impressions do matter.

The $2.5M seed round was led by Founders Fund, Cyan Banister, the Realize Tech Fund, Capital Factory, MaC Venture Capital, and Robert Schwab. “Because we are at the intersection of technology and film, we really wanted to surround ourselves with investment partners who understand how much the two industries will depend on each other in the future,” Sheridan and Todorovic said. “We were extremely fortunate to get MaC Venture Capital and Realize Tech Fund alongside FF. Both funds have a unique combination of Silicon Valley and Hollywood veterans.”

Wonder Dynamics will use the money to, as you might expect, scale its engineering and VFX teams to further develop and expand the product… whatever it is.

With their advisory board, it would be hard to make a mistake without someone calling them on it. “We’re extremely lucky to have some of the most brilliant minds from both the AI and film space,” they said, and that’s no exaggeration. Right now the lineup includes Steven Spielberg and Joe Russo (“obviously geniuses when it comes to film production and innovation”), UC Berkeley and Google’s Angjoo Kanazawa and MIT’s Antonio Torralba (longtime AI researchers in robotics and autonomy), and numerous others in film and finance who “offer us a wealth of knowledge when we’re trying to figure out how to move the company forward.”

AI is deeply integrated into many tech companies and enterprise stacks, making it a solid moneymaker in that industry, but it is still something of a fringe concept in the more creator-driven film and TV world. Yet hybrid production techniques like ILM’s StageCraft, used to film The Mandalorian, are showing how techniques traditionally used for 3D modeling and game creation can be applied safely to film production — sometimes even live on camera. AI is increasingly that part of the world, as pioneers like Nvidia and Adobe have shown, and it seems inevitable that it should come to film — though in exactly what form it’s hard to say.