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When is AI not AI?

Earlier this week, Forbes published a piece on ScaleFactor, a startup using AI to automate accounting, which shut down after raising $100m. Here’s the heart of the issue covered in the story: “Instead of [AI] producing financial statements, dozens of accountants did most of it manually from ScaleFactor’s Austin headquarters or from an outsourcing office … Continue reading When is AI not AI?

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Earlier this week, Forbes published a piece on ScaleFactor, a startup using AI to automate accounting, which shut down after raising $100m.

Here’s the heart of the issue covered in the story: “Instead of [AI] producing financial statements, dozens of accountants did most of it manually from ScaleFactor’s Austin headquarters or from an outsourcing office in the Philippines, according to former employees. Some customers say they received books filled with errors, and were forced to re-hire accountants, or clean up the mess themselves.

The piece triggered additional reactions from the press around the general theme of “AI and snake oil”. Here’s Erin Griffith from the NY Times:

And here’s Lucinda Shen from Fortune:

Couple of caveats:

  • Those are perfectly legitimate questions and it’s the role of the press to ask them, so this post is NOT another “VC against the press” rant. I’m just mentioning the above for context because they articulate well the concerns and open questions.
  • I have never met ScaleFactor so I have zero insider knowledge of what may or may not have happened there. Therefore, this is not a piece above ScaleFactor specifically.

Now, this is whole “AI vs humans” topic is really interesting to me, as I’ve spent tons of times over the last few years with AI companies, and have seen those dynamics at play.

So I just wanted to share some what I have learned, and I’ll make two quick points in this post:

  • Having humans involved in the early days of building an AI product is a feature, not a bug. Whether you can “fire” those humans over time or not determines whether you have a business or not
  • Beyond that, there are important lessons to be learned about what use cases lend themselves well to building a sustainable AI business, or not.

Humans behind the scenes

One key thing to understand about AI startups is that they are not like your regular SaaS startups.

(In this post, “AI startups” are startups that leverage AI to power a business application, not providers of horizontal AI tools and technology)

For all the stories about commodization of AI, they remain deeply technical endeavors, which require substantial R&D effort.

A good portion of this R&D effort, especially early on, involves humans. Every AI company I’m familiar with has had a concept of “AI trainer”, at least in the early days. AI trainers are people that help build the data set, label the data, and/or resolve problems when the AI wasn’t able to succeed by itself. Sometimes, all of this happens by having humans mimic what the AI is supposed to do. Sometimes they do it in a sandbox, sometimes they do it in production. I wrote about this over three years ago: Debunking the “no human” myth in AI.

So in the early days of an AI startup, you often find that the product is powered in large part by humans, with a little bit of AI. I don’t know what the exact numbers, but perhaps 70% human to 30% AI wouldn’t be completely out of the question in the beginning.

Now, the clear goal is every AI startup is that this ratio will dramatically evolve over time, as the AI gets trained. Bit by bit, module by module, the idea to automate more and more of the process, and ultimately get to as close to 100% AI automation as possible.

No AI startup founder starts their company to be in the offshore services business. It’s a means to an end.

The problem is that AI progress is very capricious. You can get to early success (call it, 60% or 70% automation) reasonably quickly. But then you often get get stuck. Sometimes it’s temporary: you make no progress for a few months, but eventually the next breakthrough comes along. Sometimes you get stuck for ever.

From a business standpoint, of course, this makes all the difference in the world. With a bunch of humans involved in the early days, you have a terribly negative gross margin business. The hope is that over time you graduate to something that looks much more like a SaaS business in terms of overall economics (with the added benefit of some defensibility as your core AI is hard to replicate). But if you don’t dramatically improve automation and get rid of the humans in the loop over time, you don’t have a business.

While again I don’t have any knowledge of the facts other than what was reported in the press, I suspect this is what happened at ScaleFactor. The early investors financed the vision and bet that the company would be able to transition from humans to fully automated AI over time. As the company wasn’t able to automate enough, however, later stage investors looked at the business, saw the (presumably) negative gross margin, and all passed. The company then ran out of money.

Picking the right use case

For founders, perhaps the most interesting takeaway from this discussion is that, when it comes to building a startup leveraging AI automation at its core, not all business use cases are created equal. So you should pick wisely.

AI is a prediction technology that is powerful, but doesn’t get it right 100% of the time – even after all the training described above.

So it’s much tougher to solve a problem entirely with AI, when your product needs to be 100% successful 100% of the time. And it’s even tougher when the expectation is this 100% success happens in real time.

Here’s some simple (and probably imperfect) heuristics/framework to think about it:

Fully-autonomous self-driving is a good example of needing 100% success in real-time. An AI powered car would need to make a million decisions in real-time (stop, turn, avoid a pedestrian, etc.), and get it right 100% of time – otherwise people would literally die. In the current state of AI technology, you cannot fully automate the solution, humans are indispensable in cars 100% of the time.

At the other extreme, there are use cases where 80-90% accuracy is perfectly fine. For example if Google Photos says it’s a pic of little Suzie when it’s little Emma, it’s annoying but nobody gets hurt. Users also generally don’t have the expectation that this determination happens in real time. This is probably the easiest category to tackle for budding AI entrepreneurs. You can fully automate the solution with AI, no humans required.

The cases in between are the tricky ones.

If you think of customer service chatbots, they need to operate in real time, but they probably fall in the category of 80%-90% success is ok. Sure, it’s annoying when the chatbot gets your request wrong, but it’s great that it works most of the time, and certainly beats waiting 30 minutes on the phone until a customer service rep picks up your call. (Developers can also layer in some simple business rules to supplement the AI in cases where no prediction is required.)

The most difficult use case is precisely something like accounting, which ScaleFactor worked on. Here, you need 100% success 100% of the time – accounting is an exact science. But AI is not going to get you 100% success 100% of the time, we’re just not at that stage yet. So you’re going to need humans in the loop to do the “last mile” – check out whatever comes out of the AI system, and then correct it before you present it to the customer. This is possible in the case of an accounting business because end users typically don’t expect their financial statements to be prepared in real time (our experience with accountants has trained us otherwise).

This last use case is exactly where getting stuck on AI progress can make the difference between having a business or not. So it’s a tricky category to pick for entrepreneurs, wit a perilous journey ahead. “No guts, no glory” I guess, but everything else being equal, it probably makes sense to pick an easier problem.

_________

Note: all the above may be moot now that GPT-3 is going to solve all problems in the world 🙂

Source: http://mattturck.com/notai/

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Private Equity

Boston startups expand region’s venture capital footprint

This year has shaken up venture capital, turning a hot early start to 2020 into a glacial period permeated with fear during the early days of COVID-19. That ice quickly melted as venture capitalists discovered that demand for software and other services that startups provide was accelerating, pushing many young tech companies back into growth […]

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This year has shaken up venture capital, turning a hot early start to 2020 into a glacial period permeated with fear during the early days of COVID-19. That ice quickly melted as venture capitalists discovered that demand for software and other services that startups provide was accelerating, pushing many young tech companies back into growth mode, and investors back into the check-writing arena.

Boston has been an exemplar of the trend, with early pandemic caution dissolving into rapid-fire dealmaking as summer rolled into fall.

We collated new data that underscores the trend, showing that Boston’s third quarter looks very solid compared to its peer groups, and leads greater New England’s share of American venture capital higher during the three-month period.

For our October look at Boston and its startup scene, let’s get into the data and then understand how a new cohort of founders is cropping up among the city’s educational network.

A strong Q3, a strong 2020

Boston’s third quarter was strong, effectively matching the capital raised in New York City during the three-month period. As we head into the fourth quarter, it appears that the silver medal in American startup ecosystems is up for grabs based on what happens in Q4.

Boston could start 2021 as the number-two place to raise venture capital in the country. Or New York City could pip it at the finish line. Let’s check the numbers.

According to PitchBook data shared with TechCrunch, the metro Boston area raised $4.34 billion in venture capital during the third quarter. New York City and its metro area managed $4.45 billion during the same time period, an effective tie. Los Angeles and its own metro area managed just $3.90 billion.

In 2020 the numbers tilt in Boston’s favor, with the city and surrounding area collecting $12.83 billion in venture capital. New York City came in second through Q3, with $12.30 billion in venture capital. Los Angeles was a distant third at $8.66 billion for the year through Q3.

Source: https://techcrunch.com/2020/10/23/boston-startups-expand-regions-venture-capital-footprint/

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Private Equity

Alternative Investments/Real Estate: Housing Market Demand Is “Insane”

Speaking to CNBC on Power Lunch, Glenn Kelman, CEO of real estate brokerage Redfin (NASDAQ: RDFN), said he expected the current boom conditions in the housing market to last well into next year. He attributed the high demand to affluent professionals looking for remote homes as well as low interest rates. Also, he thinks some sellers will put their properties on the market only after the presidential election.

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Alternative Investments/Real Estate: Housing Market Demand Is “Insane”

https://platodata.net/wp-content/uploads/2020/10/alternative-investments-real-estate-housing-market-demand-is-insane.jpg

Redfin CEO Glenn Kelman says the boom could last into next year.

Speaking to CNBC on Power Lunch, Glenn Kelman, CEO of real estate brokerage Redfin (NASDAQ: RDFN), said he expected the current boom conditions in the housing market to last well into next year. He attributed the high demand to affluent professionals looking for remote homes as well as low interest rates. Also, he thinks some sellers will put their properties on the market only after the presidential election. (CNBC)

Kelman: Too good to last forever

“This level of demand is absolutely insane. I would expect it to last into 2021, at least,” Kelman said.

Recent data from the National Association of Realtors shows up the strength in the housing market.

Existing home sales shot up 9.4% in September beating expectations. Even though the median purchase price of a home rose approximately 15% year over year, there is just a 2.7-month supply of for-sale homes, showing tight market inventory conditions.

The 30-year fixed-rate mortgage averaged 2.80% for the week ending Oct. 22, down from 2.81% in the previous week and 3.75% a year ago, according to the Freddie Mac Primary Mortgage Market Survey. Therefore, mortgage rates crept even lower in the latest week.

However, “there’s no way it can last forever,” Kelman warned of the bullish conditions.

Canada: Off the charts

Meanwhile, at the northern neighbor, home sales activity in September is described as “off-the-charts.”

Housing data released by the Canadian Real Estate Association (CREA) last week showed a nationwide year-over-year increase in sales of 45.6%.

This was a new all-time monthly record for the third month in a row.

“This is starting to sound like a broken record (about records being broken), but Canadian home sales and prices set records once again in September … as they did in July and August,” said Shaun Cathcart, senior economist at CREA, in a statement.

Real Estate ETFs in the U.S.

The year-to-date performance of some real estate ETFs is shown below:

iShares U.S. Home Construction ETF (ITB)              +24.61%

SPDR S&P Homebuilders ETF (XHB)                          +20.83%

Vanguard Real Estate Index Fund ETF                      -13.91%

It may be noted that despite the boom conditions in housing, real estate ETFs and stocks have declined in recent days.

According to Barron’s, this may be due to yields on the 10-year and 30-year Treasuries moving higher in recent weeks.

Other reasons could be fears of inflation ticking up in the future amidst an improving economic situation.

Nevertheless, the view is that interest rates are likely to remain low for longer. So demand may remain strong.

“Part of what is fueling this boom is that the economy has just split into two and rich people are able to access capital almost for free, so, of course, they’re going to use that money to buy homes,” said Redfin’s Kelman.

Related Story:   Mortgage Rates Set Another Record Low; Real Estate ETFs Could Benefit

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Source: https://dailyalts.com/alternative-investments-real-estate-housing-market-demand-is-insane/

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Private Equity

Asda’s new owner EG Group seeks new leadership ahead of IPO – report

EG Group is owned by the billionaire Issa brothers and the private equity firm TDR Capital, who teamed up for a £6.8 billion takeover of Asda last month

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UK grocer Asda Group’s new owner EG Group is looking for a new chairman and independent directors as it prepares for a £10bn initial public offering, The Timesreports.

EG Group is owned by the billionaire Issa brothers and the private equity firm TDR Capital, who teamed up for a £6.8 billion takeover of Asda last month.

The move comes after Deloitte resigned last week as the company’s auditor because of concerns over the group’s governance and lack of internal controls, according to the publication.

A decision on candidates will be taken before the end of this year, although roles haven’t been finalised yet as the company is in the process of deciding whether to float in the UK or the US, The Times reports.

Write to Barcelona editors at barcelonaeditors@dowjones.com

From Dow Jones Newswires

Source: https://www.penews.com/articles/asdas-new-owner-eg-group-seeks-new-leadership-ahead-of-ipo-reports-20201023

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