Roughly one in ten workers have filed for unemployment in the last three weeks. Here’s a look at estimated unemployment by state:

Three states might have unemployment rates over 20% right now: Michigan, Pennsylvania and Rhode Island.

15 states (and the District of Columbia) likely have unemployment rates over 15%: Alaska, California, District of Columbia, Hawaii, Kentucky, Louisiana, Maine, Massachusetts, Michigan, Nevada, New Hampshire. New Jersey, Ohio, Pennsylvania, Rhode Island, Washington

Only 11 states have estimated unemployment rates under 10%: Colorado, Connecticut, Florida, Nebraska, Oklahoma, South Dakota, Texas, Utah, Virginia, West Virginia, Wyoming

Colorado is showing what could be the lowest unemployment rate in the country and it is still an estimated 6.9%. At the end of February, only 6 states had unemployment rates over 5%.

If you add the 16.8 million individuals who have filed for unemployment over the last three weeks to the 7.14 million who were already unemployed, the national unemployment rate jumps to nearly 15 percent.

Yes, there has been some hiring over the last three weeks, but hiring has unlikely not proceeded at the same rate as it was earlier in the year. It is likely also true that many who are eligible for unemployment benefits have not applied. Some of these figures could be our lower bounds.

Over the last two weeks, the Federal Reserve has added over $500B a week to its balance sheet.

Until March 2020, the largest weekly increase in the Fed Balance sheet was $292 billion (October 2008). There were two weeks in October 2008 that saw the Fed’s balance sheet increase more than $200 billion. Until March 2020, these were the only two weeks that saw the Fed’s balance sheet increase by more than $200 billion.

In just the last two weeks, the Fed has increased their holdings of U.S. Treasuries by $700B, mortgage-backed securities (MBS) by $91B, loans by $101B and swaps with foreign central banks by $348B. This doesn’t include another $162B purchased on April 2nd and April 3rd.

The Fed balance sheet has increased $1.143 trillion over the last two weeks and just under $1.5 trillion over the last three weeks. The balance sheet has increased by almost 25 percent in the last two weeks, and 35 percent in three weeks.

The loss of hospitality jobs in March 2020 is unprecedented.

When we have April data in a month, we’ll see the U.S. economy suffered more hospitality job losses in two months than during the entirety of the last recession.

The leisure and hospitality sector includes businesses like hotels, restaurants, bars, casinos, amusements parts, museums. Some 85% of the sector is accommodations and food service (think hotels and restaurants). The sector reported 459,000 fewer jobs in March 2020. The worst monthly change prior to March 2020 was a loss of 83,000 (August 1989). The sector accounted for roughly 66% of the total decline in Nonfarm payroll (701K) during March 2020.

The leisure and hospitality sector shed 623,000 jobs during the Great Recession (Jan 2008-February 2010). That decline happened over two years. The current downturn is bigger and quicker than we have ever seen.

The March 2020 data only include workers who are paid by their employer for all or any part of the pay period including the 12th of the month. So it doesn’t include the destruction we saw in the second half of the month. That will come in April’s data. March is much worse than the data suggests.

Some of these furloughed employees will likely return to payrolls in 90 days. Many will not.



From Ross Young:

In 2020, Wuhan is expected to account for 6% of worldwide mobile OLED capacity, 3% of mobile LCD capacity and 2% of LCD TV capacity. In 2021, these figures should rise to 9% of mobile OLED capacity and 4% of LCD TV capacity…Since the display industry is still in a period of over-supply, this supply disruption could help further stabilize and increase prices.

Lately I’ve been thinking a lot about where AI fits into corporate strategy and structure. Marc Andreessen recently suggested

…there’s two obvious business models… one is to be a horizontal platform provider [or] infrastructure provider, analogous to the operating system or the database for the cloud. The other opportunity is in the verticals, the applications of AI.

…AI is a platform and an architecture, in the same sense that the mainframe was architecture, the mini computer was an architecture, the PC, the internet, the cloud, have been architectures. We think there are very good odds that AI is the next one of those. When there’s an architecture shift in our business…everything above the architecture gets rebuilt from scratch. Because the fundamental assumptions about what you’re building change. You’re no longer building a website, you’re no longer building a mobile app, you’re no longer building any of those things, you’re building instead an AI engine that is, in the ideal case, giving you the answer to whatever the question is. And if that’s the case, then basically all applications will change. Along with that all infrastructure will change. Basically, the entire industry will turn over again, the same way that it did with the internet, and the same way it did with mobile and cloud. And so if that’s the case, then it’s just it’s going to be like an absolute explosive period of growth for this entire industry.

What does this mean for business? It means incumbents are displaced because their products are no longer relevant. And the products become irrelevant because the service that was being performed is now being delivered in a new way, perhaps by an AI engine running across the organization. Or perhaps the service is no longer being delivered at all because it has become redundant in a world wherein AI engines deliver superior services to what was previously being delivered.

If AI is a feature sitting on top of an already existing underlying service, then AI will eventually be added to most products and services. But it will sit on top of the service and the underlying service won’t actually change much.

I see AI as much more transformational. I see AI reframing the underlying service. In this way, AI creates new services. Entirely different services and experiences than what was previously being offered.

The Bureau of Industry and Security (BIS) issued an interim final rule that imposes a license requirement for the export and reexport of software specially designed to automate the analysis of geospatial imagery. The rule applies to exports and reexports to all countries beyond Canada.

The rule is scheduled to be published on Monday January 6, 2020. But you can read the unpublished rule here.

Earlier this week the Census Bureau released national and state population estimates. Some key take-aways:

  1. U.S. population continues to slow. U.S. Population increased 0.48% between July 1, 2018 and July 1, 2019. This is the slowest growth rates since 1918.
  2. Nature increase (births minus deaths) continue to slow. S. births fell 0.9 percent in 2019, the fourth consecutive year of decline. Deaths increased 0.4 percent in 2019. The natural increase fell below 1 million in 2019 for the first time in decades.
  3. Net international immigration is also declining. Net immigration declined to 595,348 in 2019, the lowest level in a decade. Net international immigration has been declining annually since 2016. China replaced Mexico to become the largest sending country of foreign-born immigrants to the United States as of 2018.


Other interesting details:

  1. 42 states and the District of Columbia had fewer births in 2019 than 2018. Eight states saw increases in births – Washington (612), Utah (293), Nevada (232), Arizona (175), Idaho (166), Montana (66), Vermont (44), and Colorado (30).
  2. Four states had more deaths than births (natural decrease): West Virginia (-4,679), Maine (-2,262), New Hampshire (-121) and Vermont (-53).
  3. The Northeast region saw its population decrease for the first time in decades
  4. The South, the largest of the four regions, saw the largest numeric growth (1,011,015) and percentage growth (0.8%) between 2018 and 2019
  5. Ten states lost population between 2018 and 2019: New York (-76,790; -0.4%), Illinois (-51,250; -0.4%), West Virginia (-12,144; -0.7%), Louisiana (-10,896; -0.2%), Connecticut (-6,233; -0.2%), Mississippi (-4,871; -0.2%), Hawaii (-4,721; -0.3%), New Jersey (-3,835; 0.0%), Alaska (-3,594; -0.5%), and Vermont (-369 ; -0.1%).
  6. Over the last decade, five of the fastest growing states have been in the Mountain states. Utah was the fastest growing state in the country, followed by Colorado (3rd), Nevada (4th), Idaho (6th), and Arizona (7th).

While technological shifts will help shape the future of work, demographic shifts will also define not only what work is, but where to find it. Population growth is slowing at the same time that  domestic migration or internal migration (movement between states) is slowing to record low levels. These are some of the demographic trends playing out as the digital economy rolls forward. These trends will interact in interesting ways in the coming years.

Breast cancer is the most common cancer in women worldwide. Early detection and treatment can lower mortality rates. But clinicians still fail to identify breast cancer about 20 percent of the time (false-negative results). Clinicians also identify cancer, when there is no breast cancer present (false-positive results). Studies suggest 7-12 percent of women will receive false positive results after one mammogram and after 10 years of annual screening, more than half of women will receive at least one false-positive recall.

False-negative results provide a false sense of security and could ultimately hinder treatment effectiveness. False-positive results can cause anxiety and lead to unnecessary tests and procedures. Another hurdle in identifying breast cancer is a shortage of radiologists needed to read mammograms.

Researchers have developed an AI system that surpasses human experts in breast cancer identification. Their study results were recently published in the journal Nature.

We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers…We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%.

The study results are promising. The AI system outperformed six radiologists and also lowered missed cancer diagnoses on the U.S. sample by 9 percent and mistaken readings of breast cancer by 6 percent. It also produced results across populations, something many AI systems have yet to produce. The researchers didn’t go as far as to suggest their AI system would replace humans.

The optimal use of the AI system within clinical workflows remains to be determined. The specificity advantage exhibited by the system suggests that it could help to reduce recall rates and unnecessary biopsies. The improvement in sensitivity exhibited in the US data shows that the AI system may be capable of detecting cancers earlier than the standard of care. An analysis of the localization performance of the AI system suggests it holds early promise for flagging suspicious regions for review by experts.

Beyond improving reader performance, the technology described here may have a number of other clinical applications. Through simulation, we suggest how the system could obviate the need for double reading in 88% of UK screening cases, while maintaining a similar level of accuracy to the standard protocol. We also explore how high-confidence operating points can be used to triage high-risk cases and dismiss low-risk cases. These analyses highlight the potential of this technology to deliver screening results in a sustainable manner despite workforce shortages in countries such as the UK.

At the same time, it becomes more difficult to make the case for approaches that are exclusively human. It is hard to imagine patients, insurance companies, and others won’t demand AI systems augment what humans are doing. This is especially true in healthcare. But will also likely become increasingly true in other domains. What tasks would you want humans to do alone if you know that you can get better results (greater accuracy, faster, etc) when human capability is augmented with AI systems.

Humans will need to learn how to incorporate these type of AI systems into their workflow. The next big step for AI seems to be “operationalizing AI.” This is likely a decade in the works, but slowly you will see individuals figuring how to best work within environments that are being redefined by AI systems.