Great North Labs Startup Ecosystem Kickoff

“It doesn’t take a lot of capital with early-stage tech companies to make a big impact.” – Ryan Weber

The Great North Labs Startup Ecosystem Kickoff brought together successful entrepreneurs and innovators to learn about the current state of the tech and investment ecosystem and network with like-minded professionals. 25 speakers, 6 portfolio startups, and over 250 attendees came together for the afternoon! The topics of education, community, fostering connections, economic impact, and the ripe opportunity for venture capital in the upper Midwest dominated conversations, as some of the area’s most innovative thinkers gathered, spoke, and networked.

Here’s what people have to say about the event:

“a fantastic event with great speakers” (@jmjhjr)

“pretty amazing turnout here at #SCSU“ (@graemethickins)

“Much appreciation to @mnvikingsfan and @robertjweber of @greatnorthlabs for spending their time supporting the startup ecosystem of MN. Great event today @stcloudstate #GNLKickoff – Thank You!!!!” (@jongoldsberry)

Continue the conversation on Twitter with the #GNLKickoff hashtag. If you missed the event, or want to see it all over again, watch it on YouTube!

Follow these links for more info for investo rs and startups. Or contact us!

Thanks to everyone for coming, and stay tuned for future events!

Events

Oct. 8th-14th, Twin Cities Startup Week (TCSW), Greater Minneapolis-St.Paul Area. Over 200 events scheduled!

Oct. 9th, “Minimal Lovable Product Panel” (part of TCSW). 3-5 pm, at the Baker Center, Minneapolis. FieldNation is hosting, and Ryan Weber is a panelist.

Oct. 10th, “Project North Fall Quarterly Roundtable“. 12-4 pm, at the Lumber Exchange Event Center, Minneapolis. Rob Weber will speak on the “State of the Twin Cities Innovation and Startup Community”.

Oct. 11th, Great North Labs Pre-TedX Happy Hour (part of TCSW), St. Cloud. From 5-6 pm, we’ll gather at Great North Labs’s headquarters for a happy hour, ecosystem talk and networking before TedX St.Cloud 2018: Cultivating, which will be held only a few blocks away at the Paramount in St. Cloud. This event recently sold out, so we added a few more tickets. Purchase them through Twin Cities Startup Week!

Oct. 24-25, 2018 FUND Conference, Chicago. “FUND Conference is the nation’s connector of entrepreneurs, venture capitalists, angel investors, and industry experts with a focus on curated deal flow, captivating content and same day connections.” Pradip Madan is speaking.

Oct. 30, TalentMN Leadership Summit, Impact Hub, Minneapolis. Sponsored by Structural!

Portfolio action

CEOs from our portfolio companies presented at the Startup Ecosystem Kickoff, giving overviews, updates, and asks of the Great North Labs community. Visit the Startup Ecosystem Kickoff playlist on the Great North Labs YouTube channel to see presentations from Dispatch, Structural, TeamGenius, FactoryFix, ZAPinfo, and Pitchly.

New advisors

Great North Labs welcomed three new advisors in September:

Jason Heath is the CFO at Drip + LeadPages, and was formerly the VP of Business Intelligence & Analytics at GoDaddy.
Mike Bollinger is the Founder of Livefront and the Co-founder of TECHdotMN.
Graeme Thickins is the President and Founder of GT&A Strategic Marketing Inc. and is a MinneAnalytics board member. He also has a long career as a tech writer and analyst, and runs GraemeThickinsontech.com.

Welcome to the team!

Job Board

Dispatch is hiring Drivers in Cincinnati, Chicago, Dallas, Kansas City, Orlando, and Minneapolis.
Structural is hiring an Account Executive, Office Administrator , and a Senior Engineer (ReactJS).
Team Genius is hiring a Lead Full-Stack Engineer
Pitchly is hiring a UI/UX designer and Core engineer- watch for postings on the Pitchly website.
FactoryFix is hiring a Visual/UI Designer, Vue.js Developer, VP Talent, and a Business Development Rep.

More than 8,000 venture-backed companies received a combined $85 billion in funding in 2017, representing the highest annual total since 2000.[i] As stocks, real estate investments, and venture capital reach record highs, what are investors thinking about where to invest?
The answer depends on the type of investor:

Large funds such as university endowments, pension funds and funds-of-funds have been allocating a part of their portfolio to venture capital for many years now and have seen success. Universities like the University of Minnesota[ii], Stanford[iii] and Yale[iv] have done very well with venture investments. For the fiscal year ending June 2015, the University of Minnesota invested 26.1% of its capital in private investments, with 14% of the private allocation invested in venture capital. The overall fund returned 5.7%, private capital returned 16.1%, and venture capital returned 28%.[v] This has increased the appetite for venture investments among endowments.
High net worth individuals who have built their wealth in tech are reinvesting in tech venture funds.
High net worth individuals who have traditionally invested in the stock market, real estate, or private equity, are warming up to tech venture investing.
Family offices are increasingly doing the same. In a recent tally of the attendees of a US family office event, 35 out of 60 firms expressed interest in venture capital.

Is this a good time for venture investing?
If the economy continues to do well, venture investments will do well. If the economy falters or if there is a stock market correction, this may still be a good time to invest in venture capital.
This is because stock market corrections (and corrections in the real estate market, which usually follows the stock market) follow business cycles, which can last 4-7 years. Venture funds usually invest over a 9-10 year investment cycle (i.e., a 5-6 year investment period followed by a 4-5 year harvest period). A slower business climate or stock market correction ahead could well be bracketed within the life of a new fund. And if needed, with due approvals from the limited partners, venture funds can extend their term to time their exits better.[vi]

Is there benefit in investing in venture funds in down cycles?
Let us look at the dynamics of different asset classes in downturns.

Real estate – During the 2008 financial meltdown, real estate crumbled. As people lost their jobs, renters could not pay their rents, and property owners could not cover their mortgages. As defaults grew, real estate prices dropped. The Case-Shiller index dropped from 195 in 2005 to 116 in 2011.[vii] Considering the leverage of real estate investments, the losses for investors were much higher.
Stocks, ETFs – The stock market similarly took a serious hit. The DJIA dropped 54% from 14,164 to 6,469 over 17 months.
Venture capital – From Q1 2008 to Q1 2009, venture funding fell by 50% nationally to $3.9 billion (Dow Jones Venture Source).

Why did venture capital fare better than real estate or stocks?
First, lean times promote capital efficiency. As is often heard, recessions are the best time to start new companies, which is where early-stage venture capital is focused.
Second, venture capital firms mark up or mark down their investments over their life cycle. However, as actual valuations are pegged only by liquidity events, the real IRR is not known until the investments achieve liquidity. During the holding period, capital-efficient companies, and venture companies that focus on capital efficiency, do well, i.e., are counter-cyclical. They suffer fewer dislocations during downtimes. They can maintain their strategies, continue to do business as usual, and get ahead of those that slow down. Employees of such companies are more secure and loyal. And if needed, high-quality talent not available during good times can be hired, with loyalty that again pays dividends over the long term.

The capital efficiency of the upper Midwest
Companies in the upper Midwest inherently tend to be capital-efficient because there is less capital available. Similarly, smaller funds such as there are in the upper Midwest are inherently more capital-efficient, as they have less to invest.
44% of venture capital flows into Silicon Valley.[viii] This sets the consumption set-point of Silicon Valley companies at much higher burn rates than in regions where availability of venture funds is limited. The relative lack of available capital in other regions, including the upper Midwest, instills caution in spending.

Employee wages
While most other expenses are comparable across the US, with legendary real estate prices, Silicon Valley employees cannot survive at less than Silicon Valley wages.
This is not true in the upper Midwest. Though other expenses are comparable, housing costs may vary from 1/3^rd to 1/10^th of the Bay Area, enabling much greater capital efficiency for employers. For example, Google employees can buy 5 houses for the price of one by moving to one of Google’s locations across the country.[ix]

Figure 1. The real estate cost advantage of the upper Midwest compares well against not only the most expensive regions in the US, but also against what may be incorrectly perceived as lower-cost overseas regions (e.g., China). Seven cities in China and an equal number of cities in the US are listed above Minneapolis.

Fold? Hold? Or double down?
Not only can capital-efficient companies continue without disruption during slow times, given the lag between investment and market benefit, those that increase their investment can emerge even stronger in a recovery.
Intel applied this counter-intuitive strategy across many recessionary cycles, and invested several billion dollars in down cycles.[x] When their new semiconductor fabrication capacity resulting from these investments came online a few years later, their timing coincided with market rebound. On the other hand, competition (e.g., Atmel, Fairchild, Intersil/GE, IBM, Motorola, Raytheon, and several others) weakened from retrenchment and lost market share. As the industry consolidated during down cycles, Intel gained market share, and cumulatively over several cycles, emerged as its leader.
Some investors may feel that liquidity is useful during a downtime. Others argue against it, as getting out of the game when entrepreneurs are especially capital-efficient has a higher opportunity cost, and to use the Intel analogy, puts the winners further ahead of the losers. According to a prominent Silicon Valley investor, “you got to stay in the game”. At these times there are opportunities to go one step farther and double down.

Are smaller funds better than larger funds?
The statistical odds of a unicorn (company valued at over $1B) are lower than, say, of a ‘deci-corn’ (company valued at over $100M). Larger funds invest larger amounts per deal. To return high multiples, they need unicorns, which are rare. Smaller funds invest smaller amounts and can get the same multiples from ‘deci-corns’, which are much more common.

Advantages for Midwest venture capital
There are other tactics used by, and attributes common to, small Midwest VC’s that safeguard against downturns:

Global investments that require skills available in the upper Midwest. While staying abreast of the latest trends in Silicon Valley to stay competitive, Midwest VC’s can take advantage of expertise available in the upper Midwest to serve global markets. In so doing, they avoid the valuation markups and early-round dilutions of Silicon Valley yet seek global parity in later rounds and exits.
Local investments, global exits. An emphasis on the upper Midwest inherently allows investing at a discount compared to the investments in overheated markets such as Silicon Valley. This roughly translates to a 60% discount in term sheets offered on companies in the Upper Midwest. Global businesses rooted in the upper Midwest still attain exit valuations that correlate with global valuations. Thus, if a down cycle may require 50% markdowns for some Silicon Valley funds, Midwest VC’s can still record a 10% (=60-50%) markup at the bottom of the trough, emerge stronger from uninterrupted progress from investees’ capital efficiency, and exit with a markup brought to parity with global valuations in strong economic times.
Emphasis on product-market fit. With the reduced capital investment now possible in many tech businesses, the barrier to entry has been lowered. Smaller venture funds can adjust criteria to focus investments on product-market fit, early revenue, and early break-even and profitability, instead of being limited by the number of affordable investment options. Nothing demonstrates product-market fit and staying power than paying customers and profit; for customers, employees and investors alike, there is nothing more powerful than profitability. Judicious investment in such businesses and mentorship to focus teams on profitability facilitates survival in lean times.
Operators as investors. Small venture funds are often started by former operators with past successful exits, and the Midwest is no different. Many Midwest VC’s have a history of building profitable businesses the old-fashioned way, a dollar at a time. This experience of running a company, of managing payroll through good times and bad, of knowing the revenue and cost management discipline required to make money operationally and sustainably (i.e., not with short-term financial engineering), is invaluable for VC’s to have. So much so, that even accomplished operators will supplement their teams with experienced industry advisors.

[i] https://nvca.org/research/research-resources/

[ii] https://www.bizjournals.com/twincities/news/2018/03/15/how-the-leader-of-the-university-of-minnesotas.html

[iii] https://www.wsj.com/articles/robert-f-wallace-named-ceo-of-stanfords-endowment-1427138729

[iv] https://news.yale.edu/2017/10/10/investment-return-113-brings-yale-endowment-value-272-billion

[v] http://www.pionline.com/article/20151014/ONLINE/151019943/university-of-minnesota-endowment-reports-57-fiscal-year-return

[vi] https://www.strictlybusinesslawblog.com/2017/06/29/the-life-cycle-of-a-private-equity-or-venture-capital-fund/

[vii] https://en.wikipedia.org/wiki/Case%E2%80%93Shiller_index

[viii] National Venture Capital Association

[ix] https://www.cnbc.com/2017/04/07/you-can-buy-multiple-houses-for-the-cost-of-one-near-google-hq.html

[x] https://www.reuters.com/article/us-intel/intel-to-invest-7-billion-in-u-s-as-recession-deepens-idUSTRE5196WR20090210

By Pradip Madan, Ryan Weber, and Rob Weber

In the US, several tech ecosystems have become centers of tech innovation in addition to the much-vaunted Silicon Valley. Silicon Alley is in NYC; Austin is known as Silicon Hills; Silicon Mountain includes Boulder, Colorado Springs, Denver and Fort Collins; Silicon Forest is in the Greater Portland region; and Silicon Prairie covers Omaha, Des Moines, and Kansas City (depending on who you ask). But what about the upper Midwest? Can it rightfully be called “Silicon Lakes”?

The ‘Silicon’ brand has not only spread through the US, but has also found limited purchase overseas, as in Silicon Wadi in Israel. More importantly, the essence of Silicon Valley has become rooted in various international regions, including the thriving tech ecosystems of Hong Kong, Shenzhen, Beijing, Cheng-du, and Dalian in China; Bangalore, Pune, and Hyderabad in India; Haifa, Israel; Tsukuba, Japan; Suwon, Korea; and Hsinchu, Taiwan.

On a smaller scale, innovation hubs are also springing up from Barcelona to Buenos Aires and Paris to Johannesburg, and can be found near universities and in buildings repurposed as co-location centers for innovative tech companies.

The “Silicon” Recipe

So, what is the essence of Silicon Valley? How do you define the nature of innovative regions and hubs, beyond the “Silicon” brand? By identifying the nature of particular attributes across these hubs including culture, talent, capital, and collaboration, we can begin to see common characteristics, and what it takes to form a successful innovation hub.

1. Culture

First, you need the right culture. This attribute is best characterized by the culture Intel established in the late 1970s and early 1980s. Key traits include openness, transparency, and optimism combined with discipline, inclusion, talent and meritocracy; clear shared goals with an emphasis on value creation and collaboration; a platform to succeed with a permission to fail, and the resilience and acceptance to repeat despite failure.

At Intel, there are open cubicles for Intel’s rank and file alike, including the CEO. Measurable individual key objectives are shared with colleagues, as is encouragement to push the edge. The employees are a cultural cornucopia, including the best new college graduates (NCGs) hired from the best schools. These employees manifested the culture, and while Intel was not unique in creating it, its powerful brand did a lot to popularize it.

Today, this culture is multiplied across the many unicorns and the thousands of successful tech companies, from startups to mid-sized enterprises, that make up Silicon Valley.

2. Talent and Capital

On a physical level, the proximity of academic centers such as Stanford University and UC-Berkeley provide a fountainhead of talent and ideas in Silicon Valley. Stanford’s contribution to the development of Silicon Valley is particularly well-known and can be characterized as a successful pairing of advanced engineering and commercialization.

Commercialized advancements begat wealth, and now Silicon Valley is decades into significant wealth creation. This wealth and entrepreneurial thinking has led to a ready flow of risk capital, the availability of which is another key attribute of innovation hubs.

3. Collaboration

Complex problems benefit collaborative thinking, which benefits from diverse experiences. For many years, the complex problems of the human body have required in vitro testing of single molecular pathways for several months, testing in mouse models for 1+ years, and in clinical trials for more than 1-2 years. Realizing the need to accelerate this process by examining multiple variables at the same time, and that computational methods ranging from vertical search to structural biology could accelerate insights, Stanford University established Bio-X in 2003 as a center for interdisciplinary collaboration between the computational and life sciences.

The Innovation Ecosystem as a Rainforest

In his book “The Rainforest: The Secret to Building the Next Silicon Valley”, author Victor Hwang delves into the ingredients for a healthy “innovation ecosystem”. He uses the rainforest as a metaphor to identify the success factors for tech entrepreneurship: a natural eco-system in which abundant species thrive based on autonomy, symbiosis, and survival of the fittest as core principles.

“However, the key to the mystery of Silicon Valley is the software. And that software works like a ‘rainforest’—an ecosystem that thrives because its many elements combine to create new and unexpected flora and fauna. Those elements thrive through rapid mixing, just as they do in a natural biological system.” – Victor Hwang, in Forbes

The Rainforest Thrives in the Valley

The companies in Silicon Valley largely embody the above practices. A hundred miles away in any direction, in Central Valley, in Wine Country, or in Pebble Beach/Monterey, the atmosphere changes tangibly, and the regions are untouched by tech concepts and unicorns. It is not that the communities are deliberately or inalterably different, it’s simply that the awareness of these cultural attributes has not been as powerful or pervasive, or to put it colloquially, it’s not ‘in the air and water’. The same transitions exist around most other “Silicon” ecosystems or innovation hubs.

… But Takes Root in any “Silicon” Soil

What made an impact at Intel was the investments its legendary CEO, Andy Grove, personally made in training, writing books, giving lectures, taking the time to teach new college grad employees, and promoting concepts such as measurable goals, transparency, and constructive confrontation. Similar cultural emphases at companies like Google, Facebook, and Apple help drive innovation today, while the many opportunities to mingle at conferences, meetups and BarCamps, (where like-minded engineers share ideas and solve problems), and the proliferation of open courseware, enable innovation to thrive on a much larger scale.

We instilled these principles in the open workspaces in our own past company, located in three places: St. Cloud, Minneapolis (in the repurposed Grain Exchange), and in San Francisco. Commingling the Midwestern values of the founders with the lessons of Silicon Valley’s success experienced by our Board members, we created a culture of entrepreneurial success.

The open work environment of the co-working space Fueled Collective, in the historic Grain Exchange building, downtown Minneapolis. Where grain was once traded, ideas are currency.

Conversely, is there evidence that policy-based institutions do not have impact? Look around Silicon Valley for unicorns traceable to policy-based cause-and-effect. The city governments of Santa Clara (Intel headquarters), Cupertino (Apple Headquarters), Palo Alto (HP and Tesla headquarters), Mountain View (Google headquarters), or Menlo Park (Facebook headquarters) have not been the agents of change.

So, does that mean policy-driven innovation hubs do not succeed? We believe that policy-based initiatives (ultra-high-speed broadband, net neutrality, etc.) are important enablers, but without entrepreneurial zeal, they are never enough, and ultimately wither. Relatively speaking, regions such as China have benefited from the visible hand of policy initiatives, but in the end, even there, the invisible hand of entrepreneurship has been the necessary ingredient.

Silicon Lakes

The upper Midwest has the culture, the talent, and the capital to be an innovation hub. It has interdisciplinary collaboration – but it can use more. At Great North Labs we are working closely with St. Cloud State University and other local organizations to foster a similar ecosystem of interdisciplinary collaboration. We work with private and corporate LPs to seed startups in the upper Midwest, using the shared knowledge of our advisors and contacts to facilitate their success. We also educate, participate in events, and promote connections in the local tech communities. Our aim is to create a powerful innovation ecosystem in this region and connect it to the larger community of “Silicon” geography around the world. Welcome to Silicon Lakes!

Can the Upper Midwest drive digital innovation in manufacturing and logistics?

Legacy Manufacturing Hubs
Manufacturing and farming ecosystems in the US and around the world have developed around a combination of the following ingredients: raw materials (minerals, crops), energy (electric, hydro, wind, solar), logistics (bays/ports, rivers, roads), talent/labor pools, and viable operating economics. A combination of investment capital, balance sheet leverage, and public policy has provided the needed financial backing to scale up.
In the US, several manufacturing ecosystems have come to exist in different regions over the decades, focusing on aerospace/avionics, automotive, food and farming, healthcare, metals and mining, oil and gas, and semiconductors/electronic components/systems. Upper Midwestern states are prominent in this list. These ecosystems have been using current-generation industrial/process controls systems, heavy equipment, and ERP systems.

Industry	Region	Anchor Companies
Aerospace/Avionics	California	McDonnell Douglas, Hughes Aircraft.
	Kansas	Beechcraft, Cessna.
	Washington	Boeing.
Automotive	Michigan, Indiana, Ohio	Chrysler, Ford, GM, Visteon.
	North Carolina	Borg Warner, Bridgestone, Caterpillar, Continental, Cooper, Daimler, Denso, Freightliner, Goodyear.
	South Carolina	BMW, Mercedes, Bridgestone, Volvo, Magna.
Food and farming	Central Valley, California	Del Monte, Dole.
	Wine Country, California	Gallo, Mondavi, Jackson, J Lohr, Korbel, Rutherford, Sebastiani, Sutter, Wente.
	Minnesota	Cargill, General Mills, Land O’Lakes, Hormel Foods.
	Missouri	Monsanto.
	Wisconsin	Schreiber Foods.
	Iowa	Rembrandt, Burke, West Liberty.
	Idaho	Simplot.
Healthcare	Minnesota	Boston Scientific, Mayo, Medtronic.
	Illinois	Abbott Labs.
Industrial Equipment	Illinois	Caterpillar.
	Iowa	John Deere, Vermeer.
	Minnesota	Honeywell.
	Wisconsin	Rockwell Automation.
Metals	Pennsylvania, Ohio	Bethlehem Steel, U S Steel, USX.
Mining and materials	Minnesota	3M.
Oil and gas	New Jersey	Arco.
	Texas	Exxon-Mobil, Schlumberger, Valero.
Paper	Idaho	Boise-Cascade.
Semi-conductors	Silicon Valley	ICs – Broadcom, Intel, nVidia, Marvell, SanDisk/Western Digital. IC manufacturing equipment – Applied Materials, LAM Research.
	San Diego	ICs – Qualcomm.
	Idaho	ICs – Micron.

Table 1: US regions, manufacturing ecosystems, and key anchor companies.
Digital Manufacturing – Requirements
In the age of ‘traditional’ manufacturing, the resources needed included:

Skilled labor.
Power (electricity, coal, hydro/wind/solar).
Raw materials, natural resources (e.g., farm products, ore, water).
Railroad, shipping, air transport.

In the age of digital manufacturing, from design to shipping, the following capabilities are important:

Internet infrastructure.
Rapid prototyping.
Automation – robotics, self-navigating vehicles.
End-to-end integration with supply chain and logistic partners for real-time visibility.
Advanced STEM professionals in computer science, materials science, and life sciences (clinical medicine, plant biology, molecular biology, and biochemistry).
Digitally-literate labor with skills (e.g., workers who can operate analytic software or fix a clean room air shower to maintain low ppm particle levels).
Predictive analytics for preventive maintenance, resource management, etc.

Advanced technologies useful for these capabilities include:

IoT (e.g., for discrete automation, process controls, asset tracking, robotics, infrastructure management).
Machine learning (e.g., for instrumentation & control).
New materials, including nanomaterials.
3D printing for prototyping, small-batch quick-turn manufacturing, and on-demand inventory.
Secure technologies such as block-chain (e.g., for transactions, contracts, logistics).
AI/predictive analytics (e.g., for monitoring and control, preventive maintenance, ERP).

Next-generation digital manufacturing initiatives have taken root in regions where current manufacturing ecosystems have existed. For example, Tesla’s California factory is housed in the earlier NUUMI (General Motors and Toyota) plant in Fremont in Silicon Valley, where the plant was originally set up in 1960 by General Motors due to cheap land, the port of Oakland nearby, and a freight railroad along the Bay. Similarly, SpaceX (Hawthorne, CA) has leveraged design and manufacturing resources originally set up by McDonnell Douglas and Hughes Aircraft in the Los Angeles area near the port of Long Beach.
The Upper Midwest
Considering the extraordinary value creation of companies such as Tesla and SpaceX, there is a significant investment thesis in enabling next-generation manufacturing organizations to arise in the Upper Midwest, where many similar current-generation manufacturing ecosystems already exist.
Given the democratization (i.e., global availability) of some of the new technologies, the presence of global companies such as Google, Amazon, and IBM in most of the major cities in the Upper Midwest, and the significant academic centers across the region; highly-trained resources are available throughout the region to enable advanced digital manufacturing centers of excellence.
The Upper Midwest also has strong attributes to attract and retain relevant labor pools. Table 2 shows the relative desirability of different regions in the US as destinations for STEM workers. The statistics for the Upper Midwest, broadly including the greater Cincinnati, Columbus, Chicago, Denver, Madison, Minneapolis/St. Paul, and Pittsburgh areas, speak for themselves.

Rank (1=Best)	Metro Area	Total Score	Prof Opptys	STEM Friendliness	Quality of Life
1	Seattle, WA	73.60	2	4	15
2	Boston, MA	71.94	7	1	43
3	Pittsburgh, PA	65.90	12	11	9
4	Austin, TX	65.15	6	8	27
5	Minneapolis/ St. Paul, MN	64.95	19	6	17
6	Madison, WI	64.00	13	16	13
7	Salt Lake City, UT	62.96	9	14	18
8	Springfield, MA	62.80	36	2	7
9	Chicago, IL	60.71	49	13	8
10	Atlanta, GA	60.69	5	27	31
11	Cincinnati, OH	60.51	16	33	14
12	San Francisco Bay Area, CA	60.50	3	7	67
13	Columbus, OH	59.71	33	21	21
14	Denver, CO	57.73	10	24	37
15	San Diego, CA	57.39	59	23	16
16	Sacramento, CA	57.20	44	20	25
17	Colorado Springs, CO	57.00	17	54	20
18	Worcester, MA	56.88	43	3	65
19	Richmond, VA	56.58	8	32	44
20	San Jose, CA	55.79	18	18	53

Table 2: Desirability of cities in the US for STEM professionals. (Data Source: Richie Bernardo/ WalletHub, “2018’s Best & Worst Metro Areas for STEM Professionals”).
Superimposed with Table 1, Table 2 shows the opportunity for these regions to become magnets for next-generation manufacturing.
What Do the Next-Generation Manufacturing Innovations Look Like?
As a result, disruptive new companies are emerging in these regions, creating the investment opportunity to fund them from early-stage growth to scale-up. These companies are working with the incumbents shown in Table 1 to accelerate their evolution towards advanced digital manufacturing.
Examples of innovative companies focused on manufacturing and transportation in the upper Midwest include:
Proto Labs [i]– Rapid prototyping: allowing quick turnaround manufacturing.
FactoryFix [ii]– Contingent labor for equipment: predictive diagnosis for timely repair with skilled labor and relevant spare parts for better customer experience at lower cost.
Basin Commerce– River transportation: harnessing of shipping capacity on lake and river waterways using barge booking software.
Rambl[iii]- AI: sales call CRM logging and intelligence that analyzes calls for specific criteria and actions to surface insight.
Stemonix– Drug discovery: tissue manufacturing for testing potential side-effects of new drugs for neurological and cardiac diseases.
Aker– Crop management: Use of drones for crop management based on weather databases and image-based diseases detection.
The Four Legs of the Innovation Stool
Infrastructure, technology, labor, and capital are the four legs of the innovation stool. The Upper Midwest has all of these.
Through new digital manufacturing technologies focused on topline and bottomline benefits, today’s manufacturing ecosystems are ripe for significant new value creation.
At Great North Labs, we are focused on providing early-stage capital to innovators in the Upper Midwest, with an emphasis on next-generation manufacturing. We leverage partners and advisors with market and operating experience in manufacturing. We invest in, and provide guidance and advisory resources to technology startups like FactoryFix that are poised to disrupt the traditional manufacturing and logistics industries in the Midwest.

[i] Donald Krantz, Director at Proto Labs, is an advisor to Great North Labs
[ii] FactoryFix is a Great North Labs portfolio company
[iii] Rambl’s co-founders, Mitch Coopet and Brian Bispala, are advisors to Great North Labs

IoT and Analytics – Organizing the Industrial Internet

Figure 1: The third revolution: IoT and Analytics. [Image credit: General Electric]

The Evolution of IoT – Where we Came From
The first generation of IoT systems (IoT 1.0) was built mostly with data collected from IP-based sensors by monitoring applications. Whether standalone or embedded in phones, low-cost sensors, compact packaging and distributed power enabled new endpoints and systems. These monitoring applications served needs such as asset tracking, fitness monitoring, mood lighting, physical security, and others.
The second generation (IoT 2.0) leveraged the capabilities of infrastructure tools such as edge gateways, publish-subscribe buses, data warehouses, and API-based integration. The edge gateways allowed IP network segments to connect to sensor bus segments using a diverse set of protocols (e.g., RS-422, RS-485, BACnet, CAN, Fieldbus, Hart, LonWorks, Profibus, Seriplex, Zigbee, Z-wave, and others). The gateways extended the reach of these IoT systems across the many incumbent protocols and enabled the integration of the IP segments with legacy systems. The publish-subscribe buses made data-driven software architectures easier to implement and scale. The data warehouses enabled the integration of structured, semi-structured and unstructured data. The integration APIs enabled ingestion of data at scale. Together, these new building blocks enabled larger-scale IoT applications such as home monitoring, smart metering, power grid management, parking systems, next-generation environmental controls in buildings, windmill farms, warehouse management, etc., with varying degrees of commercial success based on the benefit provided vs. the insertion economics of each use case.

Today’s Frontier
With the larger data sets enabled by frameworks such as Hadoop and big data software such as Pivotal, the third generation of IoT systems (IoT 3.0) is integrating analytics for decision-making. These analytic platforms enable the processing and visualization of the IoT data sets. The large data sets and analytic tools identify aberrations with higher levels of confidence (statistical power) and detect ‘signals’ not seen before, they have lower detection thresholds, greater measurement sensitivity, and higher accuracy.
Applications based on these capabilities range from physical security for homes, buildings, and warehouses; to detection of diseases like lung disease, cancer metastases, or cardiac arrhythmias (see the Mayo Clinic and AliveCor’s recent work); and complex chemical analysis as in rare earth element detection. The availability of computing platforms at the ‘edge’ (e.g., gateways) enables distributed/local analysis.
“The Internet of Things is giving rise to a tsunami of data,” said Great North Labs advisor Ben Edwards (founding team member of home automation pioneer SmartThings). “The billions of residential sensors in people’s homes and the personal sensors on their bodies are sources of data of value to each of us, and depending on what we make available to others, to family members for our safety and well-being, to the retailers we buy from, to the health practitioners who take care of us.”
The proliferation of machine learning algorithms with new programming environments such as Python and dataflow libraries such as TensorFlow has opened up a wide range of new applications. These include anomaly-based security alerts, health and fitness monitoring, genomic analysis and biomarker detection for disease prediction, drones, and self-driving cars.
The addition of machine learning libraries to established platforms such as Matlab, R, SAS, and SPSS, is enabling insertion of machine learning into legacy applications.
The availability of these tools in public and private clouds has made their accessibility and deployment even easier.
Together, with supervised and unsupervised learning, the machine learning software is processing data sets with high data dimensionality, like those from mining, voice processing, drone navigation, and self-driving cars.
The integration platforms and IP-based communication are also enabling the integration of the IoT world with the enterprise world, making applications possible across hybrid computing and control environments such as airports, buildings, cargo ships, factories, hospitals, refineries and oil rigs. While this creates security issues for the enterprise as well as control systems, solutions such as micro-segmentation of hybrid systems are beginning to emerge.

Tomorrow – The New Startups
With products from companies such as Nvidia, Intel, Qualcomm, Broadcom, and now Google, real-time computing power is becoming available at the edge. With easier integration and low cost, it is becoming embeddable at sensing endpoints for applications such as drones, self-driving cars and trucks, personal walking/talking robots, personal assistants, point-of-care diagnosis, no-POS retail, smart logistics, and smart city applications from parking lots to secure airports and intelligent highways.

Adoption Outlook
Beyond analytics and monitoring, this fourth generation of IoT systems will be able to use analytics and machine learning for controls.
What is the outlook for the adoption of these applications? The answer is: it depends. And it is best found through analogies.
How confident do today’s chess masters or masters of the game of Go today feel betting against the machine? IBM’s Deep Blue computer beat chess champion Garry Kasparov in 1997. And as Great North Labs advisor Mitch Coopet (CEO of AI-focused Aftercode) points out, “Since 2016, Google’s Alpha Go platform has won against several Go masters using improved deep learning techniques.”
Or, when will the day come when your x-ray machine will have better diagnostic accuracy than your radiologist? Ahem, that day is already here.
Or, when will Alexa be able to detect tonal infection to assess mood? Based on indications from Amazon and makers of social robots and AI assistants, sentiment analysis will progressively improve the way machines will interact with humans.
Or, when will we be comfortable with self-driven cars? Completely autonomous navigation in 5-7 years may be unlikely, but it is equally likely that in 20 years, self-navigation will become a required safety feature for new cars.
Given the range of answers above, it is not a matter of if, but when, that real-time control using machine learning will be common. These systems will be able to handle use cases as diverse as (i) detecting rare earth minerals to help navigate the earthmoving equipment towards richer ore in a mining operation, (ii) making real-time sweeps at airports to pinpoint explosives across large masses of people, luggage, and infrastructure, (iii) ensuring that the robots deployed in automotive assembly stay within the extremely tight tolerances of frame construction, and (iv) predicting the failure of a component in a high value CT scanner or remote ATM to dispatch the skilled repairman in a timely way to avoid downtime (a business that Great North Labs has invested in).

The Innovation Ecosystem of the Industrial Internet
“Business Insider projects that there will be 55 billion IoT devices operating in the world by 2025, impacting a broad set of industries including automotive, consumer products, electronics, medical devices, and industrial equipment,” notes Great North Labs advisor Robert Bodor (Vice-President and GM, Americas, at Protolabs).
At Great North Labs, with an ambitious vision, we aim to help build the innovation ecosystem of the Industrial Internet visualized by IoT 3.0. This is because we believe the ingredients to build it are uniquely within reach for us.
The three pillars of any tech-enabled disruption are entrepreneurs/developers, adopters/enablers, and capital.

Entrepreneurs/developers. The Upper Midwest created the industrial enterprise. Companies such as 3M, Caterpillar, Emerson, Ford, GM, Honeywell, Johnson Controls, Rockwell, Toro, and many others, have been in the industrial enterprise as their core business for several decades. Their alumni understand the problems and opportunities of the industrial enterprise unlike any others in any other region of the world. The hungry entrepreneurs studying machine learning, paired with vertical experts who have worked on these problems, comprise the ideal startup teams to build the IoT 3.0 applications. The Upper Midwest uniquely provides this talent.
Adopters/enablers. While the industrial enterprise companies themselves may have limited appetites for leading innovation, they understand that market inflection is around the corner, and they are prepared to have their customers lead the way to achieving market alignment. Partnerships with these companies through co-investments, pilots, and sales affiliation to reach their customers and insert the innovations with minimal risk is the most effective path to adoption.
Capital. Channels for entrepreneurial capital include venture funds, incubators and accelerators, and corporate investment funds. Of these, we believe that the first two provide the most efficient path for innovators, and that they create the on-ramp for in-house corporate teams to acquire well-formed companies that have demonstrated a strong product-market fit and, through later-stage funding, have even scaled their businesses. The Silicon Valley startups of yesterday that comprise some of the biggest market caps today have done exactly that. We believe that over an extended period, the Industrial Internet can deliver similar outcomes in the Upper Midwest.

Healthcare Today

Some of the smartest minds work in healthcare, life sciences and biopharma. Yet the healthcare sector struggles to bring innovation into its ecosystem. The pace of innovation adoption has been much greater in other sectors, including in communication (Facebook, Skype), learning (Google, YouTube, Coursera), shopping (Amazon), personal finance (PayPal), and entertainment (Netflix).
This is not because of a lack of innovation in the pipeline. Healthcare sector innovators are hard at work on drugs and therapeutics, devices, and operational aspects of healthcare delivery. Breakthroughs have come in genomics-based precision drugs, machine-learning-based disease detection, EMRs, payment systems, patient adherence and education tools. In healthcare, the innovation tends to be evidence-based, with scientific papers that quantify results from well-designed experiments, and a highly-skilled academic research ecosystem at their source. That aspect is unique in the healthcare sector, and the sector has other ecosystem attributes not seen in other sectors. It’s this unique ecosystem that makes market insertion, growth and adoption at scale more complex, requiring specific insight and enablement.
The Upper Midwest has substantial healthcare anchors to promote a thriving ecosystem of clinical innovation and practice. Examples include the leading research, teaching and clinical centers of the Mayo Clinic and University of Minnesota; hospital systems like Minnesota Health System and CentraCare; device manufacturer Medtronic; software companies like Epic; payers such as United Healthcare; and the processors Optum and United. There are also hundreds of strong, related entities across the region. Healthcare investment is shifting from traditional hotspots like Boston, Houston, and Raleigh-Durham to Silicon Valley, and while the global ecosystem catches up, there is an opportunity to take advantage of this transition to strengthen the ecosystem in the Upper Midwest.
Strong healthcare research leads to breakthrough ideas which require mentorship and incubation to grow. Leading research institutions can organize ecosystem support, such as how the University of Minnesota encourages mentorship through their Venture Center’s Business Advisory Group which brings together entrepreneurs, funds (including Great North Capital Fund), and industry leaders to drive the successful commercialization of its academic research. This is big business, and the U of MN now generates roughly $1B per year from such efforts (two-thirds life sciences and one-third software/IT).
Geographic and industry-themed startup accelerators have also begun to proliferate in the region. Startup accelerators support early-stage, growth-driven companies through education, mentorship, and financing for a fixed period of time, among an admitted cohort of companies. The multi-city startup accelerator, Gener8tor, is managing a new Twin Cities med-tech accelerator backed by Boston Scientific, the University of Minnesota, and the Mayo Clinic. Venture studios and incubators are other forms of early-stage support available in the region. Minneapolis-based Invenshure has successfully launched multiple healthcare startups.
The region’s healthcare system is also significant on the demand side. For example, the cost drivers of healthcare in Minnesota reflect those in the US at large. Yet, while challenges in patient care are also similar to those of other regions, Minnesota’s efficiency is better. Healthcare spending accounts for over 16% of the US economy but is only about 13% of the Minnesota economy. So not only are Minnesota-based insights relevant, they are valuable. Innovations can be developed and piloted in Minnesota, then applied in other states. Startups developed here can be scaled nationally and, with adaptation, internationally.

Figure 1: Health Care Cost Drivers: Spending and Shares of Growth by Service, 2011 to 2013.
(Source: Minnesota Department of Health).

Change is Accelerating

Each decade brings its own set of innovations that transform industries. The healthcare industry will undergo vast changes in the next 10-20 years. The growing spate of investments and partnerships among tech innovators is signaling an increasing rate of change in this sector. The most visible examples of these innovators include Amazon, Apple, Google, Qualcomm, and Walmart. Google Ventures alone did 27 healthcare deals in 2017, up from 9 in 2013.
These companies you wouldn’t normally think of as bastions of healthcare innovation, yet they are all allocating large talent pools and budgets in the industry. Until Tesla, who would have thought that the next innovation in cars would come from Silicon Valley? More than their balance sheets, the noteworthy attributes of these companies are their culture of observing ecosystems, and their practice of inserting innovation in a stepwise and sustained manner to upend markets.
When you combine such entities with those like Berkshire Hathaway and Goldman Sachs (both of whom are partnering with Amazon in healthcare), and the financial and corporate venture groups that work with them, a disruptive landscape begins to take shape in which other innovators and incumbents alike can find new opportunities. For innovators, it means aligning their innovations with insertion points with high economic value and low resistance. For incumbents, at minimum, it means awareness and being prepared; more proactively, it means proactive engagement with capital (e.g., investments through VC firms), pilots, and adoption. For example, the Mayo Clinic has partnered with Google on leveraging its Knowledge Graph smart search algorithm for patient education, and Optum’s venture arm (based in Boston and Silicon Valley) has allocated $250M to venture investments
The range of innovations in the pipeline is equally stunning. Early examples include smartphones coupled with wearables for clinical-grade data. Today’s pipeline includes voice assistants (trained Alexa-like products) for health-related questions, machine vision for detecting physical anomalies (in skin, bones, retinae, or genes) or even bacteria in food. There are AI and visualization-enabled robotic surgery tools for doctors (e.g., Verb Surgical); machine learning in patient-specific onset detection for things like allergies and COPD; big data in early cancer detection (e.g., Freenome) and other diseases like multiple sclerosis, Parkinson’s and autism. The Mayo Clinic and AliveCor have shown that an AI can be trained to identify people at risk for arrhythmia and sudden cardiac arrest despite normal EKG results. There is also analytics-optimized underwriting for individuals and small businesses (e.g., Oscar), Medicaid (Clover) and self-insured populations (Collective Health).

Enabling the Innovation

Applying capital to create, enable and grow innovation platforms, align disruption with practical value in startups, and engage institutions for initial adoption, deployment at scale, and sustained growth requires a deep understanding of the ecosystem and cross-disciplinary skills to navigate it. This is especially true in healthcare given the ecosystem’s unique attributes and complexity, the importance of human health, government regulation, and the depth of incumbency among some players.
Startups benefit from focused enablement of resources including mentors, partners, lab space, hardware/software development expertise, and communication and data analysis platforms. Healthcare enterprises benefit from investment partners who understand their service goals and the need to balance innovation within financial constraints and with operational realities such as the need for patient privacy and the limitations of government regulations.
At Great North Labs, we focus on bringing such forces together to apply capital and expertise effectively and efficiently. We study ecosystems and leverage experts as advisors. We bring people together at events and entrepreneur training, through referrals, and with investment, mentorship and thought leadership by our team. We apply our capital and resources locally, with a deep connection to innovation hubs nationally, and with the goal of scaling globally.

Grant Gibson Apr 30, 2024

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Tipatat Chennavasin, General Manager and Co-Founder of The Venture Reality Fund: Episode 12, Execution is King

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Portfolio raises $125M + talking fundraising with Branch CEO

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