Category Archives: Entrepreneurship

10 Questions to Ask Yourself Before Moving into Management

I went looking for a post to help someone decide if they should move into management, but couldn’t find one that I really loved.  These three posts aren’t bad.  Nor is this HBR article.  But since I couldn’t find a post that I thought nails the spirit of the question, I thought I’d write one myself.

So here are the ten questions you should consider before making a move into management.

 1. Do you genuinely care about people?  

Far and away this is the most important question because management is all about people.  If you don’t enjoy working with people, if you don’t enjoy helping people, or if you’d prefer to be left alone to work on tasks or projects, then do not go into management.  If you do not genuinely care about people, then do not go into management.

2. Are you organized?

While a small number of organizational leaders and founders can get away with being unstructured and disorganized, the rest of us in management need to be organized.  If you are naturally disorganized, management will be hard for you — and the people who work for you — because your job is to make the plan and coordinate work on it.

This is why one of my managment interview questions is:  “if I opened up your kitchen cabinets what would I see?”

3.  Are you willing to continuously overcommunicate?

In a world filled with information pollution, constant distractions, and employees who think that they can pay continuous partial attention, you’d be amazed how clearly you need to state things and how often you need to repeat them in order to minimize confusion.  A big part of management is communication, so if you don’t like communicating, aren’t good at it, or don’t relish the idea of deliberately and continuously overcommunicating, then don’t go into management.

4.  Can you say “No” when you need to do?

Everybody loves yes-people managers except, of course, the people who work for them.  While saying yes to the boss and internal customers feels good, you will run your team ragged if you lack the backbone to say no when you need to.  If you can’t say no to a bad idea or offer up reprioritization options when the team is red-lining, then don’t go into management.  Saying no is an important part of the job.

5. Are you conflict averse?

Several decades I read the book Tough-Minded Management:  A Guide for Managers Too Nice for Their Own Good, and it taught me the importance of toughness in management.  Management is a tough job.  You need to layout objectives and hold people accountable for achieving them.  You need to hold peers accountable for delivering on dependencies.  You need to give people feedback that they may not want to hear.  If you’re conflict averse and loathe the idea of doing these things, don’t go into management.  Sadly, conflict averse managers actually generate far more conflict than then non-conflict-averse peers.

6. Do you care more about being liked than being effective?

If you are someone who desperately needs to be liked, then don’t go into management.  Managers need to focus on effectiveness.  The best way to be liked in management is to not care about being liked.  Employees want to be on a winning team that is managed fairly and drives results.  Focus on that and your team will like you.  If you focus on being liked and want to be everyone’s buddy, you will fail as both buddy and manager.

7. Are you willing to let go?  

Everybody knows a micromanager who can’t let go.  Nobody likes working for one.  Good managers aim to specify what needs to be done without detailing precisely how to do it.  Bad managers either over-specify or simply jump in and do it themselves.  This causes two problems:  they anger the employee whose job it was to perform the task and they abdicate their responsibility to manage the team.  If the manager’s doing the employee’s job then whose doing the manager’s?  All too often, no one.

8.  Do you have thick skin?

Managers make mistakes and managers get criticized.  If you can’t handle either, then don’t go into management.  Put differently, how many times in your career have your run into your boss’s office and said, “I just want to thank you for the wonderful job you do managing me.”  For me, that answer is zero.  (I have,  however, years later thanked past managers for putting up with my flaws.)

People generally don’t complement their managers; they criticize them.  You probably have criticized most of yours.  Don’t expect things to be any different once you become the manager.

9.  Do you enjoy teaching and coaching?

A huge positive of management is the joy you get from helping people develop their skills and advance in their careers.  That joy results from your investment in them with teaching and coaching.  Great employees want to be mentored.  If you don’t enjoy teaching and coaching, you’ll be cheating your employees out of learning opportunities and cheating yourself out of a valuable part of the management experience.

10.  Are you willing to lead?

Managers need not just to manage, but to lead.  If stepping up, definining a plan, proposing a solution, or taking an unpopular position scares you, well, part of that is normal, but if you’re not willing to do it anyway, then don’t go into management.  Management requires the courage to lead.  Remember the Peter Drucker quote that differentiates leadership and management.

“Management is doing things right, leadership is doing the right things.”

As a good manager, you’ll need to do both.

Introducing a New SaaS Metric: The Hype Factor

I said in yesterday’s post, entitled Too Much Money Makes You Stupid, that while I don’t have much of a beef with Domo, that I did want to observe in today’s fund-to-excess environment that any idea — including making a series of Alec Baldwin would-be viral videos — can sound like a good one.

While I credited Domo with creating a huge hype bubble through secrecy and mystery, big events, and raising tremendous amounts of money (yet again today) at unicorn valuations — I also questioned how much (as Gertrude Stein said of Oakland) “there there” Domo has when it comes to the company and its products.

Specifically, I began to wonder how to quantify the hype around a company.  Let’s say that, as organisms, SaaS companies convert venture capital into two things:  annual recurring revenue (ARR) and hype.  ARR has direct value as every year it turns into GAAP revenue.  Hype has value to the extent it creates halo effects that drive interest in the company that ultimately increase ARR. [1]

Hype Factor = Capital Raised / Annual Recurring Revenue

Now, unlike some bloggers, I don’t have any freshly minted MBAs doing my legwork, so I’m going to need to do some very back of the envelop analysis here.

  • Looking at some recent JMP research, I can see that the average SaaS company goes public at around $25M/quarter in revenue, a $100M annual run-rate, and which also suggests an ARR base of around $100M.
  • Looking at this post by Tomasz Tunguz, I can see that the average SaaS company has raised about $100M if you include everyone or $68M if you exclude companies that I don’t really consider enterprise software.

So, back of the envelope, this suggests that 1.5 (=100/68) is a typical capital-to-ARR ratio on the eve of an IPO.  Let’s look at some specific companies for more (all figures are approx as I’m eye-balling off charts in some cases and looking at S-1s in others) [2]:

  • NetSuite:  raised $125M, run-rate at IPO $92M  –> 1.3
  • Cornerstone:  raised $41M, run-rate $44M –> 1.0
  • Box:  raised $430M, run-rate $228M –> 1.8
  • Xactly:  raised $83M, run-rate $50M –> 1.7
  • Workday:  raised $200M, run-rate $168M –> 1.2

There are numerous limitations to this analysis.

  • I do not make any effort to take into account either how much VC was left over on the eve of the IPO or how much debt the company had raised.
  • Capital consumption per category may vary as a function of the category as a CFO friend of mine reminded me today.
  • Some companies don’t break out subscription and services revenue and the ARR run-rate calculations should only apply to subscription.

Since private companies raise capital and burn it down until an IPO, you should expect that the above values represent minima from a lifecycle perspective. (In theory, you’d arrive on IPO day broke, having raised no more cash than you needed to get there.)

So I’m going to rather subjectively assign some buckets based on this data and my own estimates about earlier stages.

  • A hype factor of 1-2 is target
  • A hype factor of 2-3 is good, particularly well before an IPO
  • A hype factor of 3-5 is not good, too much hype and too little ARR
  • A hype factor of 5+ suggests there is very little “there there” at all.

I know of at least one analytics company where I suspect the hype factor is around 10.   If I had to take a swag at Domo’s hype factor based on the comments in this interview:

  • Quote from the article:  “contracted revenue is $100M.”  Hopefully this means ARR and not TCV.
  • Capital raised:  $613M per Crunchbase, including today’s round.

This suggests Domo’s hype factor is 6.1 including today’s capital and 4.8 excluding it.  So if you’ve heard of Domo, think they are cool, are wowed by the speakers and rappers at Domopalooza, you should be.  As I like to say:  behind every marketing genius, there is usually a massive budget. [3]

Domo’s spending heavily, that’s for sure.  How efficient they are at converting that spending to ARR remains to be seen.  My instinct, and this rough math, says they are more efficient at generating hype than revenue. [4]

Time will tell.  Gosh, life was simpler (if less interesting) when companies went public at $30M.

# # #


[1] In a sense, I’m arguing that hype takes two forms:  good hype that drives ARR and wasted hype that simply makes the company, like the Kardashiansfamous for being famous.

[2] And having some trouble making the different data sources foot.  For example, the SFSF S-1 indicates $45M in convertible preferred stock, but the Tunguz post suggests $70M.  Where’s my freshly minted MBA to help?

[3] You can argue that the first step in marketing genius is committing to spend large amounts of money and I won’t debate you.  But I do think many people completely overlook the massive spend behind many marketing geniuses and, from a hype factor perspective, forget that the purpose of all that genius is not to impress TechCrunch and turn B2B brands into household words, but to win customers and drive ARR.

[4] Note that Domo says they have $200M in the bank unspent which, if true, both skews this analysis and prompts the question:  why raise more money at a flat valuation in smaller quantity when you don’t need it?  While my formula deliberately does not take cash or debt into account (because it’s hard enough to just triangulate on ARR at private companies), if you want to factor that claim into the math, I think you’d end up with a hype factor of 3-4.  (You can’t exclude all the cash because every startup keeps cash on hand to fund them through to their next round.)

CAC Payback Period:  The Most Misunderstood SaaS Metric

The single most misunderstood software-as-a-service (SaaS) metric I’ve encountered is the CAC Payback Period (CPP), a compound metric that is generally defined as the months of contribution margin to pay back the cost of acquiring a customer.   Bessemer defines the CPP as:

bess cac

I quibble with some of the Bessemerisms in the definition.  For example, (1) most enterprise SaaS companies should use annual recurring revenue (ARR), not monthly recurring revenue (MRR), because most enterprise companies are doing annual, not monthly, contracts, (2) the “committed” MRR concept is an overreach because it includes “anticipated” churn which is basically impossible to measure and often unknown, and (3) I don’t know why they use the prior period for both S&M costs and new ARR – almost everybody else uses prior-period S&M divided by current-period ARR in customer acquisition cost (CAC) calculations on the theory that last quarter’s S&M generated this quarter’s new ARR.

Switching to ARR nomenclature, and with a quick sleight of mathematical hand for simplification, I define the CAC Payback Period (CPP) as follows:

kell cac

Let’s run some numbers.

  • If your company has a CAC ratio of 1.5 and subscription gross margins of 75%, then your CPP = 24 months.
  • If your company has a CAC ratio of 1.2 and subscription gross margins of 80%, then your CPP = 18 months.
  • If you company has a CAC ratio of 0.8 and subscription gross margins of 80%, then your CPP = 12 months.

All seems pretty simple, right?  Not so fast.  There are two things that constantly confound people when looking at CAC Payback Period (CPP).

  • They forget payback metrics are risk metrics, not return metrics
  • They fail to correctly interpret the impact of annual or multi-year contracts

Payback Metrics are for Risk, Not Return

Quick, basic MBA question:  you have two projects, both require an investment of 100 units, and you have only 100 units to invest.  Which do you pick?

  • Project A: which has a payback period of 12 months
  • Project B: which has a payback period of 6 months

Quick, which do you pick?  Well, project B.  Duh.  But wait — now I tell you this:

  • Project A has a net present value (NPV) of 500 units
  • Project B has an NPV of 110 units

Well, don’t you feel silly for picking project B?

Payback is all about how long your money is committed (so it can’t be used for other projects) and at risk (meaning you might not get it back).  Payback doesn’t tell you anything about return.  In capital budgeting, NPV tells you about return.  In a SaaS business, customer lifetime value (LTV) tells you about return.

There are situations where it makes a lot of sense to look at CPP.  For example, if you’re running a monthly SaaS service with a high churn rate then you need to look closely how long you’re putting your money at risk because there is a very real chance you won’t recoup your CAC investment, let alone get any return on it.  Consider a monthly SaaS company with a $3500 customer acquisition cost, subscription gross margin of 70%, a monthly fee of $150, and 3% monthly churn.  I’ll calculate the ratios and examine the CAC recovery of a 100 customer cohort.

saas fail

While the CPP formula outputs a long 33.3 month CAC Payback Period, reality is far, far worse.  One problem with the CPP formula is that it does not factor in churn and how exposed a cohort is to it — the more chances customers have to not renew during the payback period, the more you need to consider the possibility of non-renewal in your math [1].  In this example, when you properly account for churn, you still have $6 worth of CAC to recover after 30 years!  You literally never get back your CAC.

Soapbox:  this is another case where using a model is infinitely preferable to back-of-the-envelope (BOTE) analysis using SaaS metrics.  If you want to understand the financials of a SaaS company, then build a driver-based model and vary the drivers.  In this case and many others, BOTE analysis fails due to subtle complexity, whereas a well-built model will always produce correct answers, even if they are counter-intuitive.

Such cases aside, the real problem with being too focused on CAC Payback Period is that CPP is a risk metric that tells you nothing about returns.  Companies are in business to get returns, not simply to minimize risk, so to properly analyze a SaaS business we need to look at both.

The Impact of Annual and Multi-Year Prepaid Contracts on CAC Payback Period

The CPP formula outputs a payback period in months, but most enterprise SaaS businesses today run on an annual rhythm.  Despite pricing that is sometimes still stated per-user, per-month, SaaS companies realized years ago that enterprise customers preferred annual contracts and actually disliked monthly invoicing.  Just as MRR is a bit of a relic from the old SaaS days, so is a CAC Payback Period stated in months.

In a one-hundred-percent annual prepaid contract world, the CPP formula should output in multiples of 12, rounding up for all values greater than 12.  For example, if a company’s CAC Payback Period is notionally 13 months, in reality it is 24 months because the leftover 1/13 of the cost isn’t collected until the a customer’s second payment at month 24.  (And that’s only if the customer chooses to renew — see above discussion of churn.)

In an annual prepaid world, if your CAC Payback Period is less than or equal to 12 months, then it should be rounded down to one day because you are invoicing the entire year up-front and at-once.  Even if the formula says the CPP is notionally 12.0 months, in an annual prepaid world your CAC investment money is at risk for just one day.

So, wait a minute.  What is the actual CAC Payback Period in this case?  12.0 months or 1 day?  It’s 1 day.

Anyone who argues 12.0 months is forgetting the point of the metric.  Payback periods are risk metrics and measured by the amount of time it takes to get your investment back [2].  If you want to look at S&M efficiency, look at the CAC ratio.  If you want to know about the efficiency of running the SaaS service, look at subscription gross margins.  If you want to talk about lifetime value, then look at LTV/CAC.  CAC Payback Period is a risk metric that measures how long your CAC investment is “on the table” before getting paid back.  In this instance the 12 months generated by the standard formula is incorrect because the formula misses the prepayment and the correct answer is 1 day.

A lot of very smart people get stuck here.  They say, “yes, sure, it’s 1 day – but really, it’s not.  It’s 12 months.”  No.  It’s 1 day.

If you want to look at something other than payback, then pick another metric.  But the CPP is 1 day.  You asked how long it takes for the company to recoup the money it spends to acquire a customer.  For CPPs less than or equal to 12 in a one-hundred percent annual prepaid world, the answer is one day.

It gets harder.  Imagine a company that sells in a sticky category (e.g., where typical lifetimes may be 10 years) and thus is a high-consideration purchase where prospective customers do deep evaluations before making a decision (e.g., ERP).  As a result of all that homework, customers are happy to sign long contracts and thus the company does only 3-year prepaid contracts.  Now, let’s look at CAC Payback Period.  Adapting our rules above, any output from the formula greater than 36 months should be rounded up in multiples of 36 months and, similarly, any output less than or equal to 36 months should be rounded down to 1 day.

Here we go again.  Say the CAC Payback Period formula outputs 33 months.  Is the real CPP 33 months or 1 day?  Same argument.  It’s 1 day.  But the formula outputs 33 months.  Yes, but the CAC recovery time is 1 day.  If you want to look at something else, then pick another metric.

It gets even harder.  Now imagine a company that does half 1-year deals and half 3-year deals (on an ARR-weighted basis).  Let’s assume it has a CAC ratio of 1.5, 75% subscription gross margins, and thus a notional CAC Payback Period of 24 months.  Let’s see what really happens using a model:


Using this model, you can see that the actual CAC Payback Period is 1 day. Why?  We need to recoup $1.5M in CAC.  On day 1 we invoice $2.0M, resulting in $1.5M in contribution margin, and thus leaving $0 in CAC that needs to be recovered.

While I have not yet devised general rounding rules for this situation, the model again demonstrates the key point – that the mix of 1-year and 3-year payment structure confounds the CPP formula resulting in a notional CPP of 24 months, when in reality it is again 1 day.  If you want to make rounding rules beware the temptation to treat the average contract duration (ACD) as a rounding multiple because it’s incorrect — while the ACD is 2 years in the above example, not a single customer is paying you at two-year intervals:  half are paying you every year while half are paying you every three.  That complexity, combined with the reality that the mix is pretty unlikely to be 50/50, suggests it’s just easier to use a model than devise a generalized rounding formula.

But pulling back up, let’s make sure we drive the key point home.  The CAC Payback Period is the single most often misunderstood SaaS metric because people forget that payback metrics are about risk, not return, and because the basic formulas – like those for many SaaS metrics – assume a monthly model that simply does not apply in today’s enterprise SaaS world, and fail to handle common cases like annual or multi-year prepaid contracts.

# # #


[1] This is a huge omission for a metric that was defined in terms of MRR and which thus assumes a monthly business model.  As the example shows, the formula (which fails to account for churn) outputs a CAC payback of 33 months, but in reality it’s never.  Quite a difference!

[2] If I wanted to be even more rigorous, I would argue that you should not include subscription gross margin in the calculation of CAC Payback Period.  If your CAC ratio is 1.0 and you do annual prepaid contracts, then you immediately recoup 100% of your CAC investment on day 1.  Yes, a new customer comes with a future liability attached (you need to bear the costs of running the service for them for one year), but if you’re looking at a payback metric that shouldn’t matter.  You got your money back.  Yes, going forward, you need to spend about 30% (a typical subscription COGS figure) of that money over the next year to pay for operating the service, but you got your money back in one day.  Payback is 1 day, not 1/0.7 = 17 months as the formula calculates.

CEO is Not a Part-Time Job

While I’m not that close to the whole Twitter situation and although I’m a moderately heavy user (@Kellblog), I don’t study their financials or other statistics.  That said, as a user, I feel a certain malaise around the service and I think it’s definitely in need of some new energy.

What I don’t get it is apparently soon-to-be-made permanent appointment of Jack Dorsey to CEO while simultaneously serving as CEO of Square.  Dorsey is undoubtedly an amazing guy, that’s not the question.

The question is simple:  is CEO a part-time job?  And the answer is equally simple:  no.

I can say this having worked for many CEOs over my 30 year career (e.g., at Business Objects for nearly a decade) and having been a CEO for about a decade as well between MarkLogic and Host Analytics.  No way, no how, no matter how amazing the person, CEO is not a part-time job.

Now the great part about Silicon Valley is that there are, indeed, a lot of amazing people out there.  There is no logical reason why Twitter cannot find someone amazing — who doesn’t already have a full-time job — to run the company.  So please add me to the “I don’t get it” list.

What I’m making is a general statement.  My logic is only compounded by the situation:

  • Square is working toward an IPO in the fairly short-term.  This is an extremely demanding phase for a company and its CEO.
  • Twitter has become a turnaround.  This is an even more demanding phase for company, and they don’t always end well.

So if I’m going to argue that if it’s impossible in general, then it’s kind of impossible-squared when one company is IPO mode and the other is in turnaround mode.

Is it totally unprecedented? No, per this story, but I nevertheless think it’s a bad idea, as two folks who’ve done it seem to agree.

Though rare, it’s not unheard of for a person to run two large companies. That’s what Steve Jobs did with Apple and Pixar, though he described it as “the worst time in [his] life.” Elon Musk, CEO of Tesla and SpaceX, put it more mildly: “It is quite difficult to be CEO at two companies.”

Joining the Granular Board of Directors

I’m very happy to say that I’ve joined the Board of Directors of Granular.  In this post, I’ll provide some commentary that goes beyond the formal announcement.

I think all CEOs should sit on boards because it makes you a better CEO.  You get take remove the blinders that come from your own (generally all-consuming) company, you build the network of people you can rely upon for answering typical CEO questions, and most importantly, you get to turn the tables and better understand how things might look when seen from the board perspective of your own company.

Let’s share a bit about Granular.

  • Granular is a cloud computing company, specifically a vertical SaaS company, aimed at improving the efficiency of farms.
  • They have a world-class team with the usual assortment of highly intelligent overachievers and with an unusual number of physicists on the executive team, which is always a good thing in a big data company.  (While you might think data scientists are computer science or stats majors, a large number of them seem to come from physics.)

To get a sense of the team’s DNA, here’s a word cloud of the leadership page.

wordle 2

Finally, let’s share a bit about why I decided to join the board.

  • As mentioned, they have a world-class team and I love working with supersmart people.
  • I like vertical strategies.  At MarkLogic, we built the company using a highly vertical strategy.  At Versant, a decade earlier, we turned the company around with a vertical strategy.  At BusinessObjects, while we grew to $1B largely horizontally, as we began to hit scale we used verticals as “+1” kickers to sustain growth.  As a marketeer by trade, I love getting into the mind of and focusing on the needs of the customer, and verticals are a great way to do that.
  • I love the transformational power of the cloud. (Wait, do I sound like too much like @Benioff?)  While cloud computing has many benefits, one of my favorites is that the cloud can bring software to markets and businesses where the technology was previously inaccessible.  This is particularly true with farming, which is a remote, fragmented, and “non-sexy” industry by Silicon Valley standards.
  • I like their angle.  While a lot of farming technology thus far has been focused on precision ag, Granular is taking more of financial and operations platform approach that is a layer up the stack.  Granular helps farmers make better operational decisions (e.g., which field to harvest when), tracks those decisions, and then as a by-product produces a bevy of data that can be used for big data analysis.
  • I love their opportunity.  Not only is this a huge, untapped market, but there is a two-fer opportunity:  [1] a software service that helps automate operations and [2] an information service opportunity derived from the collected big data.
  • Social good.  The best part is that all these amazing people and great technology comes packaged with a built-in social good.  Helping farmers be more productive not only helps feed the world but helps us maximize planetary resource efficiency in so doing.

I thank the Granular team for taking me on the board, and look forward to a bright, transformational future.

Summary of the 4Q14 Fenwick & West VC Survey

Because I was reading it and had a minute, I thought I’d do a quick post summarizing the 4Q14 Fenwick & West Silicon Valley Venture Capital Survey (PDF).  As the name indicates, this is an ongoing quarterly survey  on the state of venture capital that pulls from many sources, integrating lots of data into a single picture.

Some highlights (glossary here):

  • Up rounds exceeded down rounds 79% to 6%, with 15% of rounds flat.
  • Average price up 115% in 4Q14, compared to 79% in 3Q14, and the highest value they’ve recorded since they started measuring this in 2005.  (Yes Virginia, prices are good.)
  • 50% of deals were in software companies
  • $14 B was invested in US VC-backed companies in 4Q14, the highest post-bubble amount yet.  (However, remember that during Bubble 1.0, the peak ran around $25B+/quarter.)
  • $49B was invested on the year.  (And you wonder why traffic so bad on 101.)
  • There were 21 VC-backed IPOs in 4Q14 which raised $3B, and 105 on the year.
  • There were 102 acquisitions of VC-backed companies for a total price of $32B in 4Q14 and 531 such deals on the year.
  • $33B was raised by VC funds in 2014, hitting 2005-2007 levels, but not coming close to the $106B raised in 2000.
  • China passed Europe in terms of VC funding raised, tripling from less than $5B in 2013 to more than $15B in 2014.  India more than doubled going from $2B to $5B.
  • Corporate venture capitalists invested $5B in 2014, the highest amount since 2000 (where it was $15B).
  • There are currently 225 accelerators worldwide which have assisted 4264 companies.  AngelList reported over $100M was raised in 2014 across 243 startups.  (This all contributes to a system imbalance where it’s relatively too easy to get angel money, resulting in a fairly large die-off rate between angel round and series A.)
  • When classifying VC deals by the university the CEO attended and then grouping by athletic conferences, the rankings go:  Pac 12, Ivy League, Big Ten, ACC, Big Tweleve, and SEC.  (I did my part for the Pac 12 in 4Q14 — Go Bears!)
  • The Silicon Valley Venture Capitalist Confidence Index published by USF reported confidence of 3.93 in 4Q14, up from 3.89 in 3Q14, and above the (eleven-year) average of 3.72.  Full report here.
  • 19% of rounds had a senior liquidation preference (to existing preferred, not just the common).  Reminder:  glossary here.
  • Only 5% of rounds had senior multiple liquidation preference.
  • 20% of rounds had participation in liquidation, down from a recent high of 34% in 2Q13.  53% of those that had participation, had it uncapped.
  • 5% of rounds had pay-to-play provisions.
  • 13% had redemption rights.

Startups are Hard, Really Hard: Ergo Seek Mentors and Allies

A friend forwarded me a link to this presentation — So You Wanna Do A Startup, Eh?  I liked it so much, I thought I’d do quick post with some brief commentary.


  • Slide 30:  “Seek Mentors and Allies.  This is the most important point I make in this entire presentation.”  Why it’s listed as bullet 5 on slide 30 is beyond me, but it is nevertheless a key point.  I’m doing some startup advisory work of l ate and I certainly believe that some friends with experience, wisdom, and connections can go a long way towards helping a new venture head down the right path and avoid some obvious mistakes.
  • Slide 11:  Five myths about startups, particularly myth 2 (the average tech startup founder is a 25 year old Ivy league dropout) and myth 5 (location doesn’t matter)
  • Slide 30:  Ask rejectors for feedback.  Critical.
  • Slide 36:  Too funny!
  • Slide 40:  I love the Venn diagram and also note that the delusional aspect that enables founders to do the impossible in starting companies can lead to problems later on.