Category Archives: Predictions

Kellblog Predictions for 2023

Complete version, see note [1] for details.

Yikes, I’m a few weeks later than usual and now slipping into February, so let’s jump right into our ninth annual predictions post before it’s too late to publish. A quick reminder that I do these for fun and fun alone.  See my FAQ for my terms, disclosures, disclaimers, and the like.

Kellblog 2022 Predictions Review
Let’s start with a review of last year’s predictions which, as it turns out, were pretty good.

1. Covid transitions from pandemic to endemic. Hit.  We can debate the semantics.  Epidemiologists would surely differ.  And the billionaires at Davos still don’t treat it like a cold.  But nevertheless, I think people now generally treat Covid as endemic.

2. Web3 hype peaks. Hit.  I don’t think I’ve ever nailed a prediction harder than this one.  My new#boi weeps for its loss in financial, if not aesthetic, value.

3. Disruptors get disrupted. Hit.  The point here was that just as we become our parents, that Salesforce becomes Oracle, Nvidia becomes Intel, and so on.  This is more the ebb and flow of a natural cycle than a specific prediction — but given Salesforce’s rather dismal year end, I’ll give myself a hit.

4. VC continues to flow. Miss.  Well, while VC funding was down dramatically in 2022 compared to 2021, but remember that 2021 funding was at all all-time high.

The more interesting point is that all this didn’t slow VC fundraising, which hit a record high in 2022.

Going forward, while VCs clearly have dry powder, what’s unclear is their willingness to invest it.  High-quality companies will still be financed, if on less stratospheric terms.  Those delivering average performance may find themselves with water, water everywhere, but not a drop to drink.  Some believe that capital won’t flow again until after an extinction-level event for startups in 2023/2024.

5. The metaverse remains meta. Hit.  Big companies periodically catch self-boredom-itis and attempt to cure it with top-down pivots, dreamed up in corporate offsites with no regard for existing customers and no recollection of the organic, bottom-up processes that helped them become big in the first place.  IBM Watson.  Salesforce Chatter.  Oracle Network Computer.  Informatica Analytic Applications.  BusinessObjects Sundance.  Some companies treat these as publicity stunts, talking a big vision, but not really investing.  Others get confused, believe their own marketing, and bet the ranch.  Meta is in that situation:  customers don’t care,  the market doesn’t look attractive, and key employees are leaving.  Yet on they plow.  A+ commitment to a C+ strategy.

6. PLG momentum builds. Hit.  I think PLG momentum built — and peaked — in 2022.  Former Redpoint VC Tomasz Tunguz pointed out that product-led growth (PLG) firms are less profitable than sales-led growth firms, poking a hole in the “product sells itself” myth, and clouding dreams of liberation from costly S&M departments.  (What drove people to the trial again, anyway?)  PLG is a good strategy for certain categories, but VCs have a tendency, with all good intentions, to ram strategies down the throats of portfolio companies.  As it turns out, PLG is like Nebraska:  “honestly, it’s not for everyone.”

7.  Year of the privacy vault.  Partial. While it’s hard to back this with data, I believe both Okta and Hashicorp are doing well with their secrets vaults, which continues to validate the vault design pattern.  I remain excited about vaults as applied to privacy (for all the reasons I detailed last year) and my friends at Skyflow continued to make great progress with their privacy vault business and the evangelization of it.  What if privacy had an API?  Well, it should.

8.  MSDS is the new MBA.  Partial.  I don’t know how to easily measure this (irony not lost), so the scoring is entirely subjective.  The in-hindsight obvious thing I hadn’t seen coming was the integration of the two — e.g., CMU’s Tepper school offers both an MBA in Business Analytics and an MS in Business Analytics, as do many others.  So the new MBA just might be an MBA in Business Analytics or an MSDS.

9.  Get ready for social impact.  Partial.  I was right about the things that concern younger generations.  I was wrong to the extent that those things now matter somewhat less as the downturn transfers power from employees to employers.  Social change isn’t just about what people believe, it’s about their power to get it.  This is not to stay the new agenda will be completely ignored, but simply that change will come more slowly because the balance of power has shifted.

10.  The rise of causal inference.  Hit.  I continue to believe that causal inference will be to the 2020s what data science was to the 2010s.  Read The Book of Why to learn more.  Or take this causal data science course on Udemy.

Kellblog Predictions for 2023
With that warm up, here are my predictions for 2023.

1. The great pendulum of Silicon Valley swings back.  If you look at Silicon Valley over long periods of time, you see a series of pendulums that swing over decades, all loosely coupled to a great pendulum.  In 2022, that great (fka master) pendulum started to reverse its course and that will continue in 2023.

While Davos, Main Street, and Wall Street may differ on scale and scope, everyone agrees that the economy is turning.  On Sand Hill Road, they’re analyzing softening customer demand.  The interesting part is how this will drive six sub-pendulums in 2023.

  • The valuation pendulum: 10x is the new 20x, flat is the new up. That means a lot of companies need to double their size in order to earn their last-round valuation.  Some have raised enough and/or spent sufficiently little that they can do so on existing cash.  Others are not so fortunateRunway extension is the watchword of the day.
  • The structure pendulum:  it’s back.  One way to maintain a flat headline valuation is to raise money with what’s commonly called structure.  Structure generally means financing terms, such as multiple liquidation preferences or participation (definitions here), that favor new investors over existing investors and the common stockholders in a liquidation.  During boom times, structure falls out of favor.  During slowdowns, structure, and the so-called dirty term sheets that propose it, come back.  Caveat emptor.  Think hard and model multiple scenarios before doing a structured round — a dilutive downround or a clean company sale just might drive more long-term value.
  • The growth vs. profit pendulum:  balance is in, growth at all costs is out.  Formerly backseat metrics like ARR/FTE, free cashflow (FCF) margin, R40 score, and gross dollar retention (GDR) come to the front seat joining net dollar retention (NDR) and ARR growth.  ARR growth still predicts enterprise value (EV) multiples well — but particularly if FCF margins are better than 15%.  That means growth is great — but only if you’re profitable.
  • The founder friendliness pendulum: the invisibility cloak loses some power.  In the 2000s you’d routinely hear VCs whinging about “founder issues” at Buck’s.  But in 2009, with the founding of A16Z,  came a new era of founder friendliness and along with it a founder invisibility cloak (or should I say invincibility cloak) whereby the presumption that the founder should run the company became nearly absolute.  That pendulum will start to swing back in 2023.
  • The employee friendliness pendulum. This is basic Michael Porter, but the new environment has reduced the bargaining power of employees.  We’ll discover that many of those perks and policies that were ostensibly rooted in culture and values were actually rooted in competition for labor.  We’re already hearing, “get back to the office” from Benioff et alia.  Or unlimited PTO — the ultimate perverse benefit — from Microsoft.  More companies will follow.
  • The diligence pendulumFOMO gives way to FOFU.  In the past five years, I’ve never seen deals done faster in Silicon Valley, driven by a competitive market, growth investors with pre-conducted diligence, and a fear of missing out on investments.  As the market cools, deals become less competitive, and stories like FTX emerge, things should return more to normal.

2. The barbarians at the gate are back.  Valuations are down.  Growth headwinds are up.  S&M costs are high.   Stock-based compensation (SBC) is increasingly controversial.  That means activist investors will increasingly be swooping in to shake things up.  And PE giants will increasingly be jumping in to clean things up.  Anaplan and Zendesk were taken private in 2022.  Salesforce is under pressure from two activist investors.  Expect more of this activity to follow in 2023.

While it’s not Henry Kravis at the gate this time, it’s Robert Smith, Orlando Bravo, and Paul Singer.  Management teams should prepare themselves for activist investors and adapt their financial profile to keep valuations high.  While staggered boards and poison pills can stave off hostile takeovers, the best protection against an undesired acquisition is a high stock price.

3. Retain is the new add.  As companies prepare for a potential wave of churn, they put more emphasis on retention than ever before.

Why are companies afraid of churn in 2023?

  • The downturn obviously puts cost pressure on customers.  Must-have items can become nice-to-have overnight.
  • SaaS sprawl.  Per Statista, the average company uses over 100 SaaS apps and for many CFOs that’s too many.
  • SaaS rationalization.  There’s an entire emerging category of vendors (e.g., Cledara, Vendr, Vertice) who work to reduce SaaS spend.  Their mission is to drive your churn.
  • Consumption pricing.  Consumption purists (without ratchets in their contracts) may well find themselves swimming naked as the tide goes out.
  • Bankruptcy.  Companies who sell to SMB may see increased amounts of uncontrollable churn as customers cease operations.
  • Consolidation.  Increased M&A can result in fewer, larger customers with larger discounts and lower costs per unit.

Companies increasingly have internalized the cost of churn.  Namely that:

Cost to backfill churn = CAC ratio * churn ARR

That is, with a CAC ratio of 1.6, it costs $16M to backfill $10M in churn ARR.

While this bodes well for the customer success (CS) discipline, it does not automatically bode well for the customer success department.

Those business-oriented CS teams who thought customer advocacy meant generating customers who advocate for the company will continue to thrive.  But those checklist-oriented CS teams who thought customer advocacy meant internal advocacy on behalf of customers may well find themselves restructured. With new cost pressure, the idea of funding an internal K Street is unattractive compared to redeploying those resources to the underlying engines of customer success, such as product, services, and support.

There are, after all, two sides to being in the spotlight.

4. The Crux becomes strategy book of the year.  Frequent readers already know that Good Strategy, Bad Strategy is my favorite book on corporate strategy.  But my favorite part is how it eviscerates all the garbage that passes for strategy in corporate America.

In 2022, Rumelt published a second book, The Crux, which is more focused on how to build good strategy than on how to avoid bad strategy.  I might have named the books Bad Strategy, Good Strategy and Good Strategy, Bad Strategy, respectively, but I suppose that would have been confusing.

I believe The Crux will become strategy book of the year in 2023 because:

  • It takes a positive approach more than a critical one.  Readers generally prefer that, and I think it’s the one thing that held back Good Strategy, Bad Strategy.
  • It frames strategy around the plan to overcome a critical strategic challenge.  I believe 2023 will provide many companies with clear strategic challenges that need overcoming.  Demand will be strong.
  • He is logical, consistent, and practical in his thinking.  Ruthlessly so.  It’s literally therapeutic to read Rumelt if you’ve spent enough years in the C-suite.
  • Unlike business books that promise a magical answer (i.e., if you could just get <blank> right, then everything else will work), Rumelt offers a magical question:  if you could just figure out the crux of your strategic challenge, then everything else can work.

The last point is not just verbal sleight of hand.  Most business books preach a magical hammer (e.g., positioning, storybranding, category creation) — learn to use this one tool and it will fix all of your problems.  Rumelt does the opposite.  He sets you on a search for the magical nail.  Find that one problem — that central knot or apparent paradox — that, if overcome, will enable your success.

As he said in his first book, “a great deal of strategy work is trying to figure out what is going on.  Not just deciding what to do, but the more fundamental problem of comprehending the situation.”  So true, yet so rarely admitted.

5.  The professionals take over for Musk.  While he’s calmed down a fair bit since I wrote my original draft in December, I nevertheless believe that Elon Musk will hand over the CEO reins of Twitter in 2023.  The announcement of his intentions isn’t exactly news, but the question is will he actually do it?  I think he will, largely because it won’t continue to capture him the attention he needs and because the shareholders of Tesla will basically demand it.

I disclaim that I am not a Musk fanboy and that, in general, I am disappointed by the PayPal mafia, which I once saw as so full of promise.  Perhaps my expectations were too high, but as both the Book of Luke and JFK have said, “to whom much is given, much will be required.”  Particularly, in Silicon Valley, where early success can launch a virtuous cycle of opportunity.

While Silicon Valley is a paragon of innovation, it most certainly is not a paragon of management.  Musk exemplifies this:  from improperly conducted layoffs to alienation of customers to product launch fiascos to total disrespect for product management and communications to CEO code reviews to self-contradictory policy statements to empty promises to dozens of other practices.  Despite the thrill of working directly with the icon and megalomaniac, this simply isn’t sustainable.  The professionals will be tapped to take over in 2023.

6. The bloom comes off the consumption pricing roseConsumption pricing has been a hot topic for the past few years with many boards pressing companies to adopt consumption-based models.  The conventional wisdom was roughly:  if you want Snowflake’s NRR of 160-180%, then you need to adopt their consumption-based model; you can’t get there with per-seat annual SaaS or editions and upsells.

While consumption-based pricing tends to break SaaS metrics and while Snowflake is quick to explain that they are not a SaaS model, there has been significant pressure on enterprise software vendors to include at least a consumption-based component in their pricing.  While this makes sense when passing along cloud-based infrastructure charges that scale with usage, when done in general, they forgot two things:

  • To ask the customers.  Wall Street loves 180% NRR, but what about Main Street?  Do CFOs like when their software bill compounds upwards at an 80% rate?  Methinks not.
  • The tide also ebbs.  During rising tides, users go up, usage goes up, and value delivered presumably also goes up.  So maybe that 180% doesn’t sting as much.  But what about when these drivers go down?  As Buffet said, only when the tide goes out do you see who’s swimming naked.

In 2023, we’ll see there are two types of consumption-based vendors:  those with crafty CROs and those with purists.  The crafties will have already structured ratcheted deals that can only go up year over year.  The purists will not have built in that protection and will see consumption-driven churn as a result.  By the end of 2023, we’ll have many more crafty CROs and a lot fewer purists.

The early returns indicate that while consumption-based companies are seeing bigger hits to NRR, that they are nevertheless driving higher overall growth than their subscription-based counterparts.

Much like my PLG prediction last year, I don’t think consumption-based pricing is dying.  But I do think 2023 will remind everyone — some via a slap in the face — that there are two sides to the consumption-based coin.

7. The rise of unified ops.  The last decade has seen the rise of the “ops” function.  Back when I was young, we didn’t have ops.  If you wanted reporting or analytics, you’d go to finance.  But as functions become more automated, as each VP got their own app, and as CEOs and boards applied more pressure for quality reporting and analytics, each function got their ops person.  Salesops, marketingops, supportops, servicesops, and successops.  Sometimes these consolidated into revops or bizops.  Often, however, they didn’t.

What ensued was depressing.  Siloed ops led to QBRs that resembled tag-team cagefights.  When the CRO and the CMO were fighting, they’d tap in their respective ops heads to continue the brawl.  My CXO versus your CXO.  My ops person versus your ops person.  My numbers versus your numbers.  My model versus your model.

During an interim CMO gig, I worked with the CRO to unify the sales and marketing ops teams into a single revops team.  Even though we were separate organizations both reporting to the CEO, we would have one unified ops team — and we didn’t care who it reported to.  Attend both our staff meetings, but one set of numbers, one model, one forecast.  What that gig ended, the first thing the new CMO did was disband it. Let the cage fights begin again.  It’s human nature.

That story notwithstanding, I think 2023 will see the rise of unified ops.  Why?

  • Cost pressures, and the need to increase efficiency.  One single ops team, driving one set of modeling and reporting is cheaper to operate.
  • CRO consolidation.  As some customer success teams are integrated under the CRO, there will be a natural tendency to integrate salesops and successops.
  • Model wars.  CEOs get tired of having to say, “which model?”  The saleops model?  The FP&A model?   The marketingops model?  Why isn’t there just one?  There should be.
  • Battle fatigue.  Siloed ops isn’t just inefficient, the conflicts it generates are highly visible.  Over time, people get tired.  A great ops leader should be an independent trusted advisor to the business, not a personal pit bull in each CXO’s corner.
  • Resource flexibility.  A single team can move resources dynamically to meet the challenges at hand.
  • Software standardization.  Rationalizing SaaS costs and eliminating stack redundancy is easier when the various ops functions are in a single team.
  • End-to-end funnel analysis.  Breakpoints in the funnel cause problems — e.g., sales doesn’t just want 500 oppties generated this quarter, they want the good ones.  But which are the good ones?  The ones that close.  But which are the good ones for success?  The ones that renew and expand.  How can we generate those?  One team, looking end to end, is in the best position to do this analysis.

For all these reasons, I believe (and hope) that 2023 will see the rise of unified ops.

8.  Data notebooks as the data app platform.  I’ll preface this by saying I’m an angel investor in Hex, who raised a $52M round from A16Z last year, so I’m excited about data notebooks for more than one reason.

While data notebooks are old hat to most data scientists, for business analysts and business users, they are still relatively unknown.  Descended from Jupyter notebooks, today’s data notebooks (e.g., Hex, Notable) generally position as something larger, platforms for collaborative analytics and data science.

When it comes to Jupyter notebooks, this tweet was my introduction to the subject, which got me reading the underlying notebook by Kevin Systrom.

Systrom’s notebook is basically a research paper, built in collaboration, with equations; embedded, executable code; its outputs; configuration management; dependency graphs; and more.  Compare that to the unmanaged spreadsheets you probably use to run your business today.

So the idea of generalizing this to data problems and data users of all types was instantly appealing to me.  I remember when I first met with Barry at Specialty’s, he framed the problem as wrapping models.  As data scientists, we can build models, but we need to wrap them in apps — what we’d now call data apps — so users can use them.  Much as a spreadsheet has a builder and a user (think:  lock down all the cells but a few inputs), a more sophisticated model can and needs to be wrapped as well.  But wrapping a model means effectively building an application, and with that comes a dreaded backlog for building and maintaining those applications.  It was a flashback to enterprise reporting circa 2000 (back when you had the report backlog) and I was instantly hooked.

While I’m not sure I agree with Martin Casado that all SaaS apps will be remade as data apps, I do believe the world is ready for data apps.  I see them, perhaps in a more pedestrian fashion, as these integrated notebooks of code (including not only Python but SQL), no code alternatives, sequencing, models, narrative, metadata, and collaboration — and wrapped and ready for consumption.  That’s why I’m a big believer in data apps and I see data notebooks as the platform for building the first generation of them.  Check out the Hex demo on their home page for a five-minute look.

9.  Meetings somehow survive.  To paraphrase Twain, reports of the death of meetings have been greatly exaggerated.  While I’ll confess to the imprudence of giving Death By Meeting to my boss shortly after its publication, the death of meetings is an entirely different matter.  In January, Shopify announced what appeared to be a total meeting ban, but in reality was a ban on scheduled recurring meetings with three or more people along with a two-week cooling-off period before meetings could be added back to calendars.  Nevertheless, this resulted in the cancellation of 10,000 meetings.

While the move provoked some debate, some backlash, and some discussion of DEI implications, it also provoked some pile-on, often under the fairly offensive slogan, “companies are for builders, not managers.”

The death to meetings crowd makes a number of mistakes in its thinking:

  • That everyone is engaged in individual work, like coding or writing.  How should, e.g., an HR business partner add value by not meeting with people?  Or an BDR manager?
  • That managers somehow do not contribute to building a company.  Great, let’s get 100 developers all reporting to the CEO and see what happens.
  • That all meetings are bad.  There’s a clear baby/bathwater issue here.
  • That online alternatives can replace meetings.  The limitations of email and Slack are well known.  They’re great for some things and rotten for others.  My personal rule:  never try to resolve a hard issue over either.
  • That cadence is unimportant.  I believe that the cadence of regular meetings says a lot about a company — e.g., a weekly vs. a monthly forecast call, or a monthly vs. quarterly sales close.
  • That meetings cannot be improved.  In reality, the goal with meetings, as with any tool, is to use them when appropriate.  The quest is to focus on making them better.  I say quest because to do so is both difficult and endless:  as this article from 1976 demonstrates.

10.  Silicon Valley thrives again in 2024.  While I believe 2023 will be a tough, character-building year for startups, we must remember that this is simply another cycle of creation and destruction in Silicon Valley.  The bad news is that companies will be increasingly faced with difficult, sometimes existential, decisions.  The good news for me (at least) is that demand for gray hair seems to go up when the markets go down.  The good news for everyone is that this is simply a cycle, one from which we shall emerge, and when we do so, the world we emerge into will be more rational and fundamentals-focused.

Until then, stiff upper lip, hunker down, and buckle up.

Forsan et haec olim meminisse iuvabit — Virgil.

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[1]  I inadvertently published an incomplete version of this post on 2/1/23 around mid-day.  While I instantly removed it from the blog, LinkedIn, and Twitter, I was unable to recall the post sent to email subscribers.  Please accept my apologies for this mistake.  While there are a few tricks one can use to avoid such problems (e.g., publish later, don’t create in the WordPress editor), I was on approximately draft 67 of this post, meaning that 66 times I correctly hit “save draft,” but alas once hit “publish” and off it went.

Kellblog Predictions for 2022

Well it’s time for my annual predictions post, a series now in its eighth year.  Before diving in, let me remind readers that I do these predictions in the spirit of fun, they are not business or investment advice, and that all of my usual disclaimers and terms apply.  I’m starting to believe that the value of this series is more about the chosen topics than the predictions themselves because my formula for creating these posts is to select interesting topics that I want to ponder, research them, and figure out a prediction for each topic along the way.

Let’s start with a review of my 2021 predictions, keeping in mind one of my favorite quotes, often misattributed (including by me) to Yogi Berra:  “predictions are hard, especially about the future.”

Kellblog 2021 Predictions Review
On my own admittedly subjective and charitable self-scoring system, 2021 was a pretty good year for Kellblog predictions.

1.  Divisiveness decreases but unity remains elusive.  Hit.  This is totally subjective, but I’d say that divisiveness in the USA has decreased a bit and that unity has most certainly remained elusive.

2.  COVID-19 goes to brushfire mode.  Hit, until recently.  Well, it certainly felt like brushfire mode until December.  As I write, it’s still early in the omicron wave, so I’m going to remain optimistic that current predictions of omicron being more transmissible but less lethal will hold true.

3.  The new normal isn’t.  Hit.  I don’t think many people believe that we’re returning to pre-Covid norms when, and indeed if, we enter a post-Covid world.

4.  We start to value resilience, not just efficiency.  Hit.  I don’t frequently write about supply chain, but I made this prediction because for years I have wondered if, in our quest to wrest inefficiency from the supply chain, we were undervaluing resilience to Black Swan events from wars to infrastructure failures to natural disasters [1].  One person’s inefficiency is another person’s insurance.

5.  Work from home sticks.  Hit.  At this point perhaps for the wrong reasons (i.e., omicron), but where and how we work has already changed and many of those changes will become permanent.  McKinsey is producing some strong content on the future of work as is my friend Dan Turchin on his AI and the Future of Work podcast.

6.  Tech flight happens, but with a positive effect.  Hit.  A lot of Californians have moved to Texas, Arizona, and Nevada — but a lot have also moved to California (i.e., from the Bay Area to cheaper parts of the state).  Florida, despite the hype, nudges out Oregon for fifth place.  My point was that this is normal and healthy:  you can long Miami and Austin without shorting Palo Alto which, by the way, would have been a bad idea in 2020.

7.  Tech bubble relents.  Miss, until recently.  My world is probably best approximated by the WCLD ETF, which opened the year at $53, recently hit as high as $65, and (as I write) is at $51.  Taking the longer view, WCLD has nevertheless more than doubled over the past 5 years, so a lot of this depends on what you mean by “bubble” and “relent.”

Towards that end, revenue multiple is a better bubble indicator than share price, so let’s take a look at the latest from Jamin Ball at Clouded Judgement.

Are multiples down?  Yes, from a median high of nearly 20x to 12x, nearly 40%.  So I’d say yes on “relent.”  On “bubble,” well, we’re still at 12x compared to what I’d say is a normal (eyeballed) range of 6-10x — so we’re still running hot by historical standards.  [2]

8.  Net dollar retention becomes the top SaaS metric.  Hit, depending on what you mean by “top” [3], but my real point was the NDR would replace churn rates as a method for valuing the installed base and I think it has.  See my SaaStr 2020 talk or my GainSight Pulse 2021 talks for more.

9.  Data intelligence happens.  Partial hit.  I’d say it’s “happening” much more than “happened” because we’re still early days in a multi-year category transformation.  My friends at Alation continue to crush it driving their vision of data intelligence extending from the data catalog [4].

10.  Rebirth of EPM.  Hit.  While the second-generation EPM vendors [5] continue to prosper (i.e., Adaptive within Workday, Anaplan and Planful as independent companies) the industry is nevertheless being reborn underneath with new firms such Cube, Mosaic, OnPlan, and Pigment blazing the trail [6].  It’s exciting to watch.

Kellblog Predictions for 2022
Well, here we go with our predictions for 2022.

1. Covid goes from pandemic to endemic.  I’m not sure we ever had a realistic chance to keep the genie in the bottle, as they did in New Zealand, but at least our actions bought us time to create and deploy vaccines.  By the way, if you look at this chart, you might argue that New Zealand, in the end, failed to keep the genie in the bottle.  [7]

See the big bump?  Yes, it does seem that trying to bottle up Covid was destined to failure.  Or was it?  Look at the scale.  Then compare New Zealand to Louisiana, which has a similar population.

The New Zealand peak is 200, the Louisiana peak is 30x higher at 6,000.  If nothing else, and since this is something of BI-focused blog, Covid has taught us a lot about How Charts Lie.

But back to our prediction.  I think 2022 will be the year we stop thinking in pre-Covid and post-Covid terms, and accept that Covid-19 will become endemic.  Much as malaria brought us screened windows and cholera brought us clean water supplies, Covid will be with us for a long time and bring with it lasting (and hopefully in some cases, positive) changes to our day-to-day lives.

2.  Web3 hype peaks.  Is web3 going to change everything because, as Chris Dixon argues, the best entrepreneurs and developers have learned not to build atop centralized platforms?  Or, as Stephen Diehl so indelicately puts it, is web3 bullshit whereby, “the only problem to be solved by web3 is how to post-hoc rationalize its own existence?”  Or are Moxie Marlinspike’s first impressions right — e.g., the missing element in “crypto” is cryptography and that decentralizing the internals of underlying layers won’t prevent centralization at the more nimbly evolving layers above?

Is web3 a ploy to put crypto bros in charge where “the promise of decentralization is just a veneer — and blockchain is, in fact, the worst kind of vendor lock-in?”  Or, did the venerable Grady Booch get web3 right in his retweet below?

Maybe Tim O’Reilly, the person who coined the phrase web 2.0, has the best take [8], arguing simply that it’s too early to get excited about web3.

It sure does feel like 2005.  There are a bunch of new ideas in circulation.  Everyone is talking about them.  People are struggling to understand them and building frameworks to organize and explain them.  And sometimes it’s hard to tell what’s foundational to the new concept and what’s trying to hitchhike a ride on the back of it.  Based on this, I think we’re building towards a web3 hype peak that should happen in 2022 [9].

I’ve always believed that blockchain was invented to support a specific use-case (i.e., bitcoin) and, unsurprisingly, is good for that use-case but has otherwise largely been a technology in search of a business problem — particularly in the enterprise.  Imagine if you went to SIGMOD twenty years ago and predicted the database of the future would be:

You’d have been laughed out of the room.  Despite that, the reality is that database (i.e., blockchain technology) is quite useful for cryptocurrency applications.  The addition of smart contracts were a very a powerful extension that came with Ethereum.  Changing from proof-of-work to proof-of-stake may eliminate the crazy wasted compute and associated energy consumption [11].

But, as I’d say with any special-purpose database — from an OLAP server to an XML database to the Hadoop ecosystem:  it’s great at what it’s built for, but why should you use it for something else?  The default answer is you shouldn’t [12].

When it comes to the decentralization argument, enterprises are inherently centralized in power and rely on centralized systems run by a centralized IT department.  Moving enterprises to decentralized internal systems does nothing to change lock-in factors of their products (e.g., network effects that lock you into Facebook).  Nor necessarily does empowering distributed networks with decentralized technologies — see the above-linked proof-of-stake recentralization arguments.  And if blockchain means automatic freedom from intermediaries, why is Coinbase worth $50B again?

I think DAOs are an interesting concept (great primer here), but the blockchain linkage seems contrived [13] — I could make a Dunbar-number-sized group with organic governance rules and run it via in-person meetings, Zoom, Slack, or of course, Discord.  (Arguably, Richard Branson did, many times.)

I don’t know why anyone would pay $10M for a CryptoPunk or $300K for a Bored Ape, but I do understand collectibles:  an ape costs $300K in part for the same reason that a 1943 bronze Lincoln cent costs $1M — scarcity.  I just thought we were going to use the Internet to eliminate scarcity, not artificially create it.

Finally, I think the self-referentiality of this ecosystem is interesting.  If you want to buy a non-fungible token (NFT) of a Bored Ape, you’re going to need to pay in Ether because that’s the currency the price is listed in.  Which in turn increases demand for Ether.  Note interestingly that while you can use Ether to buy an NFT, you can’t use an NFT to buy Ether because NFTs are not fungible, as Alexis Gay says, “in the sense that you couldn’t funge them.”

3.  Disruptors get disrupted.  When I graduated from college, Oracle (founded 1977) was a ~$30M brash upstart challenging the entrenched leader, IBM, who no one ever got fired for selecting.  I watched Oracle aggressively grow to $1B in revenues, flail several times trying to organically expand into applications, give up on building applications and instead acquire them, inexplicably get into hardware with the acquisition of Sun, and eventually plateau at $40B, effectively having become IBM in the process.  As the saying goes, we become our parents.

Salesforce (founded 22 years later) is well into that cycle, going from brash disruptor to organic grower to M&A-driven grower, though they do a better job of preserving the entrepreneurial spirit if not growth (both were growing at ~25% at the $20B mark).

This is an ongoing pattern driven by Clayton Christensen’s cycles of disruptive innovation.  If you watch this cycle long enough, you can see the disruptors get disrupted — e.g. Siebel was disrupted by Salesforce who was disrupted by Zendesk who is being disrupted by Freshworks.  What drives these disruptive cycles:

  • Feature creep, which leads to market overshoot over time.
  • Management changes, as leadership teams drift from a spirit of value creation for customers to value extraction from them.
  • Specialization, as market leaders build breadth with integration of good-enough products, an opportunity is created for great, point solutions (which often later expand to challenge the core product).
  • Technology platform changes, which antiquate previous architectures, allow new solutions to be built more quickly, and enable entirely new classes of applications.

For several reasons, I believe in 2022 we are going to see many disruptors get disrupted.  Why?

  • Change to cloud-native.  First-generation cloud solved a deployment problem; second-generation solves a development problem as well.  When I build new apps, I can rely not just on my previously developed or open source modules, but on live, running services.  Upstarts can stand yet again on the shoulders of giants.
  • Flood of venture capital (VC).  VC is flowing at unprecedented rates driving record funding amounts at both the early company-creation stage (e.g., seed, angel) and the later growth stage as well.
  • High-growth.  The combination of Covid accelerating digital transformation and unprecedented VC financing has accelerated software company growth (aka, the Covid boost).  At the second order, I can’t help but wonder if accelerating the growth cycle hastens the aforementioned process that creates new disruption opportunities.  Software companies become their parents faster.
  • Product-led growth (PLG).  SaaS provided provided both a market disruption opportunity and a total available market (TAM) expansion in each market segment.  While I’ll cover PLG more below, I think it will have a similar effect, providing both a disruption opportunity in existing segments while simultaneously expanding their potential.

4. Venture capital continues to flow.  2018 was the first year since “the OB” (the original bubble) that we again reached 2000-era levels of VC financing.  2019 dipped a bit, but 2020 came back strong, and 2021 looks to be a blockbuster [14].

PitchBook data reveals that while total funding and mega-funding (where the round raises $100M+) are up, deal is count slightly down, meaning average deal sizes are up and consistent with my view that VC today is have or have-not market.  The haves can raise can raise a ton of money and on good terms.  But the have-nots — those who have yet to demonstrate a strong team, product-market fit, or a scalable growth model — cannot, and face a frustrating form of hunger in the land of plenty.

They keys to success in this environment are two:

  • Raise when the raising’s good.  If you can raise money, you (likely) should.  If you can’t, figure out why — dig beyond superficial, “nice” explanations into real reasons, and then go fix them.  Fast.
  • But trigger spending on business signals.  You undoubtedly raised your most recent financing on the back of an aggressive operating plan.  But don’t, don’t, don’t — for example — hire 10 sellers because they’re in the plan:  hire them because the CRO made the last 10 productive and wants to hire 10 more.

One of these years — maybe 2022, maybe thereafter — VC will be in tighter supply.  So raise money in large quantity when you can.  Fear not dilution — you’ll likely be raising at (what are, by historical standards) stratospheric valuations.  Most of all, while you shouldn’t follow my miserly great-aunt Jo’s expense strategy (whose dying words were “don’t spend”), you should spend if, only if, and when it makes good business sense to do so.

5.  The metaverse remains meta.  If you’ve not taken the 10 minutes yet, you should probably look at this Facebook/Meta, rebranding launch video, a well-produced but at times amazingly awkward metaverse concept video.

The metaverse vision has provoked a range of reactions from dystopian nightmare to dead-on-arrival to heated discussions of “reality privilege” and accusations about the new billionaire utopian boondoggle.

It’s also invited a fair bit of parody, my favorite being the Icelandic tourism board’s, Icelandverse.

Back to the metaverse, I find the vision more Oasis-style (Ready Player One) dystopia than utopia.  While I find the idea of reality privilege interesting intellectual banter, no, I don’t think the best solution to humankind’s problems is to hook everyone into an alternative, virtual reality.  Good sci fi?  Yes.  Good reality?  No. Not in the least.

  • Are virtual worlds fun for immersive gaming?  Yes.
  • Do you need virtual (or crypto) currencies in those worlds?  No, they’re just an add-on money-making opportunity like a Starbucks card [15].  You can buy an upgraded weapon in a game today via a regular credit card [16].
  • Do you need virtual museums in which to hang your NFTs?  They’re cool and I guess collectors do like to show off their collectibles, so maybe [17].  That said, CryptoPunks weigh in at a slim 576 pixels so I don’t think you’ll need fancy display capabilities for some NFTs at least.
  • Do you need virtual real-estate within your virtual world?  Second Life had a full economy with Linden dollars and real-estate, so the idea’s not new, but metaverse real-estate is setting records today.  If the key to real estate is location, location, location, that’s not really a constraint in the virtual world.  That said, a key theme of web3 seems to be manufactured scarcity (which generative NFT collections do well) and which ultimately comes down to a simple matter of trust [18].
  • Can augmented reality help business applications, like customer service?  Yes, I think AR has numerous practical enterprise use-cases and, if nothing else, all the VR technology will benefit more pragmatic use-cases in enterprise.

6.   PLG momentum builds.  While I generally have a negative reaction to hype, and I don’t like the either/or nature of the slogan below, I do think PLG is a good idea.

Let me separate PLG into what I see as two pieces:

  • PLG as business strategy, where the business is built around a model in which marketing and community relations drive end-users to try a product, hopefully like it, buy the ability to use it (or use it more fully), tell their colleagues (directly or virally, e.g., through a Calendly invite), and repeat the cycle.  While Slack, Zoom, and Dropbox are frequently-cited examples, a full list might include over 300 companies.  (You can read a great anatomy of them, here.)
  • PLG as as set of product requirements.  I think PLG brings three core, generic product requirements, none of which have frankly been common to previous generations of enterprise software:  build a product that (a) is quick to deliver end-user value, (b) is easy and even fun for the end-user to use, and (c) is built with the company’s revenue growth strategy in mind, e.g., in-built virality and carefully-selected functional and enterprise-level pay gates.

Many of the concepts behind PLG aren’t new.  Open source has always been about building a community of users who love the product, though historically composed of developers and not end-users.  Market-seeding isn’t new, though prior-generation seeders like Crystal Reports did so not through marketing- and community-driven downloads and trials, but channels of distribution [19].  Consumerization of enterprise software isn’t a new idea , but I’d argue that it’s only become real with the advent of PLG.  Velocity sales models aren’t new either, but they’re also a key part of PLG.

Some PLG ideas are new:

  • User-experience (UX) as job #1.  Only when UX became critical to business/sales strategy did it get serious commitment instead of lip service (in the enterprise at least).
  • Growth teams, subordinating functional silos to united teams of marketers, engineers, analysts, and designers working together to drive growth.
  • Digital experience tools, that go beyond useability testing labs to track what users actually do in the software with an eye towards making it better — such as Pendo, Heap, and Amplitude.

While I think it’s serious overstatement to say, “sales- and marketing-led growth is dead; long live product-led growth,” I think it’s equally dangerous to dismiss PLG along with quarter-zip sweaters as the latest VC fad.  PLG brings many good ideas that companies should consider and map to their own business models.  Despite the risk of PLG noise drowning out PLG signal, I believe companies will increasingly and intelligently apply PLG principles in 2022 — and if you’re not thinking about how to do that, you should be.

7.  Year of the privacy vault.  While I’m not an expert in this field, I am learning more, and I see a lot of exciting things happening in information security:

  • Innovations in digital identity from companies like Ory and Presidio Identity [20].
  • Innovations in cloud security and governance from companies like Cyral and Privacera [20].
  • Innovations in enterprise privacy from DataGrail [20].

The emerging and ever-changing nature of information security is a big part of what interests me, because it means that a lot of smart people with interesting ideas are attacking numerous problems from different angles.  While this leaves me in a near-perpetual state of confusion, I’ll repeat what I’ve often said about the metadata space:  anyone who isn’t confused doesn’t really understand the situation (Edward R. Murrow).  In metadata, I feel like I finally do understand the space.  In information security, well, I’m still working at it.

In the past ~25 years, there’s a particular feeling I’ve had only on rare occasion:

I’ve met a lot of great entrepreneurs and worked with a lot of great companies during those years, but only those three times did I have the immediate reaction:

  • This is obvious.  (Well, post facto obvious, once you understood it.)
  • This is huge; everyone needs this.
  • I need to be a part of this.

In Anshu’s case it admittedly took more than one drink for me to understand the idea, but what I liked about it, what made it seem so post facto obvious was this:

  • Enterprises, where possible, should get out of the business of handling sensitive information.  I know it’s not always possible, but if the data is non-core to operations, why not delegate storing it to someone else?  While hospitals need to store medical images, does TurboTax really need to store your social security number to file your taxes once per year?  It’s hard.  Let someone else do it.
  • You can replace sensitive data with tokens.  You don’t need to store someone’s credit score when you can store a token that maps to it and isolate the score to a separate database.  It’s classic indirection.  But it usually means you can’t then do anything with the data — unless you incorporate the ideas in the next two bullets.
  • You actually need an API more often than you need access.  Most of the time you don’t need direct access to sensitive data, you just need to do something with it.  You don’t need to know someone’s credit score; you need to know if you can make them a loan and at what interest rate.  That is, you can pass a token for credit score to a service that returns approval status and approved rate in a loan approval application.
  • You can encrypt data without losing the ability to work with itPolymorphic encryption lets you verify the last four digits or a social security number or return all phone numbers in the same area code without first decrypting the data.  This means you can get utility from encrypted data.  Not being a security person, this idea was entirely new and fairly mind-blowing to me [22].
  • Vaults are an existing design pattern.  Google, Apple, and Netflix have taken a low-trust, tokenized vault approach to handling sensitive information in their internal systems.

We will see if my spider sense was correct a third time.  While my sense is most developed in data and analytics, I love modularization, normalization, and specialization and this play is about all three.  To hear the Skyflow story directly from Anshu himself, watch the video here.

8.  MSDS is the new MBA.  For decades, and often contrary to prevailing fashion, I’ve counseled people to consider getting an MBA during their career journey for any of the following reasons:

  • The knowledge.  MBA coursework is generally useful in business, regardless of the caliber of school you attend.
  • The network.  At a top school, you will likely become part of a great network that will benefit you throughout your career.
  • The career-change opportunity.  The MBA offers a unique chance to switch roles or industries (e.g., from engineering into product, from consumer to enterprise).

Given the time and cost of MBAs, it’s popular these days to say that MBAs aren’t worth the trouble.  Autocomplete confirms these doubts.

While I frequently still recommend MBAs to those who seek my advice, I find myself increasingly asking them:  have you considered a master of science in data science?  Such programs can be done in as little as half the time and at half the cost of an MBA, have numerous online and hybrid options, are offered by many prestigious schools, provide superior analytical training, and offer similar career change opportunity.

While a top-tier MBA will still be de rigeur in investment banking, VC, and management consulting for the foreseeable future, I do believe that mid-career professionals will increasingly evaluate the MBA and the MSDS as alternative means to advance their careers — and that many will take the MSDS route.

9.  Get ready for social impact.  Millennials, and for that matter, many of the rest of us, increasingly demand purpose in our work.  If we’re going to spend 40, 50, or more hours per week working, then we’d like the company to provide both a paycheck and a sense of purpose.  In the workplace, according to a recent Gallup report, millennials want leadership to change its approach:

The sense of purpose, however, goes well beyond the workplace and includes the desire to address societal concerns related to sustainability, capitalism, human rights, and social justice.  While Boomers and Xers were content to Party Like It’s 1999, the next generation wants to focus on the future and solving the world’s largest problems.  Good.

This drives for whom and how they want to work, the products they buy, the brands they value, the vacations they take, the causes they support, the hobbies they pursue, the lifestyles they lead, and the money they invest.  In short, everything.

This era has brought us everything from local organic produce and forks over knives to the 1% Pledge, the B Corp, DEI, impact investing, ESG funds, stakeholder capitalism, carbon offsets, and data rights as human rights.

I think Europe is leading the US on many of these changes so, as per the famous William Gibson quote, I get a glimpse into the future through my work with Balderton Capital which has not only committed itself to a set of sustainable future goals (SFGs), but also recently announced their first annual progress report on them.

ESG momentum will build in 2022.

10.  The rise of causal inference.  For the past decade I’ve told people that data science was the new plastics — in the sense of the famous quote from The Graduate.

While I think that was spot-on, this year I have a new “one word” —  causal inference.  Why?

  • Most of the data science we do today is some sort of classification and regression.  We can group like entities, we can predict into which group a new one will fall.  We can build a mathematical model of an independent variable and make predictions about it based on dependent variables.  It’s cool stuff, but in the end, this is about correlation.  How things move together.
  • Yet, we all know that correlation does not imply causation.  We know that windmill rotation doesn’t make the wind blow [24].  We know that waking up dressed doesn’t cause headaches and that ice cream sales don’t cause drownings [25]. Yet, most businesspeople today forget that when they’re interpreting data.  We say that correlation does not imply causation and then we say stuff like, “all of the customers who churned last quarter filed more than five severity-one cases in the past year!”  [26]
  • The first-generation of data science has given us lots of data and some great modeling tools to interpret data.  The bad news is that we — not data scientists, but regular analysts and business people — are not very good at interpreting it.
  • Where possible, we need to figure not just where variables correlate but what actually causes what.  To do so normally requires an experiment (i.e., a RCT) but sometimes causal questions can be correctly answered using observational data.  The insight about how to do that, by the way, is not trivial — it won the 2021 Nobel Prize in Economics.
  • The big guys are doing it.  A decade ago the hyperscalers had data science teams and typical companies, even large ones, didn’t.  Today, the hyperscalers have causal inference teams and typical companies don’t.  To the extent you believe the big guys are leading indicators of the mainstream, you should believe that determining not just correlation, but causation, is coming soon to a business meeting near you [27].  You can get ready the easy way or the hard way.

If you made it this far, thank you!  Read the links — there’s gold in those hills.  Remember that I write this post in the spirit of fun and to force myself to research interesting topics.  Have a happy, healthy, and Rule of 40 positive 2022.

Peace out / Dave.

# # #


[1]  I did study seismology (i.e., geophysics) after all.  Earthquakes happen.

[2]  As mentioned in last year’s post there are plenty of possible reasons for this including the possibility that the companies are higher quality and/or growing faster — see last year’s post.

[3]  Some might argue growth is top — particularly if you define top as most correlated to revenue multiple.  Based on data as of this writing, the R^2 between EV/NTM-revenue multiple and NTM-revenue-growth is 0.52 vs. 0.24 for NDR.  Play around here for more.

[4]  Reminder that I am an angel investor in and sit on the board of Alation.

[5]  Who are the first-generation cloud EPM vendors

[6]  I am an investor in Planful and Cube, an advisor to OnPlan, and occasionally chat with Mosaic and Pigment, among others.  Hey, I like EPM.

[7]  Louisiana actually has about a 10% smaller population (4.6M) than New Zealand (5.1M)

[8]  Tim’s What is Web 2.0 post is well worth reading both for the history lesson and, more subtly, to beam you back to a time where something was emerging and what it looked like for people to try and understand and describe it.

[9]  Gartner has a blockchain hype cycle (that lists numerous web3 technologies) but not a web3 hype cycle.  Currently, NFTs are at the approximate peak of that cycle.

[10]  Technically, BASE as a database concept didn’t exist at the time.

[11]  Though not without its own problems.

[12]  A repeated pattern in database history — everyone wants to rule the world because it’s a big world to rule.  Most of the time, however — and relational databases are a notable exception — the new database is not a great general-purpose alternative.  The reductio argument here is there should be no general-purpose databases as every purpose is a special one.

[13]  See prior comment about hitchhiking.

[14]  Sources include Statista, PitchBook, CBInsights, and (in one case) my estimates.

[15]  In addition to providing Starbucks with consumer data, they have $1.6B in prepaid value today.  Remember a big part of how Warren Buffet got to be Warren Buffet:  float.

[16]  Yes, I understand that games can force you into their currency by providing rewards in game-units and that you can create a one-way transformation between cash and game-units (i.e., you can buy units with cash, but not cash with units).

[17]  Museums provide access as their core function but also offer security, preservation, and education (e.g., docents) surrounding their works.

[18]  Trust that the promoters will keep their promise about number and trait distribution of works and avoid the tendency to excessively extract value by minting more and/or derivative works (e.g., mutant apes) that potentially undermine the original collection and devalue traits.  Creating scarcity is easy.  Preserving it might well be hard.

[19]  In many cases because, well, the Internet didn’t exist yet.  Microsoft helped to put Crystal Reports on the map by distributing it with Visual Studio.

[20]   Disclaimers:  I’m an advisor to Presidio Identity.  Ory is a Balderton portfolio company.  I’m an advisor to and investor in Cyral.  I have done some consulting with Privacera. I am an investor in DataGrail.

[21]  Which quite happily I made.

[22]  Read up on fully homomorphic encryption which enables you to perform calculations on data without first decrypting it.  While fully homomorphic encryption is prohibitively computationally expensive, another key Skyflow insight was that many “numbers” aren’t fully treated as numbers in practice — e.g., you might verify the last 4 digits of an SSN but you’re never going to multiply two of them.

[23]  The SFGs linked come from Balderton Capital where I work part-time as an EIR.

[24]  Reverse causation.

[25]  The third-cause fallacy.  Going to bed drunk increases both waking up dressed and having a headache.  Warm weather increases both swimming rates (which increase drownings) and ice cream sales.

[26]  They also all had, e.g., brown-eyed CIOs, more than $500M in revenues, and parking lots with more than 200 spaces.

[27]  Irony alert, I’m making a correlation-based argument here!

Kellblog 2021 Predictions

I admit that I’ve been more than a little slow to put out this post, but at least I’ve missed the late December (and early January) predictions rush.  2020 was the kind of year that would make anyone in the predictions business more than a little gun shy.  I certainly didn’t have “global pandemic” on my 2020 bingo card and, even if I somehow did, I would never have coupled that with “booming stock market” and median SaaS price/revenue multiples in the 15x range.

That said, I’m back on the proverbial horse, so let’s dig in with a review of our 2020 predictions.  Remember my disclaimers, terms of use, and that this exercise is done in the spirit of fun and as a way to tee-up discussion of interesting trends, and nothing more.

2020 Predictions Review

Here a review of my 2020 predictions along with a self-graded and for this year, pretty charitable, hit/miss score.

  1. Ongoing social unrest. No explanation necessary.  HIT.
  2. A desire for re-unification. We’ll score that one a whopping, if optimistic, MISS.  Hopefully it becomes real in 2021.
  3. Climate change becomes new moonshot. Swing and a MISS.  I still believe that we will collectively rally behind slowing climate change but feel like I was early on this prediction, particularly because we got distracted with, shall we say, more urgent priorities.  (Chamath, a little help here please.)
  4. The strategic chief data officer (CDO). CDO’s are indeed becoming more strategic and they are increasingly worried about playing not only defense but also offense with data, so much so that the title is increasingly morphing into chief data & analytics officer (CDAO).  HIT.
  5. The ongoing rise of devops. In an era where we (vendors) increasingly run our own software, running it is increasingly as important as building it.  Sometimes, moreHIT.
  6. Database proliferation slows. While the text of this prediction talks about consolidation in the DBMS market, happily the prediction itself speaks of proliferation slowing and that inconsistency gives me enough wiggle room to declare HITDB-Engines ranking shows approximately the same number of DBMSs today (335) as one year ago (334).  While proliferation seems to be slowing, the list is most definitely not shrinking.
  7. A new, data-layer approach to data loss prevention. This prediction was inspired by meeting Cyral founder Manav Mital (I think first in 2018) after having a shared experience at Aster Data.  I loved Manav’s vision for securing the set of cloud-based data services that we can collectively call the “data cloud.”  In 2020, Cyral raised an $11M series A, led by Redpoint and I announced that I was advising them in March.  It’s going well.  HIT.
  8. AI/ML success in focused applications. The keyword here was focus.  There’s sometimes a tendency in tech to confuse technologies with categories.  To me, AI/ML is very much the former; powerful stuff to build into now-smart applications that were formerly only automation.  While data scientists may want an AI/ML workbench, there is no one enterprise AI/ML application – more a series of applications focused on specific problems, whether that be C3.AI in a public market context or Symphony.AI in private equity one.  HIT.
  9. Series A remains hard. Well, “hard” is an interesting term.  The point of the prediction was the Series A is the new chokepoint – i.e., founders can be misled by easily raising $1-2M in seed, or nowadays even pre-seed money, and then be in for a shock when it comes time to raise an A.  My general almost-oxymoronic sense is that money is available in ever-growing, bigger-than-ever bundles, but such bundles are harder to come by.  There’s some “it factor” whereby if you have “it” then you can (and should) raise tons of money at great valuations, whereas, despite the flood of money out there, if you don’t have “it,” then tapping into that flood can be hard to impossible.  Numbers wise, the average Series A was up 16% in size over 2019 at around $15M, but early-stage venture investment was down 11% over 2019.  Since I’m being charitable today, HIT.
  10. Autonomy CEO extradited. I mentioned this because proposed extraditions of tech billionaires are, well, rare and because I’ve kept an eye on Autonomy and Mike Lynch, ever since I competed with them back in the day at MarkLogic.  Turns out Lynch did not get extradited in 2020, so MISS, but the good news (from a predictions viewpoint) is that his extradition hearing is currently slated for next month so it’s at least possible that it happens in 2021.  Here’s Lynch’s website (now seemingly somewhat out of date) to hear his side of this story.

So, with that charitable scoring, I’m 7 and 3 on the year.  We do this for fun anyway, not the score.

 Kellblog’s Ten Prediction for 2021

1. US divisiveness decreases but unity remains elusive. Leadership matters. With a President now focused on unifying America, divisiveness will decrease.  Unity will be difficult as some will argue that “moving on” will best promote healing while others argue that healing is not possible without first holding those to account accountable.  If nothing else, the past four years have provided a clear demonstration of the power of propaganda, the perils of journalistic bothsidesism, and the power of “big tech” platforms that, if unchecked, can effectively be used for long-tail aggregation towards propagandist and conspiratorial ends.

The big tech argument leads to one of two paths: (1) they are private companies that can do what they want with their terms of service and face market consequences for such, or (2) they are monopolies (and/or, more tenuously, the Internet is a public resource) that must be regulated along the lines of the FCC Fairness Doctrine of 1949, but with a modern twist that speaks not only to the content itself but to the algorithms for amplifying and propagating it.

2. COVID-19 goes to brushfire mode. After raging like a uncontained wildfire in 2020, COVID should move to brushfire mode in 2021, slowing down in the spring and perhaps reaching pre-COVID “normal” in the fall, according to these predictions in UCSF Magazine. New variants are a wildcard and scientists are still trying to determine the extent to which existing vaccines slow or stop the B117 and 501.V2 variants.

According to this McKinsey report, the “transition towards normalcy is likely during the second quarter in the US,” though, depending on a number of factors, it’s possible that, “there may be a smaller fall wave of disease in third to fourth quarter 2021.”  In my estimation, the wildfire gets contained in 2Q21, with brush fires popping up with decreasing frequency throughout the year.

(Bear in mind, I went to the same school of armchair epidemiology as Dougall Merton, famous for his quote about spelling epidemiologist:  “there are three i’s in there and I swear they’re moving all the time.”)

3. The new normal isn’t. Do you think we’ll ever go into the office sick again? Heck, do you think we’ll ever go into the office again, period?  Will there even be an office?  (Did they renew that lease?)  Will shaking hands be an ongoing ritual? Or, in France, la bise?  How about those redeyes to close that big deal?  Will there still be 12-legged sales calls?  Live conferences?  Company kickoffs?  Live three-day quarterly business reviews (QBRs)?  Business dinners?  And, by the way, do you think everyone – finally – understands the importance of digital transformation?

I won’t do detailed predictions on each of these questions, and I have as much Zoom fatigue as the next person, but I think it’s important to realize the question is not “when we are we going back to the pre-COVID way of doing things?” and instead “what is the new way of doing things that we should move towards?”   COVID has challenged our assumptions and taught us a lot about how we do business. Those lessons will not be forgotten simply because they can be.

4.We start to value resilience, not just efficiency. For the past several decades we have worshipped efficiency in operations: just-in-time manufacturing, inventory reduction, real-time value chains, and heavy automation.  That efficiency often came at a cost in terms of resilience and flexibility and as this Bain report discusses, nowhere was that felt more than in supply chain.  From hand sanitizer to furniture to freezers to barbells – let alone toilet paper and N95 masks — we saw a huge number of businesses that couldn’t deal with demand spikes, forcing stock-outs for consumers, gray markets on eBay, and countless opportunities lost.  It’s as if we forget the lessons of the beer game developed by MIT.  The lesson:  efficiency can have a cost in terms of resilience and agility and I believe,  in an increasingly uncertain world, that businesses will seek both.

5. Work from home (WFH) sticks. Of the many changes COVID drove in the workplace, distributed organizations and WFH are the biggest. I was used to remote work for individual creative positions such as writer or software developer.  And tools from Slack to Zoom were already helping us with collaboration.  But some things were previously unimaginable to me, e.g., hiring someone who you’d never met in the flesh, running a purely digital user conference, or doing a QBR which I’d been trained (by the school of hard knocks) was a big, long, three-day meeting with a grueling agenda, with drinks and dinners thereafter.  I’d note that we were collectively smart enough to avoid paving cow paths, instead reinventing such meetings with the same goals, but radically different agendas that reflected the new constraints.  And we – or at least I in this case – learned that such reinvention was not only possible but, in many ways, produced a better, tighter meeting.

Such reinvention will be good for business in what’s now called The Future of Work software category such as my friends at boutique Future-of-Work-focused VCs like Acadian Ventures — who have even created a Bessemer-like Future of Work Global Index to track the performance of public companies in this space.

6. Tech flight happens, but with a positive effect. Much has been written about the flight from Silicon Valley because of the cost of living, California’s business-unfriendly policies, the mismanagement of San Francisco, and COVID. Many people now realize that if they can work from home, then why not do so from Park City, Atlanta, Raleigh, Madison, or Bend?  Better yet, why not work from home in a place with no state income taxes at all — like Las Vegas, Austin, or Miami?

Remember, at the end of the OB (original bubble), B2C meant “back to Cleveland” – though, at the time, the implication was that your job didn’t go with you.  This time it does.

The good news for those who leave:

  • Home affordability, for those who want the classic American dream (which now has a median price of $2.5M in Palo Alto).
  • Lower cost of living. I’ve had dinners in Myrtle Beach that cost less than breakfasts at the Rosewood.
  • Burgeoning tech scenes, so you don’t have go cold turkey from full immersion in the Bay Area. You can “step down,” into a burgeoning scene in a place like Miami, where Founder’s Fund partner Keith Rabois, joined by mayor Francis Suarez, is leading a crusade to turn Miami into the next hot tech hub.

But there also good news for those who stay:  house prices should flatten, commutes should improve, things will get a little bit less crazy — and you’ll get to keep the diversity of great employment options that leavers may find lacking.

Having grown up in the New York City suburbs, been educated on Michael Porter, and worked both inside and outside of the industry hub in Silicon Valley, I feel like the answer here is kind of obvious:  yes, there will be flight from the high cost hub, but the brain of system will remain in the hub.  So it went with New York and financial services, it will go with Silicon Valley and tech.  Yes, it will disperse.  Yes, certainly, lower cost and/or more staffy functions will be moved out (to the benefit of both employers and employees).  Yes, secondary hubs will emerge, particularly around great universities.  But most of the VCs, the capital, the entrepreneurs, the executive staff, will still orbit around Silicon Valley for a long time.

7. Tech bubble relents. As an investor, I try to never bet against bubbles via shorts or puts because “being right long term” is too often a synonym for “being dead short term.” Seeing manias isn’t hard, but timing them is nearly impossible.  Sometimes change is structural – e.g., you can easily convince me that if perpetual-license-based software companies were worth 3-5x revenues that SaaS companies, due to their recurring nature, should be worth twice that.  The nature of the business changed, so why shouldn’t the multiple change with it?

Sometimes, it’s actually true that This Time is Different.   However, a lot of the time it’s not.  In this market, I smell tulips.  But I started smelling them over six months ago, and BVP Emerging Cloud Index is up over 30% in the meantime.  See my prior point about the difficultly of timing.

But I also believe in reversion to the mean.  See this chart by Jamin Ball, author of Clouded Judgement, that shows the median SaaS enterprise value (EV) to revenue ratio for the past six years.  The median has more than tripled, from around 5x to around 18x.  (And when I grew up 18x looked more like a price/earnings ratio than a price/revenue ratio.)

What accounts for this multiple expansion?  In my opinion, these are several of the factors:

  • Some is structural: recurring businesses are worth more than non-recurring businesses so that should expand software multiples, as discussed above.
  • Some is the quality of companies: in the past few years some truly exceptional businesses have gone public (e.g., Zoom).  If you argue that those high-quality businesses deserve higher multiples, having more of them in the basket will pull up the median.  (And the IPO bar is as high as it’s ever been.)
  • Some is future expectations, and the argument that the market for these companies is far bigger than we used to think. SaaS and product-led growth (PLG) are not only better operating models, but they actually increase TAM in the category.
  • Some is a hot market: multiples expand in frothy markets and/or bubbles.

My issue:  if you assume structure, quality, and expectations should rationally cause SaaS multiples to double (to 10), we are still trading at 80% above that level.  Ergo, there is 44% downside to an adjusted median-reversion of 10.  Who knows what’s going to happen and with what timing but, to quote Newton, what goes up (usually) must come down.  I’m not being bear-ish; just mean reversion-ish.

(Remember, this is spitballing.  I am not a financial advisor and don’t give financial advice.  See disclaimers and terms of use.)

8. Net dollar retention (NDR) becomes the top SaaS metric, driving companies towards consumption-based pricing and expansion-oriented contracts. While “it’s the annuity, stupid” has always been the core valuation driver for SaaS businesses, in recent years we’ve realized that there’s only one thing better than a stream of equal payments – a stream of increasing payments.  Hence NDR has been replacing churn and CAC as the headline SaaS metric on the logic of, “who cares how much it cost (CAC) and who cares how much leaks out (churn) if the overall bucket level is increasing 20% anyway?”  While that’s not bad shorthand for an investor, good operators should still watch CAC and gross churn carefully to understand the dynamics of the underlying business.

This is driving two changes in SaaS business, the first more obvious than the second:

  • Consumption-based pricing. As was passed down to me by the software elders, “always hook pricing to something that goes up.”  In the days of Moore’s Law, that was MIPS.  In the early days of SaaS, that was users (e.g., at Salesforce, number of salespeople).  Today, that’s consumption pricing a la Twilio or Snowflake.   The only catch in a pure consumption-based model is that consumption better go up, but smart salespeople can build in floors to protect against usage downturns.
  • Built-in expansion. SaaS companies who have historically executed with annual, fixed-fee contracts are increasingly building expansion into the initial contract.  After all, if NDR is becoming a headline metric and what gets measured gets managed, then it shouldn’t be surprising that companies are increasingly signing multi-year contracts of size 100 in year 1, 120 in year 2, and 140 in year 3.  (They need to be careful that usage rights are expanding accordingly, otherwise the auditors will flatten it back out to 120/year.)  Measuring this is a new challenge.  While it should get captured in remaining performance obligation (RPO), so do a lot of other things, so I’d personally break it out.  One company I work with calls it “pre-sold expansion,” which is tracked in aggregate and broken out as a line item in the annual budget.

See my SaaStr 2020 talk, Churn is Dead, Long Live Net Dollar Retention, for more information on NDR and a primer on other SaaS metrics.  Video here.

9. Data intelligence happens. I spent a lot of time with Alation in 2020, interim gigging as CMO for a few quarters. During that time, I not only had a lot of fun and worked with great customers and teammates, I also learned a lot about the evolving market space.

I’d been historically wary of all things metadata; my joke back in the day was that “meta-data presented the opportunity to make meta-money.”  In the old days just getting the data was the problem — you didn’t have 10 sources to choose from, who cared where it came from or what happened to it along the way, and what rules (and there weren’t many back then) applied to it.  Those days are no more.

I also confess I’ve always found the space confusing.  Think:

Wait, does “MDM” stand for master data management or metadata management, and how does that relate to data lineage and data integration?  Is master data management domain-specific or infrastructure, is it real-time or post hoc?  What is data privacy again?  Data quality?  Data profiling?  Data stewardship?  Data preparation, and didn’t ETL already do that?  And when did ETL become ELT?  What’s data ops?  And if that’s not all confusing enough, why do I hear like 5 different definitions of data governance and how does that relate to compliance and privacy?”

To quote Edward R. Murrow, “anyone who isn’t confused really doesn’t understand the situation.”

After angel investing in data catalog pioneer Alation in 2013, joining their board in 2016, and joining the board of master data management leader Profisee in 2019, I was determined to finally understand the space.  In so doing, I’ve come to the conclusion that the vision of what IDC calls data intelligence is going to happen.

Conceptually, you can think of DI as the necessary underpinning for both business intelligence (BI) and artificial intelligence (AI).  In fact, AI increases the need for DI.  Why?  Because BI is human-operated.  An analyst using a reporting or visualization tool who sees bad or anomalous data is likely going to notice.  An algorithm won’t.  As we used to say with BI, “garbage in, garbage out.”  That’s true with AI as well, even more so.  Worse yet, AI also suffers from “bias in, bias out” but that’s a different conversation.

I think data intelligence will increasingly coalesce around platforms to bring some needed order to the space.  I think data catalogs, while originally designed for search and discovery, serve as excellent user-first platforms for bringing together a wide variety of data intelligence use cases including data search and discovery, data literacy, and data governance.  I look forward to watching Alation pursue, with a hat tip to Marshall McLuhan, their strategy of “the catalog is the platform.”

Independent of that transformation, I look forward to seeing Profisee continue to drive their multi-domain master data management strategy that ultimately results in cleaner upstream data in the first place for both operational and analytical systems.

It should be a great year for data.

10. Rebirth of Planning and Enterprise Performance Management (EPM). EPM 1.0 was Hyperion, Arbor, and TM1. EPM 2.0 was Adaptive Insights, Anaplan, and Planful (nee Host Analytics).  EPM 3.0 is being born today.  If you’ve not been tracking this, here a list of next-generation planning startups that I know (and for transparency my relationship with them, if any.)

Planning is literally being reborn before our eyes, in most cases using modern infrastructure, product-led growth strategies, stronger end-user focus and design-orientation, and often with a functional, vertical, or departmental twist.  2021 will be a great year for this space as these companies grow and put down roots.  (Also, see the follow-up post I did on this prediction.)

Well, that’s it for this year’s list.  Thanks for reading this far and have a healthy, safe, and Rule-of-40-compliant 2021.

Kellblog’s 10 Predictions for 2020

As I’ve been doing every year since 2014, I thought I’d take some time to write some predictions for 2020, but not without first doing a review of my predictions for 2019.  Lest you take any of these too seriously, I suggest you look at my batting average and disclaimers.

Kellblog 2019 Predictions Review

1.  Fred Wilson is right, Trump will not be president at the end of 2019.  PARTIAL.  He did get impeached after all, but that’s a long way from removed or resigned. 

2.  The Democratic Party will continue to bungle the playing of its relatively simple hand.  HIT.  This is obviously subjective and while I think they got some things right (e.g., delaying impeachment), they got others quite wrong (e.g., Mueller Report messaging), and continue to play more left than center which I believe is a mistake.

3.  2019 will be a rough year for the financial markets.  MISS.  The Dow was up 22% and the NASDAQ was up 35%.  Financially, maybe the only thing that didn’t work in 2019 were over-hyped IPOs.  Note to self:  avoid quantitative predictions if you don’t want to risk ending up very wrong.  I am a big believer in regression to the mean, but nailing timing is the critical (and virtually impossible) part.  Nevertheless, I do use tables like these to try and eyeball situations where it seems a correction is needed.  Take your own crack at it.

4.  VC tightens.  MISS.  Instead of tightening, VC financing hit a new record.  The interesting question here is whether mean reversion is relevant.  I’d argue it’s not – the markets have changed structurally such that companies are staying private far longer and thus living off venture capital (and/or growth-stage private equity) in ways not previously seen.  Mark Suster did a great presentation on this, Is VC Still a Thing, where he explains these and other changes in VC.  A must read.

5. Social media companies get regulated.  PARTIAL.  While “history may tell us the social media regulation is inevitable,” it didn’t happen in 2019.  However, the movement continued to gather steam with many Democratic presidential candidates calling for reform and, more notably, none other than Facebook investor Roger McNamee launching his attack on social media via his book Zucked: Waking Up To The Facebook Catastrophe.  As McNamee says, “it’s an issue of ‘right vs. wrong,’ not ‘right vs. left.’”


6. Ethics make a comeback.  HIT.  Ethics have certainly been more discussed than ever and related to the two reasons I cited:  the current administration and artificial intelligence.  The former forces ethics into the spotlight on a daily basis; the later provokes a slew of interesting questions, from questions of accidental bias to the trolley car problem.  Business schools continue to increase emphasis on ethics.  Mark Benioff has led a personal crusade calling for what he calls a new capitalism.

7.  Blockchain, as an enterprise technology, fades away.  HIT.  While I hate to my find myself on the other side of Ray Wang, I’m personally not seeing much traction for blockchain in the enterprise.  Maybe I’m running with the wrong crowd.  I have always felt that blockchain was designed for one purpose (to support cybercurrency), hijacked to another, and ergo became a vendor-led technology in search of a business problem.  McKinsey has a written a sort of pre-obituary, Blockchain’s Occam Problem, which was McKinsey Quarterly’s second most-read article of the year.  The 2019 Blockchain Opportunity Summit’s theme was Is Blockchain Dead?  No. Industry Experts Join Together to Share How We Might Not be Using it Right which also seems to support my argument. 

8.  Oracle enters decline phase and is increasingly seen as a legacy vendor.  HIT.  Again, this is highly subjective and some people probably concluded it years ago.  My favorite support point comes from a recent financial analyst note:  “we believe Oracle can sustain ~2% constant currency revenue growth, but we are dubious that Oracle can improve revenue growth rates.”  That pretty much says it all.

9.  ServiceNow and/or Splunk get acquired.  MISS.  While they’re both great businesses and attractive targets, they are both so expensive only a few could make the move – and no one did.  Today, Splunk is worth $24B and ServiceNow a whopping $55B.

10.  Workday succeeds with its Adaptive Insights agenda.  HIT.  Changing general ledgers is a heart transplant while changing planning systems is a knee replacement.  By acquiring Adaptive, Workday gave itself another option – and a far easier entry point – to get into corporate finance departments.  While most everyone I knew scratched their head at the enterprise-focused Workday acquiring a more SMB-focused Adaptive, Workday has done a good job simultaneously leaving Adaptive alone-enough to not disturb its core business while working to get the technology more enterprise-ready for its customers.  Whether that continues I don’t know, but for the first 18 months at least, they haven’t blown it.  This remains high visibility to Workday as evidenced by the Adaptive former CEO (and now Workday EVP of Planning) Tom Bogan’s continued attendance on Workday’s quarterly earnings calls.

With the dubious distinction of having charitably self-scored a 6.0 on my 2019 predictions, let’s fearlessly roll out some new predictions for 2020.

Kellblog 2020 Predictions

1.  Ongoing social unrest. The increasingly likely trial in the Senate will be highly contentious, only to be followed by an election that will be highly contentious as well.  Beyond that, one can’t help but wonder if a defeated Trump would even concede, which could lead to a Constitutional Crisis of the next level. Add to all that the possibility of a war with Iran.  Frankly, I am amazed that the Washington, DC continuous distraction machine hasn’t yet materially damaged the economy.  Like many in Silicon Valley, I’d like Washington to quietly go do its job and let the rest of us get back to doing ours.  The reality TV show in Washington is getting old and, happily, I think many folks are starting to lose interest and want to change the channel.

2.  A desire for re-unification.  I remain fundamentally optimistic that your average American – Republican, Democrat, or the completely under-discussed 38% who are Independents — wants to feel part of a unified, not a divided, America.  While politicians often try to leverage the most divisive issues to turn people into single-issue voters, the reality is that far more things unite us as Americans than divide us.  Per this recent Economist/YouGov wide-ranging poll, your average American looks a lot more balanced and reasonable than our political party leaders.  I believe the country is tired of division, wants unification, and will therefore elect someone who will be seen as able to bring people together.  We are stronger together.

3.  Climate change becomes the new moonshot.  NASA’s space missions didn’t just get us to the moon; they produced over 2,000 spin-off technologies that improve our lives every day – from emergency “space” blankets to scratch-resistant lenses to Teflon-coated fabrics.  Instead of seeing climate change as a hopeless threat, I believe in 2020 we will start to reframe it as the great opportunity it presents.  When we mobilize our best and brightest against a problem, we will not only solve it, but we will create scores to hundreds of spin-off technologies that will benefit our everyday lives in the process.  See this article for information on 10 startups fighting climate change, this infographic for an overview of the kinds of technologies that could alleviate it, or this article for a less sanguine view on the commitment required and extent to which we actually can de-carbonize the air. Or check out this startup which makes “trees” that consume the pollution of 275 regular trees.

4.  The strategic chief data officer (CDO).  I’m not a huge believer in throwing an “O” at every problem that comes along, but the CDO role is steadily becoming mainstream – in 2012 just 12% of F1000 companies reported having a CDO; in 2018 that’s up to 68%.  While some of that growth was driven by defensive motivations (e.g., compliance), increasingly I believe that organizations will define the CDO more strategically, more broadly, and holistically as someone who focuses on data, its cleanliness, where to find it, where it came from, its compliance with regulations as to its usage, its value, and how to leverage it for operational and strategic advantage.   These issues are thorny, technical, and often detail-oriented and the CIO is simply too busy with broader concerns (e.g., digital transformation, security, disruption).  Ergo, we need a new generation of chief data officers who want to play both offense and defense, focused not just tactically on compliance and documentation, but strategically on analytics and the creation of business value for the enterprise. This is not a role for the meek; only half of CDOs succeed and their average tenure is 2.4 years.  A recent Gartner CDO study suggests that those who are successful take a more strategic orientation, invest in a more hands-on model of supporting data and analytics, and measure the business value of their work.

5.  The ongoing rise of DevOps.   Just as agile broke down barriers between product management and development so has DevOps broken down walls between development and operations.  The cloud has driven DevOps to become one of the hottest areas of software in recent years with big public company successes (e.g., Atlassian, Splunk), major M&A (e.g., Microsoft acquiring GitHub), and private high-flyers (e.g., HashiCorp, Puppet, CloudBees).  A plethora of tools, from configuration management to testing to automation to integration to deployment to multi-cloud to performance monitoring are required to do DevOps well.  All this should make for a $24B DevOps TAM by 2023 per a recent Cowen & Company report.  Ironically though, each step forward in deployment is often a step backward in developer experience.

6. Database proliferation slows.  While 2014 Turning Award winner Mike Stonebraker was right over a decade ago when he argued in favor of database specialization (One Size Fits All:  An Idea Whose Time Has Come and Gone), I think we may now too much of a good thing.   DB Engines now lists 350 different database systems of 14 different types (e.g., relational, graph, time series, key-value). Crunchbase lists 274 database (and database-related) startups.  I believe the database market is headed for consolidation.  One of the first big indicators of a resurgence in database sanity was the failure of the (Hadoop-based) data lake, which happened in 2018-2019 and was the closest thing I’ve seen to déjà vu in my professional career – it was as if we learned nothing from the Field of Dreams enterprise data warehouse of the 1990s (“build it and they will come”).  Moreover, after a decade of developer-led database selection, developers and now re-realizing what database people knew along – that a lot of the early NoSQL movement was akin to throwing out the ACID transaction baby with the tabular schema bathwater.

7.  A new, data-layer approach to data loss prevention (DLP).  I always thought DLP was a great idea, especially the P for prevention.  After all, who wants tools that can help with forensics after a breach if you could prevent one from happening at all — or at least limit one in progress?  But DLP doesn’t seem to work:  why is it that data breaches always seem to be measured not in rows, but in millions of rows?  For example, Equifax was 143M and Marriott was 500M.  DLP has many known limitations.  It’s perimeter-oriented in a hybrid cloud world of dissolving perimeters and it’s generally offline, scanning file systems and database logs to find “misplaced data.”  Wouldn’t a better approach be to have real-time security monitored and enforced at the data layer, just the same way as it works at the network and application layer?  Then you could use machine learning to understand normal behavior, detect anomalous behavior, and either report it — or stop it — in real time.  I think we’ll see such approaches come to market in 2020, especially as cloud services like Snowflake, RDS, and BigQuery become increasingly critical components of the data layer.

8. AI/ML continue to see success in highly focused applications.  I remain skeptical of vendors with broad claims around “enterprise AI” and remain highly supportive of vendors applying AI/ML to specific problems (e.g., Moveworks and Astound who both provide AI/ML-based trouble-ticket resolution).  In the end, AI and ML are features, not apps, and while both technologies can be used to build smart applications, they are not applications unto themselves.  In terms of specificity, the No Free Lunch Theorem reminds us that any two optimization techniques perform equivalently when averaged across all possible problems – meaning that no one modeling technique can solve everything and thus that AI/ML is going to be about lots of companies applying different techniques to different problems.   Think of AI/ML more as a toolbox than a platform.  There will not be one big winner in enterprise AI as there was in enterprise applications or databases.  Instead, there will be lots of winners each tackling specific problems.  The more interesting battles will those between systems of intelligence (e.g., Moveworks) and systems of record (e.g., ServiceNow) with the systems-of-intelligence vendors running Trojan Horse strategies against systems-of-record vendors (first complementing but eventually replacing them) while the system-of-record vendors try to either build or acquire systems of intelligence alongside their current offerings. 

9.  Series A rounds remain hard.  I think many founders are surprised by the difficulty of raising A rounds these days.  Here’s the problem in a nutshell:

  • Seed capital is readily available via pre-seed and seed-stage investments from angel investors, traditional early-stage VCs, and increasingly, seed funds.  Simply put, it’s not that hard to raise seed money.
  • Companies are staying in the seed stage longer (a median of 1.6 years), increasingly extending seed rounds, and ergo raising more money during seed stage (e.g., $2M to $4M).
  • Such that, companies are now expected to really have achieved something in order to raise a Series A.  After all, if you have been working for 2 years and spent $3M you better have an MVP product, a handful of early customers, and some ARR to show for it – not just a slide deck talking about a great opportunity.

Moreover, you should be making progress roughly in line with what you said at the outset and, if you took seed capital from a traditional VC, then they better be prepared to lead your round otherwise you will face signaling risk that could imperil your Series A.

Simply put, Series A is the new chokepoint.  Or, as Suster likes to say, the Series A and B funnel hasn’t really changed – we’ve just inserted a new seed funnel atop it that is 3 times larger than it used to be.

10.  Autonomy’s former CEO gets extradited.  Silicon Valley is generally not a place of long memories, but I saw the unusual news last month that the US government is trying to extradite Autonomy founder and former CEO Mike Lynch from the UK to face charges.  You might recall that HP, in the brief era under Leo Apotheker, acquired enterprise search vendor Autonomy in August, 2011 for a whopping $11B only to write off about $8.8B under subsequent CEO Meg Whitman a little more than a year later in November, 2012.  Computerworld provides a timeline of the saga here, including a subsequent PR war, US Department of Justice probe, UK Serious Fraud Office investigation (later dropped), shareholder lawsuits, proposed settlements, more lawsuits including Lynch’s suing HP for $150M for reputation damages, and HP’s spinning-off the Autonomy assets.  Subsequent to Computerworld’s timeline, this past May Autonomy’s former CFO was sentenced to five years in prison.  This past March, the US added criminal charges of securities fraud, wire fraud, and conspiracy against Lynch.  Lynch continues to deny all wrongdoing, blames the failed acquisition on HP, and even maintains a website to present his point of view on the issues.  I don’t have any special legal knowledge or specific knowledge of this case, but I do believe that if the US government is still fighting this case, still adding charges, and now seeking extradition, that they aren’t going to give up lightly, so my hunch is that Lynch does come to the US and face these charges. 

More broadly, regardless of how this particular case works out, in a place so prone to excess, where so much money can be made so quickly, frauds will periodically happen and it’s probably the most under-reported class of story in Silicon Valley.  Even this potentially huge headline case – the proposed extradition of a British billionaire tech mogul —  never seems to make page one news.  Hey, let’s talk about something positive like Loft’s $175M Series C instead.

To finish this up, I’ll add a bonus prediction:  Dave doesn’t get a traditional job in 2020.  While I continue to look at VC-backed startup and/or PE-backed CEO opportunities, I am quite enjoying my work doing a mix of boards, advisory relationships, and consulting gigs.  While I remain interested in looking at great CEO opportunities, I am also interested in adding a few more boards to my roster, working on stimulating consulting projects, and a few more advisory relationships as well.

I wish everyone a happy, healthy, and above-plan 2020.

Kellblog Predictions for 2019

Because I’ve been quite busy of late with the sale of my company, I’m doing a somewhat quicker and lighter (if not later) version of my annual predictions post.  Here goes, starting with a review of last year’s predictions.

2018 Kellblog Predictions Review

1. We will again continue to see a level of divisiveness and social discord not seen since the 1960s. HIT.  Hard to argue I need to justify this one.  Want to argue about it?

2. The war on facts and expertise will continue to escalate. HIT. Unfortunately, the President is leading the charge on this front, with the Washington Post fact checker tallying 7,645 false claims since taking office.


3. Leading technology and social media companies finally step up to face ethical challenges. MAJOR MISS.  Well, I nailed that the issue would be critical, but boy did I overestimate the maturity of the management of these companies.

4. AI will move from hype to action, meaning bigger budgets, more projects, and some high visibility failures. HIT, I think.  See this McKinsey report for some interesting survey data on AI adoption and barriers to it.

5. AI will continue to generate lots of controversy about job displacement. HIT. While the optimists say AI will create more jobs than it will displace, many still worry conversely.  Since the prediction was about the controversy continuing, we’ll call it a hit.

6. The bitcoin bubble bursts. MAJOR HIT.  This one partially redeems me for over-estimating Facebook’s management.


7. The Internet of Things (IoT) will continue to build momentum.  HIT. See this Forbes article about data from Dresner Advisory’s 2018 IoT Intelligence Market Study.

8. The freelance / gig economy continues to gain momentum with freelance workers poised to pass traditional employees by 2027. HIT.  Per this Forbes article, 57M people now participate in the gig economy in some way.

9. M&A heats up due to repatriation of overseas cash.  HIT. Per Berkery Noyes, software M&A deal value was up nearly $100B over 2017.  To the extent this was due to overseas cash repatriation I don’t know, but it certainly was a factor.


10. 2018 will be a good year for cloud EPM vendors. MAJOR HIT.  Anaplan went public, Adaptive Insights was acquired by Workday, and Host Analytics was acquired by Vector Capital. 

With 9 hits, two of them major – and with only one offsetting major miss — I should probably just drop the mike and get out of the predictions business.  But no guts, no glory.

Kellblog’s 2019 Predictions

Reminder to see the disclaimers in my FAQ and remember that these predictions are not financial or business advice – they are made in the spirit of fun.  To the extent they’re concrete, that’s to make the game more interesting so we can better assess them next year.  Here we go.

1. Fred Wilson is right, Trump will not be president at the end of 2019. I think Fred’s also right on virtually all of the other predictions made in his epic post, which I won’t attempt to summarize here. Read Fred’s post – and just make sure you read to the end, because it’s not all doom and gloom.  So, as a Kellblog first, prediction #1 is a pointer.

2. The Democratic Party will continue to bungle the playing of its relatively simple hand. Party leaders will continue to fail to realize that the way to beat Trump is not through a hard-left platform with 70% tax rates that caters to the most liberal Democrats – but a centrist, pragmatic, people- and business-friendly platform that certainly won’t be enough for the far left, but will be far better than the Republican alternative for all Democrats, and most importantly, give centrist Republicans a realistic alternative to what their party is offering them.  The Democratic Party will continue to be more concerned with making statements than winning elections.  This may cost it, and the Nation, dearly.

Remember the famous Will Rodgers quote: “I am not a member of any organized political party.  I am a Democrat.”

 3. 2019 will be a rough year for the financial markets. Political problems in the USA, Europe, and increasingly Latin American.  Trade wars.  Record deficits as we re-discover that trickle-down, tax-cut economics don’t work.  Threat of rising interest rates.   Brexit.   Many folks see a bear market coming.

Years ago, I accepted the fact that – like many – I am a hypocrite when it comes to the stock market.  Yes, I absolutely believe that it’s theoretically impossible to time the market.   But yes, I’m entering 2019 with a high allocation to cash and intend to keep it that way.  Hum.  Try to reconcile that.

For fun, let’s makes this concrete and predict that the BVP Emerging Cloud Index will end 2019 at 750.  I do this mostly to provide some PR for Bessemer’s Index, officially launched via the NASDAQ in October, 2018, but which was built on the back of five years of Bessemer maintaining it themselves.

4. VC tightens. Venture capital funding has been booming the past several years and – for the above reasons and others (e.g., the fact that most VCs don’t product enough returns to justify the risk and illiquidity) – I believe there will be tightening of VC in 2019.  If you agree, that means you should raise money now, while the sun’s still shining, and try to raise two years of capital required in your business plan (with some cushion).


If things follow the recent trends, this will be hardest on average and/or struggling companies as VCs increasingly try to pick winners and make bets conservative in the sense that they are on known winners, even if they have to overpay to do so.  In this scenario, capital on reasonable terms could all but dry up for companies who have gone off-rails on their business plans.   So, if you’re still on rails, you might raise some extra capital now.  Getting greedy by trying to put up two more good quarters to take less dilution on your next round could backfire – you might miss one of those quarters in this increasingly volatile environment, but even if you don’t, VC market tightening could offset any potential valuation increase.

5. Social media companies get regulated. Having failed for years to self-regulate in areas of data privacy and usage, these companies will likely to face regulations in 2019 in the face of strong consumer backlash.  The first real clue I personally had in this area was during the 2016 election when Facebook didn’t just feed me, but actually promoted, a fake Denver Guardian story about a supposedly dead FBI agent linked to “her emails.”  I then read the now-famous “bullshit is highly engaging” quote from this story which helped reveal the depth of the problem:

Or, as former Facebook designer Bobby Goodlatte wrote on his own Facebook wall on November 8, “Sadly, News Feed optimizes for engagement. As we’ve learned in this election, bullshit is highly engaging. A bias towards truth isn’t an impossible goal. Wikipedia, for instance, still bends towards the truth despite a massive audience. But it’s now clear that democracy suffers if our news environment incentivizes bullshit.”

I won’t dive into detail here.  I do think Sheryl Sandberg may end up leaving Facebook; she was supposed to be the adult supervision, after all.  While I think he’s often a bit too much, I nevertheless recommend reading Chaos Monkeys for an interesting and, at times, hilarious insider look at Facebook and/or following its author Antonio Garcia Martinez.

6. Ethics make a comeback, for two reasons.  The first will be as a backlash to the blatant corruption of the current administration.  To wit:  the House recently passed a measure requiring annual ethics training for its members.  The second will have to do with AI and automation.  The Trolley Problem, once a theoretical exercise in ethics, is now all too real with self-driving cars.  Consider this data, based on MIT research in this article which shows preferences for sparing various characters in the event of a crash.


Someone will probably end up programming such preferences into a self-driving car.  Or, worse yet, as per the Trolley Problem, maybe they won’t.  While we may want to avoid these issues because they are uncomfortable, in 2019 I think they will be thrust onto center stage.

7. Blockchain, as an enterprise technology, fades away. Blockchain is a technology in search of a killer application.  Well, it actually has one killer application, cryptocurrency, which is why it was built.  And while I am a fan of cybercurrencies, blockchain is arguably inefficient at what it was built to do.  While Bitcoin will not take down the world electric grid as some have feared, it is still tremendously energy consumptive –in coming years, Bitcoin is tracking to consume 7.7 GW per year, comparable to the entire country of Austria at 8.2 GW.

While I’m not an expert in this field, I see three things that given me huge pause when it comes to blockchain in the enterprise:  (1) it’s hard to understand, (2) it consumes a huge amount of energy, and (3) people have been saying for too long that the second blockchain killer app (and first enterprise blockchain killer app) is just around the corner.  Think:  technology in search of a business problem.  What’s more, even for its core use-case, cryptocurrency, blockchain is vulnerable to being cracked by quantum computing by 2027.

8. Oracle enters decline phase and is increasingly seen as a legacy vendor. For decades I have personally seen Oracle as a leader.  First, in building the RDBMS market.  Second, in consolidating a big piece of the enterprise applications market.  Third, more generally, in consolidating enterprise software.  But, in my mind, Oracle is no longer a leader.  Perhaps you felt this way long ago.  I’d given them a lot of credit for their efforts (if not their progress) in the cloud – certainly better than SAP’s or IBM’s.  But SAP and IBM are not the competitors to beat in the future:  Amazon, Google, and a rejuvenated Microsoft are.  The reality is that Oracle misses quarters, cloud-washes sales, and is basically stagnant in revenue growth.  They have no vision.  They have become a legacy vendor.

The final piece of this snapped into place when Thomas Kurian departed to Google in a dispute with Larry Ellison about the cloud.  DEC’s Ken Olsen once said that Unix was “snake oil” and that was the beginning of the end for DEC.  Ellison once said roughly the same thing (“complete gibberish”) about the cloud.  And now the cloud is laughing back.

9. ServiceNow and/or Splunk get acquired. A friend of mine planted this seed in my mind and it’s more about corporate evolution than anything else.  They’re both great businesses that mega-vendors would love to own – especially if they end up “on sale” if we hit a bear market.

10. Workday succeeds with its Adaptive Insights agenda, meaning that Adaptive’s mid-market and SMB presence will be greatly lessened.   Most people I know think Workday’s acquisition of Adaptive was a head-scratcher.  Yes, Workday struggles in financial apps.  Yes, EPM is an easier entry point than core financials (which, as Zach Nelson used to say, were like a heart transplant).  But why in the world would a high-end vendor (with average revenue/customer of $1M+) acquire a low-end EPM vendor (with average revenue/customer of $27K)?  That’s hard to figure out.

But just because the acquisition was, to be kind, non-obvious, it doesn’t mean Workday won’t be successful with it.  Workday’s goals are clear: (1) to unite Adaptive with Workday in The Power of One – including re-platforming the backend and re-writing the user-interface, (2) to provide EPM to Workday’s high-end customer base, and (3) to provide an alternate financial entry point for sales when prospects say they’re not up for a heart transplant for at least 5 years.  I’m not saying Workday can’t be successful with their objectives.  I am saying Adaptive won’t be Adaptive when they’re done — you can’t be the high-end, low-end, cheap, expensive, simple, complex, agnostic, integrated EPM system.   Or, as SNL put it, you can’t be Shimmer — a dessert topping and a floor wax.  The net result:   like Platfora before them or Outlooksoft within SAP, Adaptive disappears within Workday and its presence in the mid-market and SMB is greatly reduced.

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Disclaimer:  these predictions are offered in the spirit of fun.  See my FAQ for more and other terms of use.