Jason Lemkin on Finding SaaS Investments, Founder Feedback and the Algolia Bet

Harry Stebbings welcomes SaaStr founder and investor Jason Lemkin back seven years after their first conversation. Lemkin says their long relationship illustrates how what looks like overnight success often takes years. They use his investment in search company Algolia to examine how he finds and supports founders.

Lemkin says his investments have come through inbound approaches from enthusiastic SaaStr followers, rather than outbound hunting or warm introductions. That is a personal approach, not a universal rule. A founder who knows his work can explain why they want him involved and provide enough information for him to respond quickly.

The filter has real costs. Lemkin later discovered that monday.com founders had emailed him, but their brief invitation to meet did not explain the company and he missed it. His best founders have often been strong communicators, he says, but judging an opportunity by an email can also exclude exceptional businesses.

His preferred cold email is detailed: what the company does, monthly recurring revenue, growth, retention, customers, team and why it can win. He wants enough substance to be excited before meeting. Other investors told him they prefer two lines or a detailed deck, so founders should not assume one investor’s format suits everyone.

Algolia’s founders contacted him after Y Combinator’s demo day in 2014, with a round already full. He recognized their search-as-a-service problem from painful experience: at his own company search servers repeatedly went down. A colleague rescued the missed email from his inbox, and he met the founders before they returned to France.

After a short meeting he wanted as much of the round as he could get. The allocation was rearranged, and he invested $500,000 at a $12 million pre-money valuation. He later invested $3.5 million in the Series A. These are historical deal terms and Lemkin has a direct financial interest in Algolia’s success.

He had been offered a choice between $500,000 at $12 million pre-money and $1 million at $15 million. As a new investor worried about valuation and loss, he chose the smaller check. In retrospect he wishes he had bought more ownership, while acknowledging that the original choice had a logic if a modest exit was the expected outcome.

Lemkin says learning to accept losses took years. As a founder he had worried intensely about losing his investors’ money; as an investor, seeing large paper gains helped him understand how venture portfolios can absorb individual failures. He describes his own adjustment to risk, not a guarantee that any specific loss will be recovered.

Today he says he generally finds less than 10% ownership hard to justify for his solo fund, although he makes exceptions for founders, relationships and portfolio inclusion. He aims for at least one position of 20% or more in a group of investments. Stebbings asks whether this risks adverse selection; Lemkin believes the best founders may have several options and still choose a particular investor.

A founder unfamiliar with SaaStr is less likely, in Lemkin’s experience, to pick him for a lasting reason. He acknowledges that his narrow funnel will miss opportunities. His fund model relies on recognizing the occasional exceptional founder who chooses him and acquiring meaningful ownership, a strategy tied to his own fund size and circumstances.

His early operator experience shaped the problems he chose to back. At EchoSign, later Adobe Sign, search, contact centers, CRM and sales outreach had been persistent headaches. He invested in businesses including Algolia, Talkdesk, Pipedrive and Salesloft; those are also examples in which he has an investor’s stake or history.

Problems that worked well for him were harder to understand as investment opportunities. He recalls missing the appeal of payroll because his own payroll had worked, while he recognized recruiting problems. For a founder turning investor, he recommends considering familiar problems early, when personal experience makes it easier to evaluate a proposed solution.

That advantage fades, in Lemkin’s view: an investor may remember a problem yet struggle to recognize the next generation of solutions after leaving an operating role. He suggests curiosity, a team closer to current users and continued operational work can help, but does not claim they fully erase the gap.

Running SaaStr and its community keeps him close to some sales, marketing and management problems. At the time he projected roughly $40 million for SaaStr that year and described working with many marketing leaders. His knowledge is still concentrated in a particular set of problems rather than the whole software stack.

For operators moving into venture, Lemkin argues against automatically waiting a year to invest. Fund partners and new investors may have different incentives: a new investor needs a track record, while an established firm may prefer fewer risks. His recommendation is to pursue strong opportunities in areas where the operator has current insight.

He also looks for founders he believes are better than he was at the same problem. He judged Algolia’s team more knowledgeable and its early traction stronger than his own had been. His emphatic claim that such investments cannot lose is his personal conviction, not a reliable prediction of investment outcomes.

Competing with a free, high-quality open-source alternative is difficult for a founder, but Lemkin sees early paid traction against it as a potentially strong signal. Customers must find some use case compelling enough to pay. That does not mean the paid product is best for every customer or that early traction guarantees an enduring advantage.

He dislikes generic competition slides that place the startup alone in the top right of an arbitrary matrix. A founder should instead show a serious understanding of rivals and explain where each product is stronger. He admired Algolia’s comparison with Elasticsearch because it described where Algolia won and where prospective customers should use the alternative.

Stebbings worries about products that become commodities and compete only on price. Lemkin shares the concern but warns that venture heuristics can make investors miss exceptional companies. He sees Stripe as an example of an elegant API and surrounding functionality that made a seemingly commoditized market more differentiated.

Another lazy question, he says, is simply why a large incumbent would not build the product. Salesforce might appear an obvious competitor to sales-engagement startups, but that alone did not explain their opportunity. A better question asks founders to explain what the incumbent has done, why the gap remains and how the startup can build on it.

Lemkin thinks speed of high-quality iteration compounds over years. A team shipping improvements faster can expand its lead through integrations and features while slower competitors struggle to close gaps. He contrasts products that continually improve with once-popular tools that, in his view, failed to renew themselves.

Speed matters before product-market fit too, because early releases generate learning. He speculates that even products remembered for long development likely went through many tests, while acknowledging he does not know the details. At EchoSign he pushed a launch earlier than his team wanted; he calls that a mistake, but wanted enough runway for several iterations.

Asked about companies that raised very large rounds before product-market fit, Lemkin says he lacks enough direct experience to know their eventual fate. He worries that long runways can create stagnant “zombies” with little urgency. Stebbings describes investors offering founders cash to return remaining capital; neither presents that anecdote as a representative market statistic.

The Algolia investment also challenged a conventional market-size calculation. Lemkin recalls identifying only a very small amount of revenue among direct search competitors at the time. He reasoned that Algolia’s fast monthly growth, although based on a small revenue base, could not continue for long if that narrow estimate represented the entire addressable market.

He believes products can expand a category as technology changes what customers buy, and uses early growth as evidence of possible market breadth. That is an investment heuristic, not proof: he acknowledges that growth can decay and early numbers may be statistically fragile. He contrasts this with treating an existing market-size estimate as a fixed ceiling.

Lemkin says his successful portfolio companies have experienced periods of plateau. In his account, one recurring problem is waiting too long to build or refresh a management team. If a first set of senior hires fails, a company that started early has more time to correct it before delays compound.

What looks like a product or market problem may reflect leadership’s ability to expand the addressable market, move upmarket or build a second product. He recalls Algolia waiting until a substantial revenue scale before building its first management team and says the group did not work out. He describes this as a difficult period for the founders.

His first investment, Pipedrive, taught him not to imitate the investor behavior he had disliked as a founder. He recalls being overly prescriptive and less collaborative than he should have been. On Algolia he was not initially on the board, which he says helped him develop a different relationship with the founders.

Investors still need to give difficult advice, he says, especially when a company faces an urgent risk, but the founders must ultimately run the business. Stebbings objects to investors withholding uncomfortable views for fear of looking bad. Lemkin agrees that silence has costs while warning that direct criticism can permanently strain trust.

Lemkin recalls a founder who later thanked him for a hard conversation and another board discussion in which he warned that an unsustainable burn rate required action within days. The latter founders remained upset with him. His rough percentages about who accepts feedback are impressions, not measured survey results.

Stebbings argues that leaders who cannot take hard feedback will struggle with demanding enterprise customers and employees. Lemkin responds that a board member’s relationship with a CEO is unusually intermittent and nuanced. He is unsure whether his blunt approach is always right, even when he believes waiting months to raise a serious issue could be too slow.

Algolia was initially opposed by the rest of his investment partnership, while his next investment, Talkdesk, was quickly unanimous. Lemkin warns that “conviction” can excuse sloppy analysis, but says his direct experience with search made him willing to push for Algolia despite colleagues’ concerns about the market and other risks.

On investment partnerships, he distinguishes a genuine close partnership from a large group of general partners managing a large fund. He believes groups larger than two can lose some of the benefits of shared decision-making, though fund size may require them. This is a personal assessment, not a universal rule for partnership design.

Lemkin regrets not finding a true investing co-founder. Established peers might have had too much to leave behind, while younger investors he worked with pursued their own paths. He believes a complementary partner would have made him a better investor, even though the SaaStr community keeps his work from feeling lonely.

The pair discuss whether limited partners would withdraw from venture after the market downturn. Lemkin says his own established institutional backers had largely remained committed across funds, although individual managers could be dropped. His expectation was that experienced institutions would generally stay in the asset class; it was a prediction made at the time, not a current market finding.

He criticizes the casual use of very high return targets during the boom. In his view, a three-times net fund return had historically been difficult and attractive, and he expects it to become rare again. His examples of fund returns and comparisons with other assets are conversational claims, not independently established benchmarks.

An early meeting with an institutional allocator taught him that the investor might look first at returns and asset-class fit rather than an elaborate strategy deck. Emerging managers had recently attracted more attention, he says, but he speculates that tighter LP budgets could favor established brands over new small funds.

Stebbings describes losing money on Airlift in Pakistan and says his diligence failed to account for local context. Lemkin reflects on corners he cut during the boom and says he regrets them even where overall returns masked mistakes. Both offer personal accounts of investment errors rather than a full account of that company’s circumstances.

Asked about face-to-face meetings after the pandemic, Lemkin admits he has not solved the problem. Remote investing can work, as other successful investors have shown, but he feels he lost a useful way to develop trust. He cautions against treating an in-person meeting as a magical test of character; its value for him is relationship building.

Stebbings worries about newer creators and changing platforms. Lemkin says a strong venture brand helps founders find an investor, but maintaining it requires adapting as channels change. He still writes for Quora because, in his account, the remaining audience and occasional deal can justify the effort even after the platform’s relevance changed.

He believes brands can last if they are nurtured and that even one or two strong opportunities a year may matter for a venture fund. His examples of firms rebuilding their reputations are observations, and his expectations about future creators and deal flow are predictions, not certainties.

Stebbings asks whether success has dulled Lemkin’s drive. Lemkin says he still worries about LP returns but no longer feels the same pressure to prove himself as when he started. He regards that reduced hunger as a possible disadvantage, even though experience can improve judgment.

For 20VC, Lemkin suggests Stebbings may be underinvesting in a leadership bench. A mentor once told Lemkin that SaaStr needed separate leaders for media, events and funds; he says he did not build that team early enough. Stebbings agrees that finding sufficiently strong candidates is difficult.

In the quick-fire round, Lemkin says he did not perform a formal premortem on Algolia. He describes his early expectation that his investments could eventually reach billion-dollar outcomes, while noting that the market and the route to that valuation changed. His assertion that founders alone can kill a B2B startup is an opinion, not an exhaustive account of business failure.

He credits Alex Kayyal of Salesforce Ventures for supporting Algolia before and during a difficult financing round. Lemkin says he and Salesforce Ventures were early participants in that round; he speaks from the position of an existing investor and his praise reflects that relationship.

Looking ten years ahead, Lemkin says he cannot predict precisely how search will change. He values the continuing technical leadership of an Algolia co-founder and expects the company to benefit from its exposure to e-commerce and SaaS, but presents those as market views and future possibilities, not established outcomes.

Stebbings closes by thanking Lemkin for years of support and friendship. Their closing exchange reinforces the long personal relationship behind a conversation in which both speak as investors and operators with their own business interests.