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Sep 23, 2018 · 26m · top-founders

1156 YC Alum Burning $110k/mo in Cash To Scope Projects and Place Dev Talent Using Machine Learning

Iba Masood · 14m spoken Nathan Latka · 9m spoken
0:00 / 0:00

gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

In this episode of The Top Entrepreneurs podcast, host Nathan Latka interviews Iba Masood, co-founder and CEO of Tara Intelligence, exploring how the Y Combinator-backed startup leverages neural networks to automate software project scoping and deploy developer talent across 65 enterprise clients while generating $80,000 in monthly recurring revenue.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Nathan holds 39.5% of the talking time here. How this is scored →

Nathan as informed peer 5.1 Guest teaching 3.4 Guest disagreement 2.1 Nathan pushing back 3.9
05100:0010:0020:002:00–5:29 · Nathan as informed peer 5/10 Tara's AI Scoping Platform and Neural Network Training Latka demonstrates deep familiarity with the practical pains of product management and sprint scoping. Masood educates him on how Tara scraped open-source repos to build baseline neural nets.5:30–7:35 · Nathan as informed peer 5/10 Monetization Model and Developer Incentives Latka compares Tara's model to Toptal, prompting Masood to explain how marketplace developers are underpaid on micro-tasks and how Tara aggregates larger enterprise workloads.7:36–10:37 · Nathan as informed peer 4/10 Company Pivot, Enterprise Adoption, and Revenue Breakdown Latka probes into the classification of enterprise versus mid-market customers and the 60/40 talent-to-software revenue split.10:40–16:27 · Nathan as informed peer 6/10 Sponsor Message: SEMrush Competitive Intelligence Latka assumes their run rate is over $650k/mo based on customer counts, but Masood flatly denies this and explains that the $120k ACV tier only rolled out recently, bringing current MRR down to $80k.16:30–20:14 · Nathan as informed peer 6/10 Long-Term Platform Vision and Market Positioning Latka challenges Tara's strategy of competing on both software and talent placement, questioning why incumbent marketplaces would cooperate. Masood defends the dual model by highlighting automated scoping advantages for developers.20:14–23:59 · Nathan as informed peer 6/10 Fundraising, Unit Economics, and Enterprise Focus Latka presses Masood on why they have not sold to mature YC alumni companies like Weebly, but Masood redefines enterprise as publicly traded corporations with thousands of engineers.24:01–25:21 · Nathan as informed peer 4/10 The Famous Five Rapid-Fire Questions Latka runs through the rapid-fire Famous Five, adding background color on BlackLine founder Therese Tucker.2:00–5:29 · Guest teaching 3/10 Tara's AI Scoping Platform and Neural Network Training Latka demonstrates deep familiarity with the practical pains of product management and sprint scoping. Masood educates him on how Tara scraped open-source repos to build baseline neural nets.5:30–7:35 · Guest teaching 3/10 Monetization Model and Developer Incentives Latka compares Tara's model to Toptal, prompting Masood to explain how marketplace developers are underpaid on micro-tasks and how Tara aggregates larger enterprise workloads.7:36–10:37 · Guest teaching 2/10 Company Pivot, Enterprise Adoption, and Revenue Breakdown Latka probes into the classification of enterprise versus mid-market customers and the 60/40 talent-to-software revenue split.10:40–16:27 · Guest teaching 6/10 Sponsor Message: SEMrush Competitive Intelligence Latka assumes their run rate is over $650k/mo based on customer counts, but Masood flatly denies this and explains that the $120k ACV tier only rolled out recently, bringing current MRR down to $80k.16:30–20:14 · Guest teaching 4/10 Long-Term Platform Vision and Market Positioning Latka challenges Tara's strategy of competing on both software and talent placement, questioning why incumbent marketplaces would cooperate. Masood defends the dual model by highlighting automated scoping advantages for developers.20:14–23:59 · Guest teaching 5/10 Fundraising, Unit Economics, and Enterprise Focus Latka presses Masood on why they have not sold to mature YC alumni companies like Weebly, but Masood redefines enterprise as publicly traded corporations with thousands of engineers.24:01–25:21 · Guest teaching 1/10 The Famous Five Rapid-Fire Questions Latka runs through the rapid-fire Famous Five, adding background color on BlackLine founder Therese Tucker.2:00–5:29 · Guest disagreement 1/10 Tara's AI Scoping Platform and Neural Network Training Latka demonstrates deep familiarity with the practical pains of product management and sprint scoping. Masood educates him on how Tara scraped open-source repos to build baseline neural nets.5:30–7:35 · Guest disagreement 1/10 Monetization Model and Developer Incentives Latka compares Tara's model to Toptal, prompting Masood to explain how marketplace developers are underpaid on micro-tasks and how Tara aggregates larger enterprise workloads.7:36–10:37 · Guest disagreement 1/10 Company Pivot, Enterprise Adoption, and Revenue Breakdown Latka probes into the classification of enterprise versus mid-market customers and the 60/40 talent-to-software revenue split.10:40–16:27 · Guest disagreement 3/10 Sponsor Message: SEMrush Competitive Intelligence Latka assumes their run rate is over $650k/mo based on customer counts, but Masood flatly denies this and explains that the $120k ACV tier only rolled out recently, bringing current MRR down to $80k.16:30–20:14 · Guest disagreement 4/10 Long-Term Platform Vision and Market Positioning Latka challenges Tara's strategy of competing on both software and talent placement, questioning why incumbent marketplaces would cooperate. Masood defends the dual model by highlighting automated scoping advantages for developers.20:14–23:59 · Guest disagreement 4/10 Fundraising, Unit Economics, and Enterprise Focus Latka presses Masood on why they have not sold to mature YC alumni companies like Weebly, but Masood redefines enterprise as publicly traded corporations with thousands of engineers.24:01–25:21 · Guest disagreement 1/10 The Famous Five Rapid-Fire Questions Latka runs through the rapid-fire Famous Five, adding background color on BlackLine founder Therese Tucker.2:00–5:29 · Nathan pushing back 2/10 Tara's AI Scoping Platform and Neural Network Training Latka demonstrates deep familiarity with the practical pains of product management and sprint scoping. Masood educates him on how Tara scraped open-source repos to build baseline neural nets.5:30–7:35 · Nathan pushing back 3/10 Monetization Model and Developer Incentives Latka compares Tara's model to Toptal, prompting Masood to explain how marketplace developers are underpaid on micro-tasks and how Tara aggregates larger enterprise workloads.7:36–10:37 · Nathan pushing back 3/10 Company Pivot, Enterprise Adoption, and Revenue Breakdown Latka probes into the classification of enterprise versus mid-market customers and the 60/40 talent-to-software revenue split.10:40–16:27 · Nathan pushing back 6/10 Sponsor Message: SEMrush Competitive Intelligence Latka assumes their run rate is over $650k/mo based on customer counts, but Masood flatly denies this and explains that the $120k ACV tier only rolled out recently, bringing current MRR down to $80k.16:30–20:14 · Nathan pushing back 6/10 Long-Term Platform Vision and Market Positioning Latka challenges Tara's strategy of competing on both software and talent placement, questioning why incumbent marketplaces would cooperate. Masood defends the dual model by highlighting automated scoping advantages for developers.20:14–23:59 · Nathan pushing back 6/10 Fundraising, Unit Economics, and Enterprise Focus Latka presses Masood on why they have not sold to mature YC alumni companies like Weebly, but Masood redefines enterprise as publicly traded corporations with thousands of engineers.24:01–25:21 · Nathan pushing back 1/10 The Famous Five Rapid-Fire Questions Latka runs through the rapid-fire Famous Five, adding background color on BlackLine founder Therese Tucker.

speaking balance: gold is Nathan, purple is the guest (3 minute bins)

0:00 · Nathan 70.6% · guest 29.4%0:00 · Nathan 70.6% · guest 29.4%3:00 · Nathan 17.2% · guest 82.8%3:00 · Nathan 17.2% · guest 82.8%6:00 · Nathan 24% · guest 76%6:00 · Nathan 24% · guest 76%9:00 · Nathan 58% · guest 42%9:00 · Nathan 58% · guest 42%12:00 · Nathan 33.9% · guest 66.1%12:00 · Nathan 33.9% · guest 66.1%15:00 · Nathan 34.9% · guest 65.1%15:00 · Nathan 34.9% · guest 65.1%18:00 · Nathan 20.9% · guest 79.1%18:00 · Nathan 20.9% · guest 79.1%21:00 · Nathan 31.4% · guest 68.6%21:00 · Nathan 31.4% · guest 68.6%24:00 · Nathan 80% · guest 20%24:00 · Nathan 80% · guest 20%
Sharpest disagreement ▶ 21:58 Masood rejects Latka's definition of enterprise

Masood rejects Latka's suggestion that they should be pitching large YC alumni like Weebly, clarifying that true enterprise means publicly traded companies with thousands of developers.

 Hardest push from Nathan ▶ 18:57 Latka challenges the dual-marketplace strategy

Latka directly confronts Masood on why giant competitors like Toptal would ever grant access or allow Tara to capture value across both talent placement and software.

 Biggest teaching moment ▶ 12:25 Masood corrects Latka's $650k MRR calculation

When Latka calculates that 65 customers at $120k ACV equals $650k per month, Masood immediately shuts it down, explaining historical pricing was only $40k to $50k.

 Nathan holds their own ▶ 15:25 Latka forces an accurate MRR baseline

Latka recalculates their revenue on the fly after identifying inconsistencies, pressing Masood until she reveals their actual current MRR is $80k.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Tara's AI Scoping Platform and Neural Network Training 5312 Latka demonstrates deep familiarity with the practical pains of product management and sprint scoping. Masood educates him on how Tara scraped open-source repos to build baseline neural nets.
Monetization Model and Developer Incentives 5313 Latka compares Tara's model to Toptal, prompting Masood to explain how marketplace developers are underpaid on micro-tasks and how Tara aggregates larger enterprise workloads.
Company Pivot, Enterprise Adoption, and Revenue Breakdown 4213 Latka probes into the classification of enterprise versus mid-market customers and the 60/40 talent-to-software revenue split.
Sponsor Message: SEMrush Competitive Intelligence 6636 Latka assumes their run rate is over $650k/mo based on customer counts, but Masood flatly denies this and explains that the $120k ACV tier only rolled out recently, bringing current MRR down to $80k.
Long-Term Platform Vision and Market Positioning 6446 Latka challenges Tara's strategy of competing on both software and talent placement, questioning why incumbent marketplaces would cooperate. Masood defends the dual model by highlighting automated scoping advantages for developers.
Fundraising, Unit Economics, and Enterprise Focus 6546 Latka presses Masood on why they have not sold to mature YC alumni companies like Weebly, but Masood redefines enterprise as publicly traded corporations with thousands of engineers.
The Famous Five Rapid-Fire Questions 4111 Latka runs through the rapid-fire Famous Five, adding background color on BlackLine founder Therese Tucker.

Statements from this episode (16)

Assertion Not checkable as stated
Masood: Tara trained early AI models on roughly 5,000 software projects
“So we used roughly about our early data set included about 5000 software projects, and we use that to train our system to understand that, okay, with an iOS app, if the iOS app requires a two sided marketplace, here are the typical milestones, tasks, and this …”
Iba Masood Sep 23, 2018 ▶ 3:43
Assertion Not checkable as stated
Masood: Product managers spend roughly 70% of their time on project scoping
“We had about 10 to 15 contractors early on that were actually, you know, running the scoping process. And we were comparing results between what the AI could do versus what human product managers were mapping out. And what we found was that there was about 70%…”
Iba Masood Sep 23, 2018 ▶ 4:16
Insight
Masood: Open source projects are among the most efficiently run software projects
“We, as a company, we have this hypothesis that open source projects are one of the most efficiently run projects as a whole, because Typically when people are pursuing side projects, they have a timeline and they need to get things done quickly, but they like …”
Iba Masood Sep 23, 2018 ▶ 4:50
Assertion Not checkable as stated
Masood: Tara charges enterprise clients $120k annually plus marketplace fees
“So we're looking at about a 120 K annual. And then on top of that, there's also marketplace charges.”
Iba Masood Sep 23, 2018 ▶ 5:52
Assertion Not checkable as stated
Masood: Freelance developers on platforms like Upwork averaged $200 per widget project
“We found that the average developer was being paid about 200 dollars on the leading freelance marketplaces like Upwork. And they had to 200 dollars for a small widget project. Okay. And so they would need to take at least 10 to 18 of those projects in a month …”
Iba Masood Sep 23, 2018 ▶ 6:42
Insight
Masood: Companies accept AI hiring for freelance roles, not full-time
“Plus we found that companies were more willing to allow AI to make the recruiting decision when it was primarily for freelance positions. So even with full time, they were still not willing to like Allow an algorithm to recruit developers specifically that wer…”
Iba Masood Sep 23, 2018 ▶ 7:55
Assertion Not checkable as stated
Masood: Tara has about 10 enterprise and 65 total customers
“So enterprises, we have about 10. A total companies, we have about 65.”
Iba Masood Sep 23, 2018 ▶ 8:20
Assertion Not checkable as stated
Masood: Tara generates 60% of revenue from talent placement, 40% from software
“So about 60% of our revenue comes from placing the talent, and about 40% is coming from the software.”
Iba Masood Sep 23, 2018 ▶ 9:49
Disclosure
Masood: Tara shifted contract pricing from $40K-$50K to $120K
“So we started pitching our 120 K annual contract model last month. before that, a lot of our customers were on 40 K to 50 K.”
Iba Masood Sep 23, 2018 ▶ 12:51
Assertion Not checkable as stated
Masood: Tara Intelligence is growing at about 140% year-over-year
“Right now it's about a 140%.”
Iba Masood Sep 23, 2018 ▶ 15:08
Prediction Not checkable as stated
Masood: Tara will grow from $80K to $140K MRR within two months
“And so from, but we're actually going to go from 80 K to about a 140 K in the next two months because of the enterprise deals that we're almost about to close.”
Iba Masood Sep 23, 2018 ▶ 15:54
Assertion Not checkable as stated
Masood: Freelance developers regularly cross-list across Toptal and Tara
“We have TopTile developers on our platform too. It's not like every developer just joins one platform. They usually join multiple.”
Iba Masood Sep 23, 2018 ▶ 19:15
Insight
Masood: Building a machine learning platform takes two to three years
“We started developing the product in 2015 because it takes two to three years just to build out an ML platform.”
Iba Masood Sep 23, 2018 ▶ 20:37
Disclosure
Masood: Tara Intelligence has roughly 8% monthly logo churn
“We're at about eight percent churn.”
Iba Masood Sep 23, 2018 ▶ 22:46
Disclosure
Masood: Tara Intelligence burns around $110,000 per month
“So well, our burn as of today is at about a 110 K.”
Iba Masood Sep 23, 2018 ▶ 23:07
Disclosure
Masood: Tara Intelligence spends roughly $20k to acquire a $120k customer
“So for example, if one customer closes at about a 120, a 120 K, then we've probably spent roughly about 20,000 dollars to acquire them.”
Iba Masood Sep 23, 2018 ▶ 23:21
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