The executives leading the product and engineering efforts for OpenAI’s developer platform say companies must adopt a result-oriented approach to successfully roll out AI to employees.

Olivier Godement and Sherwin Wu head product and engineering for OpenAI’s developer platform, respectively. During an interview on the BG2 podcast that aired Thursday, Godement and Wu shared three tips on how companies can integrate AI.

“Number one is the interesting combination of top-down buy-in and enabling a very clear group, like a tiger team,” Godement said. He added that the team could be a mix of staff from AI providers like OpenAI, as well as the company’s own employees.

Godement said members of the “tiger team” should possess either technical skills or a deep understanding of the company’s processes.

“In the enterprise, like customer support, what we found is that the vast majority of the knowledge is in people’s heads,” Godement said.

“Unless you have that tiger team, a mix of technical and subject matter experts, it’s really hard to get something out of the ground,” he added.

Next, Godement and Wu said companies need to develop clear benchmarks, or what they call “evals,” to track their progress with AI.

“Evals are much harder than what it looks to get done,” Godement said.

“Evals, oftentimes, need to come bottom-up, because all of these things are kind of in people’s heads, in the actual operator’s heads. It’s actually very hard to have a top-down mandate,” Wu said.

Lastly, Godement said that companies should monitor their benchmarks closely and strive to make progress against them.

“A lot of that is like art sometimes, more than science,” he said.

Progress can be achieved by having a good understanding of an AI model’s design, behavior, and constraints, Godement said.

“Sometimes, we even need to fine-tune ourselves the models, when there are some clear limitations, and you know, being patient, getting you way up there and then, ship,” he added.

Godement said it was important for a company’s top leadership to make AI a priority and give their staff the opportunity to experiment.

“Letting the team organize and be like, ‘OK, if you want to start small, start small, and then you can scale it up.’ That would be number 1,” he added.

OpenAI did not respond to a request for comment from Business Insider.

Tech CEOs have been stepping up their efforts when it comes to getting their employees to use AI.

Duolingo CEO Luis von Ahn said in an August interview with The New York Times that Duolingo has been organizing weekly activities to encourage teams to use AI.

“Every Friday morning, we have this thing: It’s a bad acronym, f-r-A-I-days,” von Ahn told the Times.

Howie Liu, the CEO of the vibe coding platform Airtable, said in an episode of Lenny’s Podcats that aired last month that he wants his staff to experiment with AI, even if it entails taking time off work.

“If you want to cancel all your meetings for a day or for an entire week and just go play around with every AI product that you think could be relevant to Airtable, go do it. Period,” Liu said.

Ernst & Young LLP (EY) has launched EY Growth Platforms (EYGP), an artificial intelligence enabled solution powered by neurosymbolic AI.

Announced recently, this update from the EY-Parthenon practice aims to enable businesses to identify untapped opportunities, predict market shifts, and unlock revenue at scale.

As economic uncertainties persist, EYGP positions itself as a digital tool for professionals seeking to redefine commercial models and drive sustainable profitability.

At the heart of EYGP is neurosymbolic AI, a hybrid approach that merges the probabilistic pattern recognition of neural networks with the precise, rule-based logic of symbolic reasoning.

Unlike traditional generative AI, which often excels at content creation but falls short on explainable decisions, neurosymbolic AI delivers actionable insights that are said to be grounded in real-world logic.

This combination allows for predictions that are not only accurate but also transparent and traceable, addressing a need in enterprise decision-making.

Jeff Schumacher said:

“Neurosymbolic AI is not another analytics tool; it’s a growth engine.”

He is the newly appointed EYGP Leader for EY-Parthenon.

Schumacher, who founded Growth Protocol—the proprietary technology EY has exclusively licensed—brings a wealth of expertise in business strategy and innovation to spearhead this initiative.

EYGP functions as a unified data and reasoning engine, ingesting vast amounts of structured and unstructured data from internal systems, external market signals, and EY’s extensive proprietary datasets.

Developed over several years, the platform simulates real-time market scenarios to generate tailored business strategies without the hassle of extensive data cleaning or digital overhauls.

It processes information through intelligent workflows that blend statistical analysis with logical inference, uncovering patterns that traditional methods might miss.

This enables companies to pivot quickly, optimizing everything from go-to-market strategies to high-stakes transactions.

The benefits for businesses are seemingly significant.

In an era where growth demands agility, EYGP helps organizations reimagine their revenue trajectories by identifying hundred-million-dollar opportunities and scaling new ventures.

It tackles complex challenges like building corporate innovation labs or executing mergers with predictive foresight, all while ensuring decisions are statistically sound and compliant.

Mitch Berlin, EY Americas Vice Chair for EY-Parthenon, emphasized this potential:

“In today’s uncertain economic climate, leading companies aren’t just adapting—they’re taking control. EY Growth Platforms gives our clients the predictive power and actionable foresight they need to confidently steer their revenue trajectory.

This is potentially a game changer, poised to become the backbone of enterprise growth.

”Real-world applications span diverse industries. In financial services, EYGP enhances underwriting and claims processing with transparent AI that aligns with regulatory standards, optimizing customer outcomes while minimizing risks.

For consumer products companies, it powers hyperpersonalized experiences—think real-time product recommendations, adaptive user interfaces, and location-based services—by analyzing individual behaviors and preferences in context.

In the industrial sector, the platform optimizes supply chains from sourcing to distribution, integrating domain knowledge with operational data to inform decisions on facility placement, logistics routing, and workforce allocation tailored to specific markets.

Deployed for EY-Parthenon clients in consumer goods, industrials, and financial services, EYGP is available in North America, Europe, and Australia.

This launch underscores EY‘s commitment to blending human expertise with AI, fostering trust in an increasingly automated environment (where it has become increasingly difficult to reliably distinguish between AI powered activity and the finer touches of human interaction).

And as businesses grapple with volatility, tools like EYGP could mark a pivotal shift, turning data into dollars and uncertainty into potential opportunities.

AI is driving extraordinary market concentration and raising tough questions for everyday investors, write Australian academics Angel Zhong and Jason Tian.

Just for a moment this week, Larry Ellison, co-founder of US cloud computing company Oracle, became the world’s richest person. The octogenarian tech titan briefly overtook Elon Musk after Oracle’s share price rocketed 43% in a day, adding about US$100 billion (NZ$167 billion) to his wealth.

The reason? Oracle inked a deal to provide artificial intelligence (AI) giant OpenAI with US$300 billion (NZ$503 billion) in computing power over five years.

While Ellison’s moment in the spotlight was fleeting, it also illuminated something far more significant: AI has created extraordinary levels of concentration in global financial markets.

This raises an uncomfortable question not only for seasoned investors – but also for everyday Australians who hold shares in AI companies via their superannuation. Just how exposed are even our supposedly “safe”, “diversified” investments to the AI boom?

The man who built the internet’s memory

As billionaires go, Ellison isn’t as much of a household name as Tesla and SpaceX’s Musk or Amazon’s Jeff Bezos. But he’s been building wealth from enterprise technology for nearly five decades.

Ellison co-founded Oracle in 1977, transforming it into one of the world’s largest database software companies. For decades, Oracle provided the unglamorous but essential plumbing that kept many corporate systems running.

The AI revolution changed everything. Oracle’s cloud computing infrastructure, which helps companies store and process vast amounts of data, became critical infrastructure for the AI boom.

Every time a company wants to train large language models or run machine learning algorithms, they need huge amounts of computing power and data storage. That’s precisely where Oracle excels.

When Oracle reported stronger-than-expected quarterly earnings this week, driven largely by soaring AI demand, its share price spiked.

That response wasn’t just about Oracle’s business fundamentals. It was about the entire AI ecosystem that has been reshaping global markets since ChatGPT’s public debut in late 2022.

The great AI concentration

Oracle’s story is part of a much larger phenomenon reshaping global markets. The so-called “Magnificent Seven” tech stocks – Apple, Microsoft, Alphabet, Amazon, Meta, Tesla and Nvidia – now control an unprecedented share of major stock indices.

Year-to-date in 2025, these seven companies have come to represent approximately 39% of the US S&P500’s total value. For the tech-heavy NASDAQ100, the figure is a whopping 74%.

This means if you invest in an exchange-traded fund that tracks the S&P500 index, often considered the gold standard of diversified investing, you’re making an increasingly concentrated bet on AI, whether you realise it or not.

Are we in an AI ‘bubble’?

This level of concentration has not been seen since the late 1990s. Back then, investors were swept up in “dot-com mania”, driving technology stock prices to unsustainable levels.

When reality finally hit in March 2000, the tech-heavy Nasdaq crashed 77% over two years, wiping out trillions in wealth.

Today’s AI concentration raises some similar red flags. Nvidia, which controls an estimated 90% of the AI chip market, currently trades at more than 30 times expected earnings. This is expensive for any stock, let alone one carrying the hopes of an entire technological revolution.

Yet, unlike the dot-com era, today’s AI leaders are profitable companies with real revenue streams. Microsoft, Apple and Google aren’t cash-burning startups. They are established giants, using AI to enhance existing businesses while generating substantial profits.

This makes the current situation more complicated than a simple “bubble” comparison. The academic literature on market bubbles suggests genuine technological innovation often coincides with speculative excess.

The question isn’t whether AI is transformative; it clearly is. Rather, the question is whether current valuations reflect realistic expectations about future profitability.

Hidden exposure for many

For Australians, the AI concentration problem hits remarkably close to home through our superannuation system.

Many balanced super fund options include substantial allocations to international shares, typically 20–30% of their portfolios.

When your super fund buys international shares, it’s often getting heavy exposure to those same AI giants dominating US markets.

The concentration risk extends beyond direct investments in tech companies. Australian mining companies, such as BHP and Fortescue, have become indirect AI players because their copper, lithium and rare earth minerals are essential for AI infrastructure.

Even diversifying away from technology doesn’t fully escape AI-related risks. Research on portfolio concentration shows when major indices become dominated by a few large stocks, the benefits of diversification diminish significantly.

If AI stocks experience a significant correction or crash, it could disproportionately impact Australians’ retirement nest eggs.

A reality check

This situation represents what’s called “systemic concentration risk”. This is a specific form of systemic risk where supposedly diversified investments become correlated through common underlying factors or exposures.

It’s reminiscent of the 2008 financial crisis, when seemingly separate housing markets across different regions all collapsed simultaneously. That was because they were all exposed to subprime mortgages with high risk of default.

This does not mean anyone should panic. But regulators, super fund trustees and individual investors should all be aware of these risks. Diversification only works if returns come from a broad range of companies and industries.

Angel Zhong is a Professor of Finance at RMIT University in Melbourne; Jason Tian is a Senior Lecturer at Swinburne University of Technology in Melbourne

This article was republished from The Conversation under a Creative Commons licence.