Using Copilot’s ‘Think Deeper’ option

Microsoft Copilot Chat has introduced the ‘Think Deeper’ option (which has been available for free since March 2025). This is a huge to have OpenAI’s o1 model without needing a premium plan.

Using a recent news article, we used these two prompts consecutively:

summarise this page and analyse what it could mean for the digital and data market in new zealand based on the customer base and digital assets of trademe and stuff digital.

create a financial model outlining the financial breakeven point for this acquisition to provide a return.

Given the limited public information, the below results are based on significant assumptions but can be refined with additional information. Hopefully this shows how it could be applied to an internal problem with far more information available.

Below are the outputs from Copilot

Note: Not to be taken as facts or research but certainly shows the ease of use of the ‘Think Deeper’ model.

Summary of the TradeMe and Stuff Digital Acquisition

The article reports on a major strategic move in New Zealand’s digital landscape: Trade Me is taking a 50% stake in Stuff Digital, the digital arm of Stuff Group. Under the deal, Trade Me will gain a significant equity interest in the operation that runs high-traffic websites such as stuff.co.nz and ThreeNews. Notably, the agreement excludes Stuff Group’s traditional masthead publishing, events business, and certain other assets like Neighbourly. A key operational change is that the property section on Stuff Digital’s platform will be rebranded as Trade Me Property, with listings, advertisements, and selected content being shared between the two platforms. Both companies emphasize that while the partnership paves the way for enhanced growth opportunities and technological investments, editorial independence and ethical standards will remain paramount—with Stuff Digital retaining operational control via a board structure that ensures shared representation and a casting vote mechanism. This deal also marks an important milestone for Stuff, whose owner Sinead Boucher, having acquired the business for a nominal fee years ago, now sees a multi-million-dollar payday while bolstering future investments in technology and talent .

Analysis: Implications for the Digital and Data Market in New Zealand

1. Convergence of Digital Ecosystems and Enhanced Data Capabilities
This partnership represents a significant example of digital convergence in New Zealand. Trade Me, known for its expansive and highly engaged marketplace covering categories like property, motors, and jobs, brings an impressive customer base and deep transactional data. On the other hand, Stuff Digital dominates digital content and news consumption, offering extensive engagement metrics and rich consumer behavior insights. Combining these disparate yet complementary data sets could lead to powerful analytics capabilities, enabling more personalized content delivery and targeted advertising. Agencies and advertisers may gain unprecedented insight into consumer patterns, leading to innovations in tailored marketing strategies and data-driven service offerings .

2. Integrated Customer Experience and Cross-Market Synergies
For the digital market, the integration means that New Zealand consumers can expect a more seamless online experience. For example, the new Trade Me Property branding on Stuff Digital’s platform will likely enhance the discovery process for property buyers and sellers by leveraging Trade Me’s trusted marketplace interface. Such cross-pollination is expected to not only increase engagement on both platforms but also deliver more holistic digital experiences—from news to transactions—thereby reinforcing customer loyalty across New Zealand’s leading digital services .

3. Competitive Dynamics and Market Innovation
The deal signals an intensifying competitive landscape in New Zealand’s digital and data market. As two well-established Kiwi brands merge their digital assets, rivals in both the classifieds and media sectors may feel compelled to rethink their strategies. The fusion of expansive user data and a broad digital reach sets a new precedent for innovation, where data intelligence drives everything from content curation to marketplace recommendations. However, this also means that competitors will need to invest heavily in technology and consumer insights to keep pace, potentially spurring an overall boost in sector-wide innovation .

4. Data Privacy, Security, and Regulatory Considerations
With the merging of diverse datasets—from transaction records on Trade Me to content consumption metrics on Stuff Digital—there is enormous potential for deriving detailed consumer insights. However, this also elevates the stakes in terms of data privacy and security. Greater data integration can attract regulatory attention, meaning both companies must continue to prioritize user data protection, transparent policies, and robust ethical safeguards. Ensuring consumer trust in an era of heightened data sensitivity will be critical for sustaining long-term growth and market confidence .

5. Broader Digital Transformation in New Zealand
Finally, this partnership is emblematic of New Zealand’s broader digital transformation. As large-scale digital assets are consolidated and leveraged for more effective consumer engagement and data analytics, the move could set the stage for further investments in tech innovation across the country. The combined force of a popular marketplace and a trusted digital news source may well become a model for future collaborations, not only within New Zealand but also as an example for similar mid-sized markets globally .

Overall, the move by Trade Me to acquire a 50% stake in Stuff Digital heralds a new chapter in New Zealand’s digital ecosystem. It underscores the growing importance of integrating commerce and content, the value of robust data assets, and the need for strategic innovation in delivering consumer experiences. As this merger evolves, one can expect ripple effects across advertising, customer engagement, and data analytics—transforming the way digital businesses operate in New Zealand and beyond.

Below is a conceptual financial model that outlines how Trade Me’s 50% acquisition in Stuff Digital might reach a breakeven on its investment. Keep in mind that actual figures would need to be sourced from internal company data and market research; the numbers below are hypothetical yet illustrative of the underlying dynamics.

Financial Model – Key Assumptions

  1. Acquisition Cost: For this model, we assume Trade Me pays NZD 50 million for a 50% stake. (This implies an overall enterprise value for Stuff Digital of NZD 100 million, though our focus here is on Trade Me’s outlay.)
  2. Initial Revenue & Margins:
    • Year 0 (pre-integration) annual revenue from the Stuff Digital platform is assumed at NZD 25 million.
    • The standalone operating margin is modest at 10%, resulting in an operating profit of NZD 2.5 million.
  3. Growth and Synergy Effects:
    • Revenue Growth: Integration synergies (e.g., cross-promotional opportunities between Trade Me’s marketplace and Stuff Digital’s content channels) spur an enhanced annual revenue growth rate of about 10% per year (a blend of organic growth plus synergy lift).
    • Margin Improvement: With cost efficiencies in operations and improved digital capabilities, operating margins are expected to rise to approximately 15% from Year 1 onward.
  4. Timeline: We will track annual operating profit accumulation over a 10-year period until cumulative profit equals the NZD 50 million investment. (Note that discounting for the time value of money is not shown here but can be incorporated through a discounted cash flow approach once more detailed projections are available.)

Simplified Financial Model

The following table illustrates a year-by-year projection under the above assumptions:

YearRevenue (NZD million)Operating MarginAnnual Operating Profit (NZD million)Cumulative Operating Profit (NZD million)
025.010%2.502.50
125.0 × 1.10 = 27.515%27.5 × 0.15 = 4.132.50 + 4.13 = 6.63
227.5 × 1.10 ≈ 30.2515%30.25 × 0.15 ≈ 4.546.63 + 4.54 ≈ 11.17
330.25 × 1.10 ≈ 33.2815%33.28 × 0.15 ≈ 5.0011.17 + 5.00 ≈ 16.17
433.28 × 1.10 ≈ 36.6115%36.61 × 0.15 ≈ 5.4916.17 + 5.49 ≈ 21.66
536.61 × 1.10 ≈ 40.2715%40.27 × 0.15 ≈ 6.0421.66 + 6.04 ≈ 27.70
640.27 × 1.10 ≈ 44.3015%44.30 × 0.15 ≈ 6.6427.70 + 6.64 ≈ 34.34
744.30 × 1.10 ≈ 48.7315%48.73 × 0.15 ≈ 7.3134.34 + 7.31 ≈ 41.65
848.73 × 1.10 ≈ 53.6015%53.60 × 0.15 ≈ 8.0441.65 + 8.04 ≈ 49.69
953.60 × 1.10 ≈ 58.9615%58.96 × 0.15 ≈ 8.8449.69 + 8.84 ≈ 58.53
1058.96 × 1.10 ≈ 64.8515%64.85 × 0.15 ≈ 9.7358.53 + 9.73 ≈ 68.26

Interpretation: Up to Year 0 (pre-integration), the platform’s performance contributes a modest NZD 2.5 million in operating profit. From Year 1, as the enhanced model kicks in (with synergies and margin improvements), the cumulative operating profit grows steadily.

According to the table, the cumulative operating profit crosses the NZD 50 million threshold between Year 8 and Year 9. This suggests that, under these assumptions, the breakeven point (where the cumulative operating profit equals the acquisition cost of NZD 50 million) is reached in roughly 8–9 years.

Note that incorporating the time value of money via discounting could extend the break-even timeline, and additional factors (like integration costs, tax implications, capital expenditures, and unforeseen market shifts) may also impact the actual period required.

What This Means in the Broader Context

  • Revenue and Data Synergies: With Trade Me’s extensive customer base and robust marketplace data combined with Stuff Digital’s rich digital content platform, the model assumes that the companies can drive an accelerated revenue growth rate. This integration deepens customer engagement and opens up new advertising and cross-selling opportunities.
  • Cost Efficiencies: Margin improvements reflect both cost-saving synergies (e.g., shared technology and infrastructure investments) and better monetization of digital assets through targeted advertising and premium content offerings.
  • Market Leadership: Achieving the breakeven point within 8–9 years would not only validate the strategic investment but also create a financially stronger, more competitive presence in New Zealand’s digital landscape. This would likely spur further innovation and could set a benchmark for similar consolidations in mid-sized markets.
  • Risk Considerations: Realizing these synergies depends on successful integration, continual innovation, and maintaining strong user trust and data privacy standards. The assumptions in growth and margin improvement are key levers in the model; deviations here could shift the break-even timeline.

Divergent Insights

While this simplified model provides a framework, further analysis could delve into:

  • Sensitivity Analysis: Quantify how changes in revenue growth, margin improvement, or capital costs affect the break-even timeline.
  • Discounted Cash Flow (DCF): Apply an appropriate discount rate (e.g., 8–10% given market conditions) to assess the present value of future operating profits.
  • Scenario Modeling: Consider high‐growth vs. conservative scenarios to understand upside potential or downside risks in the digital and data market.
  • Integration Costs & Capital Expenditures: Factor in any additional investments needed for technology, talent, or integration activities which could delay breakeven.

Video creation tools

With Google extending their Veo option, it’s always good to revisit the key options that are available.

Here’s a high-level look at each AI-powered video and audio generation tool from major tech companies, including links to explore them:

1. OpenAI – Sora

Link: Explore Sora

  • Cost: Available through ChatGPT Plus and Pro subscriptions, starting at around US$20 per month.
  • Quality: Generates high-quality, realistic videos up to 60 seconds long with complex scenes and camera movements.
  • Safety: OpenAI has implemented safeguards to prevent misuse, including red teaming and watermarking AI-generated content.

2. Adobe – Firefly

Link: Try Adobe Firefly

  • Cost: Subscription-based model integrated into Adobe Creative Cloud.
  • Quality: Offers professional-grade AI-generated videos, animations, and visual effects with seamless integration into Adobe Premiere Pro and After Effects.
  • Safety: Firefly is trained on Adobe Stock and public domain content, ensuring commercially safe outputs.

3. Google – Veo 3

Link: Use Veo 3

  • Cost: Available through Google AI Ultra subscription ($249.99/month) and Google AI Pro for limited access (8 second video).
  • Quality: Generates high-resolution videos with integrated audio, including dialogue and ambient sounds.
  • Safety: Google emphasizes AI ethics, prompt adherence, and safeguards against misuse.

4. Meta – MovieGen

Link: Learn about MovieGen

  • Cost: Currently in research phase, not yet commercially available.
  • Quality: Capable of generating 1080p HD videos with synchronized audio, personalized video creation, and precise editing.
  • Safety: Meta is developing safeguards to prevent misuse and ensure ethical AI deployment.

5. RunwayML

Link: Try RunwayML

  • Cost: Freemium model with paid tiers for advanced features.
  • Quality: Offers high-resolution AI-generated videos with advanced editing tools, including lip-syncing and generative audio.
  • Safety: Implements AI ethics policies to prevent harmful content.

Each platform has unique strengths, whether it’s affordability, professional quality, or ethical safeguards. The evolution in this space is rapid but does not yet replace the need for high quality media content.

Pitfalls

Here’s an example of a generated video that initially appears what I asked for. After a few seconds, the man on the left appears to sneeze and disappears (quite the magic trick!).

Prompt: Create a hot steaming cup of coffee with a colour changing data logo on the outside with a blurred background of an office with robots and humans sitting at desk wearing headsets.

Model: Google Veo 2

Understanding AI and ML in Modern Enterprise Solutions

In today’s rapidly evolving technology landscape, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into enterprise applications has sparked both excitement and skepticism. While some argue that trusting AI/ML to handle critical tasks indicates a lack of concern for the job’s quality, others see it as a revolutionary step towards efficiency and innovation. Let’s delve into this debate and explore the nuanced roles of AI and ML in enterprise applications.

The Purpose of Enterprise Applications

Enterprise applications are designed to codify workflows and processes, ensuring consistency and reliability across various operations. These applications rely on well-defined algorithms to perform tasks that require precision and predictability. For instance, determining whether it’s freezing outside can be easily handled by mathematical formulas and sensors.However, there are scenarios where traditional algorithms fall short. This is where ML comes into play. ML is specifically designed to tackle problems that are difficult or impossible to solve with conventional algorithms. Examples include identifying spam emails, detecting nudity in images, or recognising pedestrians in real-time video feeds. These tasks require a level of adaptability and learning that static algorithms cannot provide.

The Misconceptions about AI and ML

A common misconception is that AI and ML are meant to replace well-codified processes. This is not the case. AI, particularly in its current state, is intended to augment existing algorithms, handling edge cases and providing insights where traditional methods fail. For example, while conventional code is deterministic and reliable, ML models are probabilistic and can adapt to new data patterns, making them suitable for dynamic and complex environments.Critics often label AI initiatives, such as those by OpenAI, as overhyped. They argue that these technologies are not designed to codify processes but to enhance them. It’s crucial to understand that AI and ML are tools that complement traditional coding, not substitutes for it. Conventional code remains superior for tasks that require deterministic outcomes, while ML excels in areas where adaptability and learning from data are essential.

The Challenges of Implementing AI and ML

Implementing AI and ML in enterprise applications is not without challenges. One significant hurdle is ensuring that the AI-generated solutions work reliably. Writing code that functions correctly, scales efficiently, and remains maintainable over time is a complex task. It requires rigorous testing and validation, especially when financial stakes are high.Moreover, maintaining AI-driven systems poses its own set of challenges. Business processes are rarely static; they evolve over time, necessitating continuous updates and modifications. While generating initial code might be straightforward, making precise changes without introducing errors is a daunting task. This is where the expertise of developers, analysts, and quality assurance professionals becomes indispensable.

Compute costs, Environmental and Economic Impact

Another critical aspect to consider is the cost, environmental and economic impact of AI and ML. Training and deploying ML models require substantial computational resources, leading to significant energy consumption and ecological concerns. Additionally, the costs associated with developing and maintaining AI-driven systems can be high, making it essential to weigh up the cost benefit.

AI’s Role in Creating ML Models

Interestingly, AI itself can be leveraged to create ML models. Automated Machine Learning (AutoML) platforms enable users to build ML models without extensive coding knowledge. These platforms use AI to automate the process of selecting algorithms, tuning hyperparameters, and validating models. This democratizes access to ML, allowing businesses to harness its power without requiring deep technical expertise.

Wrap up

AI and ML are powerful tools that, when used appropriately, can significantly enhance enterprise applications. They are not meant to replace traditional coding but to augment it, handling tasks that require adaptability and learning from data. While there are challenges and misconceptions surrounding their implementation, the potential benefits make them invaluable assets in the modern technological landscape. As we continue to innovate, the synergy between AI, ML, and conventional coding will drive the next wave of enterprise solutions.—What are your thoughts on the role of AI and ML in enterprise applications? Do you see them as complementary tools or potential replacements for traditional methods?